CL for Olympiad

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Published on February 4, 2008

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Computational Linguistics:  Computational Linguistics James Pustejovsky Brandeis University Boston Computational Linguistics Olympiad Team Fall, 2007 What is Computational Linguistics?:  What is Computational Linguistics? Computational Linguistics is the computational analysis of natural languages. Process information contained in natural language. Can machines understand human language? Define ‘understand’ Understanding is the ultimate goal. However, one doesn’t need to fully understand to be useful. Goals of this Lecture:  Goals of this Lecture Learn about the problems and possibilities of natural language analysis: What are the major issues? What are the major solutions? At the end you should: Agree that language is subtle and interesting! Know about some of the algorithms. Know how difficult it can be! It’s 2007, but we’re not anywhere close to realizing the dream (or nightmare …) of 2001:  It’s 2007, but we’re not anywhere close to realizing the dream (or nightmare …) of 2001 Slide5:  Dave Bowman: “Open the pod bay doors.” HAL 9000: “I’m sorry Dave. I’m afraid I can’t do that.” Dave Bowman: “Open the pod bay doors, please, HAL.” Why is NLP difficult?:  Why is NLP difficult? Computers are not brains There is evidence that much of language understanding is built-in to the human brain Computers do not socialize Much of language is about communicating with people Key problems: Representation of meaning Language presupposed knowledge about the world Language only reflects the surface of meaning Language presupposes communication between people Hidden Structure:  Hidden Structure English plural pronunciation Toy + s  toyz ; add z Book + s  books ; add s Church + s  churchiz ; add iz Box + s  boxiz ; add iz Sheep + s  sheep ; add nothing What about new words? Bach + ‘s  boxs ; why not boxiz? Language subtleties :  Language subtleties Adjective order and placement A big black dog A big black scary dog A big scary dog A scary big dog A black big dog Antonyms Which sizes go together? Big and little Big and small Large and small Large and little World Knowledge is subtle:  World Knowledge is subtle He arrived at the lecture. He chuckled at the lecture. He arrived drunk. He chuckled drunk. He chuckled his way through the lecture. He arrived his way through the lecture. Words are ambiguous (have multiple meanings):  Words are ambiguous (have multiple meanings) I know that. I know that block. I know that blocks the sun. I know that block blocks the sun. Headline Ambiguity:  Headline Ambiguity Iraqi Head Seeks Arms Juvenile Court to Try Shooting Defendant Teacher Strikes Idle Kids Kids Make Nutritious Snacks British Left Waffles on Falkland Islands Red Tape Holds Up New Bridges Bush Wins on Budget, but More Lies Ahead Hospitals are Sued by 7 Foot Doctors Ban on nude dancing on Governor’s desk Local high school dropouts cut in half The Role of Memorization:  The Role of Memorization Children learn words quickly As many as 9 words/day Often only need one exposure to associate meaning with word Can make mistakes, e.g., overgeneralization “I goed to the store.” Exactly how they do this is still under study The Role of Memorization:  The Role of Memorization Dogs can do word association too! Rico, a border collie in Germany Knows the names of each of 100 toys Can retrieve items called out to him with over 90% accuracy. Can also learn and remember the names of unfamiliar toys after just one encounter, putting him on a par with a three-year-old child. http://www.nature.com/news/2004/040607/pf/040607-8_pf.html But there is too much to memorize!:  But there is too much to memorize! establish establishment the church of England as the official state church. disestablishment antidisestablishment antidisestablishmentarian antidisestablishmentarianism is a political philosophy that is opposed to the separation of church and state. Rules and Memorization:  Rules and Memorization Current thinking in psycholinguistics is that we use a combination of rules and memorization However, this is very controversial Mechanism: If there is an applicable rule, apply it However, if there is a memorized version, that takes precedence. (Important for irregular words.) Artists paint “still lifes” Not “still lives” Past tense of think  thought blink  blinked This is a simplification; for more on this, see Pinker’s “Words and Language” and “The Language Instinct”. Representation of Meaning:  Representation of Meaning I know that block blocks the sun. How do we represent the meanings of “block”? How do we represent “I know”? How does that differ from “I know that.”? Who is “I”? How do we indicate that we are talking about earth’s sun vs. some other planet’s sun? When did this take place? What if I move the block? What if I move my viewpoint? How do we represent this? How to tackle these problems?:  How to tackle these problems? The field was stuck for quite some time. A new approach started around 1990 Well, not really new, but the first time around, in the 50’s, they didn’t have the text, disk space, or GHz Main idea: combine memorizing and rules How to do it: Get large text collections (corpora) Compute statistics over the words in those collections Surprisingly effective Even better now with the Web Corpus-based Example: Pre-Nominal Adjective Ordering:  Corpus-based Example: Pre-Nominal Adjective Ordering Important for translation and generation Examples: big fat Greek wedding fat Greek big wedding Some approaches try to characterize this as semantic rules, e.g.: Age < color, value < dimension Data-intensive approaches Assume adjective ordering is independent of the noun they modify Compare how often you see {a, b} vs {b, a} Keller & Lapata, “The Web as Baseline”, HLT-NAACL’04 Corpus-based Example: Pre-Nominal Adjective Ordering:  Corpus-based Example: Pre-Nominal Adjective Ordering Data-intensive approaches Compare how often you see {a, b} vs {b, a} What happens when you encounter an unseen pair? Shaw and Hatzivassiloglou ’99 use transitive closures Malouf ’00 uses a back-off bigram model P(<a,b>|{a,b}) vs. P(<b,a>|{a,b}) He also uses morphological analysis, semantic similarity calculations and positional probabilities Keller and Lapata ’04 use just the very simple algorithm But they use the web as their training set Gets 90% accuracy on 1000 sequences As good as or better than the complex algorithms Keller & Lapata, “The Web as Baseline”, HLT-NAACL’04 Real-World Applications of NLP:  Real-World Applications of NLP Spelling Suggestions/Corrections Grammar Checking Synonym Generation Information Extraction Text Categorization Automated Customer Service Speech Recognition (limited) Machine Translation In the (near?) future: Question Answering Improving Web Search Engine results Automated Metadata Assignment Online Dialogs Synonym Generation:  Synonym Generation Synonym Generation:  Synonym Generation Synonym Generation:  Synonym Generation Levels of Language:  Levels of Language Sound Structure (Phonetics and Phonology) The sounds of speech and their production The systematic way that sounds are differently realized in different environments. Word Structure (Morphology) From morphos = shape (not transform, as in morph) Analyzes how words are formed from minimal units of meaning; also derivational rules dog + s = dogs; eat, eats, ate Phrase Structure (Syntax) From the Greek syntaxis, arrange together Describes grammatical arrangements of words into hierarchical structure Levels of Language:  Levels of Language Thematic Structure Getting closer to meaning Who did what to whom Subject, object, predicate Semantic Structure How the lower levels combine to convey meaning Pragmatics and Discourse Structure How language is used across sentences. Parsing at Every Level:  Parsing at Every Level Transforming from a surface representation to an underlying representation It’s not straightforward to do any of these mappings! Ambiguity at every level Word: is “saw” a verb or noun? Phrase: “I saw the guy on the hill with the telescope.” Who is on the hill? Semantic: which hill? Tokens and Types :  Tokens and Types The term word can be used in two different ways: To refer to an individual occurrence of a word To refer to an abstract vocabulary item For example, the sentence “my dog likes his dog” contains five occurrences of words, but four vocabulary items. To avoid confusion use more precise terminology: Word token: an occurrence of a word Word Type: a vocabulary item Tokenization (continued):  Tokenization (continued) Tokenization is harder that it seems I’ll see you in New York. The aluminum-export ban. The simplest approach is to use “graphic words” (i.e., separate words using whitespace) Another approach is to use regular expressions to specify which substrings are valid words. NLTK provides a generic tokenization interface: TokenizerI Terminology:  Terminology Tagging The process of associating labels with each token in a text Tags The labels Tag Set The collection of tags used for a particular task Example:  Example Typically a tagged text is a sequence of white-space separated base/tag tokens: The/at Pantheon’s/np interior/nn ,/,still/rb in/in its/pp original/jj form/nn ,/, is/bez truly/ql majestic/jj and/cc an/at architectural/jj triumph/nn ./. Its/pp rotunda/nn forms/vbz a/at perfect/jj circle/nn whose/wp diameter/nn is/bez equal/jj to/in the/at height/nn from/in the/at floor/nn to/in the/at ceiling/nn ./. What does Tagging do?:  What does Tagging do? Collapses Distinctions Lexical identity may be discarded e.g. all personal pronouns tagged with PRP Introduces Distinctions Ambiguities may be removed e.g. deal tagged with NN or VB e.g. deal tagged with DEAL1 or DEAL2 Helps classification and prediction Significance of Parts of Speech:  Significance of Parts of Speech A word’s POS tells us a lot about the word and its neighbors: Limits the range of meanings (deal), pronunciation (object vs object) or both (wind) Helps in stemming Limits the range of following words for Speech Recognition Can help select nouns from a document for IR Basis for partial parsing (chunked parsing) Parsers can build trees directly on the POS tags instead of maintaining a lexicon Choosing a tagset:  Choosing a tagset The choice of tagset greatly affects the difficulty of the problem Need to strike a balance between Getting better information about context (best: introduce more distinctions) Make it possible for classifiers to do their job (need to minimize distinctions) Some of the best-known Tagsets:  Some of the best-known Tagsets Brown corpus: 87 tags Penn Treebank: 45 tags Lancaster UCREL C5 (used to tag the BNC): 61 tags Lancaster C7: 145 tags The Brown Corpus :  The Brown Corpus The first digital corpus (1961) Francis and Kucera, Brown University Contents: 500 texts, each 2000 words long From American books, newspapers, magazines Representing genres: Science fiction, romance fiction, press reportage scientific writing, popular lore Penn Treebank:  Penn Treebank First syntactically annotated corpus 1 million words from Wall Street Journal Part of speech tags and syntax trees How hard is POS tagging?:  How hard is POS tagging? In the Brown corpus, - 11.5% of word types ambiguous - 40% of word TOKENS Important Penn Treebank tags:  Important Penn Treebank tags Verb inflection tags:  Verb inflection tags The entire Penn Treebank tagset :  The entire Penn Treebank tagset Tagging methods:  Tagging methods Hand-coded Statistical taggers Brill (transformation-based) tagger Default Tagger:  Default Tagger We need something to use for unseen words E.g., guess NNP for a word with an initial capital How to do this? Apply a sequence of regular expression tests Assign the word to a suitable tag If there are no matches… Assign to the most frequent unknown tag, NN Other common ones are verb, proper noun, adjective Note the role of closed-class words in English Prepositions, auxiliaries, etc. New ones do not tend to appear. Training vs. Testing:  Training vs. Testing A fundamental idea in computational linguistics Start with a collection labeled with the right answers Supervised learning Usually the labels are done by hand “Train” or “teach” the algorithm on a subset of the labeled text. Test the algorithm on a different set of data. Why? If memorization worked, we’d be done. Need to generalize so the algorithm works on examples that you haven’t seen yet. Thus testing only makes sense on examples you didn’t train on. Evaluating a Tagger:  Evaluating a Tagger Tagged tokens – the original data Untag (exclude) the data Tag the data with your own tagger Compare the original and new tags Iterate over the two lists checking for identity and counting Accuracy = fraction correct Language Modeling :  Language Modeling Another fundamental concept in NLP Main idea: For a given language, some words are more likely than others to follow each other, or You can predict (with some degree of accuracy) the probability that a given word will follow another word. N-Grams:  N-Grams The N stands for how many terms are used Unigram: 1 term Bigram: 2 terms Trigrams: 3 terms Usually don’t go beyond this You can use different kinds of terms, e.g.: Character based n-grams Word-based n-grams POS-based n-grams Ordering Often adjacent, but not required We use n-grams to help determine the context in which some linguistic phenomenon happens. E.g., look at the words before and after the period to see if it is the end of a sentence or not. Features and Contexts:  Features and Contexts wn-2 wn-1 wn wn+1 CONTEXT FEATURE CONTEXT tn-2 tn-1 tn tn+1 Unigram Tagger:  Unigram Tagger Trained using a tagged corpus to determine which tags are most common for each word. E.g. in tagged WSJ sample, “deal” is tagged with NN 11 times, with VB 1 time, and with VBP 1 time Performance is highly dependent on the quality of its training set. Can’t be too small Can’t be too different from texts we actually want to tag Nth Order Tagging:  Nth Order Tagging Order refers to how much context It’s one less than the N in N-gram here because we use the target word itself as part of the context. Oth order = unigram tagger 1st order = bigrams 2nd order = trigrams Bigram tagger For tagging, in addition to considering the token’s type, the context also considers the tags of the n preceding tokens What is the most likely tag for w_n, given w_n-1 and t_n-1? The tagger picks the tag which is most likely for that context. Tagging with lexical frequencies:  Tagging with lexical frequencies Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN Problem: assign a tag to race given its lexical frequency Solution: we choose the tag that has the greater P(race|VB) P(race|NN) Actual estimate from the Switchboard corpus: P(race|NN) = .00041 P(race|VB) = .00003 Rule-Based Tagger:  Rule-Based Tagger The Linguistic Complaint Where is the linguistic knowledge of a tagger? Just a massive table of numbers Aren’t there any linguistic insights that could emerge from the data? Could thus use handcrafted sets of rules to tag input sentences, for example, if input follows a determiner tag it as a noun. The Brill tagger:  The Brill tagger An example of TRANSFORMATION-BASED LEARNING Very popular (freely available, works fairly well) A SUPERVISED method: requires a tagged corpus Basic idea: do a quick job first (using frequency), then revise it using contextual rules Brill Tagging: In more detail:  Brill Tagging: In more detail Start with simple (less accurate) rules…learn better ones from tagged corpus Tag each word initially with most likely POS Examine set of transformations to see which improves tagging decisions compared to tagged corpus Re-tag corpus using best transformation Repeat until, e.g., performance doesn’t improve Result: tagging procedure (ordered list of transformations) which can be applied to new, untagged text An example:  An example Examples: It is expected to race tomorrow. The race for outer space. Tagging algorithm: Tag all uses of “race” as NN (most likely tag in the Brown corpus) It is expected to race/NN tomorrow the race/NN for outer space Use a transformation rule to replace the tag NN with VB for all uses of “race” preceded by the tag TO: It is expected to race/VB tomorrow the race/NN for outer space Transformation-based learning in the Brill tagger:  Transformation-based learning in the Brill tagger Tag the corpus with the most likely tag for each word Choose a TRANSFORMATION that deterministically replaces an existing tag with a new one such that the resulting tagged corpus has the lowest error rate Apply that transformation to the training corpus Repeat Return a tagger that first tags using unigrams then applies the learned transformations in order Examples of learned transformations:  Examples of learned transformations Templates:  Templates Probabilities in Language Modeling :  Probabilities in Language Modeling A fundamental concept in NLP Main idea: For a given language, some words are more likely than others to follow each other, or You can predict (with some degree of accuracy) the probability that a given word will follow another word. Next Word Prediction:  Next Word Prediction From a NY Times story... Stocks ... Stocks plunged this …. Stocks plunged this morning, despite a cut in interest rates Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall ... Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall Street began Slide60:  Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall Street began trading for the first time since last … Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall Street began trading for the first time since last Tuesday's terrorist attacks. Human Word Prediction:  Human Word Prediction Clearly, at least some of us have the ability to predict future words in an utterance. How? Domain knowledge Syntactic knowledge Lexical knowledge Claim:  Claim A useful part of the knowledge needed to allow word prediction can be captured using simple statistical techniques In particular, we'll rely on the notion of the probability of a sequence (a phrase, a sentence) Applications:  Applications Why do we want to predict a word, given some preceding words? Rank the likelihood of sequences containing various alternative hypotheses, e.g. for ASR Theatre owners say popcorn/unicorn sales have doubled... Assess the likelihood/goodness of a sentence for text generation or machine translation. The doctor recommended a cat scan. El doctor recommendó una exploración del gato. N-Gram Models of Language:  N-Gram Models of Language Use the previous N-1 words in a sequence to predict the next word Language Model (LM) unigrams, bigrams, trigrams,… How do we train these models? Very large corpora Simple N-Grams:  Simple N-Grams Assume a language has V word types in its lexicon, how likely is word x to follow word y? Simplest model of word probability: 1/V Alternative 1: estimate likelihood of x occurring in new text based on its general frequency of occurrence estimated from a corpus (unigram probability) popcorn is more likely to occur than unicorn Alternative 2: condition the likelihood of x occurring in the context of previous words (bigrams, trigrams,…) mythical unicorn is more likely than mythical popcorn A Word on Notation:  A Word on Notation P(unicorn) Read this as “The probability of seeing the token unicorn” Unigram tagger uses this. P(unicorn|mythical) Called the Conditional Probability. Read this as “The probability of seeing the token unicorn given that you’ve seen the token mythical Bigram tagger uses this. Related to the conditional frequency distributions that we’ve been working with. Computing the Probability of a Word Sequence:  Computing the Probability of a Word Sequence Compute the product of component conditional probabilities? P(the mythical unicorn) = P(the) P(mythical|the) P(unicorn|the mythical) The longer the sequence, the less likely we are to find it in a training corpus P(Most biologists and folklore specialists believe that in fact the mythical unicorn horns derived from the narwhal) Solution: approximate using n-grams Bigram Model:  Bigram Model Approximate by P(unicorn|the mythical) by P(unicorn|mythical) Markov assumption: The probability of a word depends only on the probability of a limited history Generalization: The probability of a word depends only on the probability of the n previous words trigrams, 4-grams, … the higher n is, the more data needed to train backoff models Using N-Grams:  Using N-Grams For N-gram models P(wn-1,wn) = P(wn | wn-1) P(wn-1) By the Chain Rule we can decompose a joint probability, e.g. P(w1,w2,w3) P(w1,w2, ...,wn) = P(w1|w2,w3,...,wn) P(w2|w3, ...,wn) … P(wn-1|wn) P(wn) For bigrams then, the probability of a sequence is just the product of the conditional probabilities of its bigrams P(the,mythical,unicorn) = P(unicorn|mythical)P(mythical|the) P(the|<start>) Training and Testing:  Training and Testing N-Gram probabilities come from a training corpus overly narrow corpus: probabilities don't generalize overly general corpus: probabilities don't reflect task or domain A separate test corpus is used to evaluate the model, typically using standard metrics held out test set; development test set cross validation results tested for statistical significance Shallow (Chunk) Parsing:  Shallow (Chunk) Parsing Goal: divide a sentence into a sequence of chunks. Chunks are non-overlapping regions of a text [I] saw [a tall man] in [the park]. Chunks are non-recursive A chunk can not contain other chunks Chunks are non-exhaustive Not all words are included in chunks Chunk Parsing Examples:  Chunk Parsing Examples Noun-phrase chunking: [I] saw [a tall man] in [the park]. Verb-phrase chunking: The man who [was in the park] [saw me]. Prosodic chunking: [I saw] [a tall man] [in the park]. Question answering: What [Spanish explorer] discovered [the Mississippi River]? Shallow Parsing: Motivation:  Shallow Parsing: Motivation Locating information e.g., text retrieval Index a document collection on its noun phrases Ignoring information Generalize in order to study higher-level patterns e.g. phrases involving “gave” in Penn treebank: gave NP; gave up NP in NP; gave NP up; gave NP help; gave NP to NP Sometimes a full parse has too much structure Too nested Chunks usually are not recursive Representation:  Representation BIO (or IOB) Trees Comparison with Full Syntactic Parsing :  Comparison with Full Syntactic Parsing Parsing is usually an intermediate stage Builds structures that are used by later stages of processing Full parsing is a sufficient but not necessary intermediate stage for many NLP tasks Parsing often provides more information than we need Shallow parsing is an easier problem Less word-order flexibility within chunks than between chunks More locality: Fewer long-range dependencies Less context-dependence Less ambiguity Chunks and Constituency:  Chunks and Constituency Constituents: [[a tall man] [ in [the park]]]. Chunks: [a tall man] in [the park]. A constituent is part of some higher unit in the hierarchical syntactic parse Chunks are not constituents Constituents are recursive But, chunks are typically subsequences of constituents Chunks do not cross major constituent boundaries Chunking:  Chunking Define a regular expression that matches the sequences of tags in a chunk A simple noun phrase chunk regexp: (Note that <NN.*> matches any tag starting with NN) <DT>? <JJ>* <NN.?> Chunk all matching subsequences: the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN [the/DT little/JJ cat/NN] sat/VBD on/IN [the/DT mat/NN] If matching subsequences overlap, first 1 gets priority Unchunking:  Unchunking Remove any chunk with a given pattern e.g., unChunkRule(‘<NN|DT>+’, ‘Unchunk NNDT’) Combine with Chunk Rule <NN|DT|JJ>+ Chunk all matching subsequences: Input: the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN Apply chunk rule [the/DT little/JJ cat/NN] sat/VBD on/IN [the/DT mat/NN] Apply unchunk rule [the/DT little/JJ cat/NN] sat/VBD on/IN the/DT mat/NN Chinking:  Chinking A chink is a subsequence of the text that is not a chunk. Define a regular expression that matches the sequences of tags in a chink A simple chink regexp for finding NP chunks: (<VB.?>|<IN>)+ First apply chunk rule to chunk everything Input: the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN ChunkRule('<.*>+', ‘Chunk everything’) [the/DT little/JJ cat/NN sat/VBD on/IN the/DT mat/NN] Apply Chink rule above: [the/DT little/JJ cat/NN] sat/VBD on/IN [the/DT mat/NN] Chink Chunk Chunk Merging:  Merging Combine adjacent chunks into a single chunk Define a regular expression that matches the sequences of tags on both sides of the point to be merged Example: Merge a chunk ending in JJ with a chunk starting with NN MergeRule(‘<JJ>’, ‘<NN>’, ‘Merge adjs and nouns’) [the/DT little/JJ] [cat/NN] sat/VBD on/IN the/DT mat/NN [the/DT little/JJ cat/NN] sat/VBD on/IN the/DT mat/NN Splitting is the opposite of merging Applying Chunking to Treebank Data:  Applying Chunking to Treebank Data Classifying at Different Granularies:  Classifying at Different Granularies Text Categorization: Classify an entire document Information Extraction (IE): Identify and classify small units within documents Named Entity Extraction (NE): A subset of IE Identify and classify proper names People, locations, organizations Example: The Problem: Looking for a Job:  Example: The Problem: Looking for a Job Martin Baker, a person Genomics job Employers job posting form What is Information Extraction:  What is Information Extraction Filling slots in a database from sub-segments of text. As a task: October 14, 2002, 4:00 a.m. PT For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation. Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers. "We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“ Richard Stallman, founder of the Free Software Foundation, countered saying… What is Information Extraction:  What is Information Extraction Filling slots in a database from sub-segments of text. As a task: October 14, 2002, 4:00 a.m. PT For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation. Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers. "We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“ Richard Stallman, founder of the Free Software Foundation, countered saying… NAME TITLE ORGANIZATION Bill Gates CEO Microsoft Bill Veghte VP Microsoft Richard Stallman founder Free Soft.. IE What is Information Extraction:  What is Information Extraction Information Extraction = segmentation + classification + association As a family of techniques: October 14, 2002, 4:00 a.m. PT For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation. Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers. "We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“ Richard Stallman, founder of the Free Software Foundation, countered saying… Microsoft Corporation CEO Bill Gates Microsoft Gates Microsoft Bill Veghte Microsoft VP Richard Stallman founder Free Software Foundation aka “named entity extraction” What is Information Extraction:  What is Information Extraction Information Extraction = segmentation + classification + association A family of techniques: October 14, 2002, 4:00 a.m. PT For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation. Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers. "We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“ Richard Stallman, founder of the Free Software Foundation, countered saying… Microsoft Corporation CEO Bill Gates Microsoft Gates Microsoft Bill Veghte Microsoft VP Richard Stallman founder Free Software Foundation What is Information Extraction:  What is Information Extraction Information Extraction = segmentation + classification + association A family of techniques: October 14, 2002, 4:00 a.m. PT For years, Microsoft Corporation CEO Bill Gates railed against the economic philosophy of open-source software with Orwellian fervor, denouncing its communal licensing as a "cancer" that stifled technological innovation. Today, Microsoft claims to "love" the open-source concept, by which software code is made public to encourage improvement and development by outside programmers. Gates himself says Microsoft will gladly disclose its crown jewels--the coveted code behind the Windows operating system--to select customers. "We can be open source. We love the concept of shared source," said Bill Veghte, a Microsoft VP. "That's a super-important shift for us in terms of code access.“ Richard Stallman, founder of the Free Software Foundation, countered saying… Microsoft Corporation CEO Bill Gates Microsoft Gates Microsoft Bill Veghte Microsoft VP Richard Stallman founder Free Software Foundation IE in Context:  IE in Context Create ontology Segment Classify Associate Cluster Load DB Spider Query, Search Data mine IE Document collection Database Filter by relevance Label training data Train extraction models Landscape of IE Tasks: Degree of Formatting:  Landscape of IE Tasks: Degree of Formatting Landscape of IE Tasks: Intended Breadth of Coverage:  Landscape of IE Tasks: Intended Breadth of Coverage Web site specific Genre specific Wide, non-specific Amazon.com Book Pages Resumes University Names Formatting Layout Language Landscape of IE Tasks” Complexity:  Landscape of IE Tasks” Complexity Landscape of IE Tasks: Single Field/Record:  Landscape of IE Tasks: Single Field/Record Single entity Person: Jack Welch Binary relationship Relation: Person-Title Person: Jack Welch Title: CEO N-ary record “Named entity” extraction Jack Welch will retire as CEO of General Electric tomorrow. The top role at the Connecticut company will be filled by Jeffrey Immelt. Relation: Company-Location Company: General Electric Location: Connecticut Relation: Succession Company: General Electric Title: CEO Out: Jack Welsh In: Jeffrey Immelt Person: Jeffrey Immelt Location: Connecticut State of the Art Performance: a sample:  State of the Art Performance: a sample Named entity recognition from newswire text Person, Location, Organization, … F1 in high 80’s or low- to mid-90’s Binary relation extraction Contained-in (Location1, Location2) Member-of (Person1, Organization1) F1 in 60’s or 70’s or 80’s Web site structure recognition Extremely accurate performance obtainable Human effort (~10min?) required on each site Three generations of IE systems:  Three generations of IE systems Hand-Built Systems – Knowledge Engineering [1980s– ] Rules written by hand Require experts who understand both the systems and the domain Iterative guess-test-tweak-repeat cycle Automatic, Trainable Rule-Extraction Systems [1990s– ] Rules discovered automatically using predefined templates, using automated rule learners Require huge, labeled corpora (effort is just moved!) Statistical Models [1997 – ] Use machine learning to learn which features indicate boundaries and types of entities. Learning usually supervised; may be partially unsupervised Landscape of IE Techniques :  Landscape of IE Techniques Any of these models can be used to capture words, formatting or both. Lexicons Alabama Alaska … Wisconsin Wyoming Abraham Lincoln was born in Kentucky. member? Trainable IE systems:  Trainable IE systems Pros Annotating text is simpler & faster than writing rules. Domain independent Domain experts don’t need to be linguists or programers. Learning algorithms ensure full coverage of examples. Cons Hand-crafted systems perform better, especially at hard tasks. (but this is changing) Training data might be expensive to acquire May need huge amount of training data Hand-writing rules isn’t that hard!! MUC: the genesis of IE:  MUC: the genesis of IE DARPA funded significant efforts in IE in the early to mid 1990’s. Message Understanding Conference (MUC) was an annual event/competition where results were presented. Focused on extracting information from news articles: Terrorist events Industrial joint ventures Company management changes Information extraction of particular interest to the intelligence community (CIA, NSA). (Note: early ’90’s) Message Understanding Conference (MUC):  Message Understanding Conference (MUC) Named entity Person, Organization, Location Co-reference Clinton  President Bill Clinton Template element Perpetrator, Target Template relation Incident Multilingual MUC Typical Text:  MUC Typical Text Bridgestone Sports Co. said Friday it has set up a joint venture in Taiwan with a local concern and a Japanese trading house to produce golf clubs to be shipped to Japan. The joint venture, Bridgestone Sports Taiwan Co., capitalized at 20 million new Taiwan dollars, will start production of 20,000 iron and “metal wood” clubs a month MUC Typical Text:  MUC Typical Text Bridgestone Sports Co. said Friday it has set up a joint venture in Taiwan with a local concern and a Japanese trading house to produce golf clubs to be shipped to Japan. The joint venture, Bridgestone Sports Taiwan Co., capitalized at 20 million new Taiwan dollars, will start production of 20,000 iron and “metal wood” clubs a month MUC Templates:  MUC Templates Relationship tie-up Entities: Bridgestone Sports Co, a local concern, a Japanese trading house Joint venture company Bridgestone Sports Taiwan Co Activity ACTIVITY 1 Amount NT$2,000,000 MUC Templates:  MUC Templates ATIVITY 1 Activity Production Company Bridgestone Sports Taiwan Co Product Iron and “metal wood” clubs Start Date January 1990 Slide106:  Example of IE from FASTUS (1993) Slide107:  Example of IE: FASTUS(1993) Slide108:  Example of IE: FASTUS(1993): Resolving anaphora Evaluating IE Accuracy:  Evaluating IE Accuracy Always evaluate performance on independent, manually-annotated test data not used during system development. Measure for each test document: Total number of correct extractions in the solution template: N Total number of slot/value pairs extracted by the system: E Number of extracted slot/value pairs that are correct (i.e. in the solution template): C Compute average value of metrics adapted from IR: Recall = C/N Precision = C/E F-Measure = Harmonic mean of recall and precision MUC Information Extraction: State of the Art c. 1997:  MUC Information Extraction: State of the Art c. 1997 NE – named entity recognition CO – coreference resolution TE – template element construction TR – template relation construction ST – scenario template production Finite State Transducers for IE:  Finite State Transducers for IE Basic method for extracting relevant information IE systems generally use a collection of specialized FSTs Company Name detection Person Name detection Relationship detection Three Equivalent Representations:  Three Equivalent Representations Finite automata Regular expressions Regular languages Each can describe the others Theorem: For every regular expression, there is a deterministic finite-state automaton that defines the same language, and vice versa. Question Answering:  Question Answering Today: Introduction to QA A typical full-fledged QA system A very simple system, in response to this An intermediate approach Wednesday: Using external resources WordNet Encyclopedias, Gazeteers Incorporating a reasoning system Machine Learning of mappings Other question types (e.g., biography, definitions) A of Search Types:  A of Search Types What is the typical height of a giraffe? What are some good ideas for landscaping my client’s yard? What are some promising untried treatments for Raynaud’s disease? Beyond Document Retrieval:  Document Retrieval Users submit queries corresponding to their information needs. System returns (voluminous) list of full-length documents. It is the responsibility of the users to find information of interest within the returned documents. Open-Domain Question Answering (QA) Users ask questions in natural language. What is the highest volcano in Europe? System returns list of short answers. … Under Mount Etna, the highest volcano in Europe, perches the fabulous town … A real use for NLP Beyond Document Retrieval Questions and Answers:  Questions and Answers What is the height of a typical giraffe? The result can be a simple answer, extracted from existing web pages. Can specify with keywords or a natural language query However, most web search engines are not set up to handle questions properly. Get different results using a question vs. keywords The Problem of Question Answering:  The Problem of Question Answering What is the nationality of Pope John Paul II? … stabilize the country with its help, the Catholic hierarchy stoutly held out for pluralism, in large part at the urging of Polish-born Pope John Paul II. When the Pope emphatically defended the Solidarity trade union during a 1987 tour of the… When was the San Francisco fire? … were driven over it. After the ceremonial tie was removed - it burned in the San Francisco fire of 1906 – historians believe an unknown Chinese worker probably drove the last steel spike into a wooden tie. If so, it was only… Where is the Taj Mahal? … list of more than 360 cities around the world includes the Great Reef in Australia, the Taj Mahal in India, Chartre’s Cathedral in France, and Serengeti National Park in Tanzania. The four sites Japan has listed include… The Problem of Question Answering:  The Problem of Question Answering What is the nationality of Pope John Paul II? … stabilize the country with its help, the Catholic hierarchy stoutly held out for pluralism, in large part at the urging of Polish-born Pope John Paul II. When the Pope emphatically defended the Solidarity trade union during a 1987 tour of the… Natural language question, not keyword queries Short text fragment, not URL list Question Answering from text:  Question Answering from text With massive collections of full-text documents, simply finding relevant documents is of limited use: we want answers QA: give the user a (short) answer to their question, perhaps supported by evidence. An alternative to standard IR The first problem area in IR where NLP is really making a difference. People want to ask questions…:  People want to ask questions… Examples from AltaVista query log who invented surf music? how to make stink bombs where are the snowdens of yesteryear? which english translation of the bible is used in official catholic liturgies? how to do clayart how to copy psx how tall is the sears tower? Examples from Excite query log (12/1999) how can i find someone in texas where can i find information on puritan religion? what are the 7 wonders of the world how can i eliminate stress What vacuum cleaner does Consumers Guide recommend A Brief (Academic) History:  A Brief (Academic) History In some sense question answering is not a new research area Question answering systems can be found in many areas of NLP research, including: Natural language database systems A lot of early NLP work on these Problem-solving systems STUDENT (Winograd ’77) LUNAR (Woods & Kaplan ’77) Spoken dialog systems Currently very active and commercially relevant The focus is now on open-domain QA is new First modern system: MURAX (Kupiec, SIGIR’93): Trivial Pursuit questions Encyclopedia answers FAQFinder (Burke et al. ’97) TREC QA competition (NIST, 1999–present) AskJeeves :  AskJeeves AskJeeves is probably most hyped example of “Question answering” How it used to work: Do pattern matching to match a question to their own knowledge base of questions If a match is found, returns a human-curated answer to that known question If that fails, it falls back to regular web search (Seems to be more of a meta-search engine now) A potentially interesting middle ground, but a fairly weak shadow of real QA Question Answering at TREC:  Question Answering at TREC Question answering competition at TREC consists of answering a set of 500 fact-based questions, e.g., “When was Mozart born?”. Has really pushed the field forward. The document set Newswire textual documents from LA Times, San Jose Mercury News, Wall Street Journal, NY Times etcetera: over 1M documents now. Well-formed lexically, syntactically and semantically (were reviewed by professional editors). The questions Hundreds of new questions every year, the total is ~2400 Task Initially extract at most 5 answers: long (250B) and short (50B). Now extract only one exact answer. Several other sub-tasks added later: definition, list, biography. Sample TREC questions:  Sample TREC questions 1. Who is the author of the book, "The Iron Lady: A Biography of Margaret Thatcher"? 2. What was the monetary value of the Nobel Peace Prize in 1989? 3. What does the Peugeot company manufacture? 4. How much did Mercury spend on advertising in 1993? 5. What is the name of the managing director of Apricot Computer? 6. Why did David Koresh ask the FBI for a word processor? 7. What is the name of the rare neurological disease with symptoms such as: involuntary movements (tics), swearing, and incoherent vocalizations (grunts, shouts, etc.)? TREC Scoring:  TREC Scoring For the first three years systems were allowed to return 5 ranked answer snippets (50/250 bytes) to each question. Mean Reciprocal Rank Scoring (MRR): Each question assigned the reciprocal rank of the first correct answer. If correct answer at position k, the score is 1/k. 1, 0.5, 0.33, 0.25, 0.2, 0 for 1, 2, 3, 4, 5, 6+ position Mainly Named Entity answers (person, place, date, …) From 2002 on, the systems are only allowed to return a single exact answer and the notion of confidence has been introduced. Top Performing Systems:  Top Performing Systems In 2003, the best performing systems at TREC can answer approximately 60-70% of the questions Approaches and successes have varied a fair deal Knowledge-rich approaches, using a vast array of NLP techniques stole the show in 2000-2003 Notably Harabagiu, Moldovan et al. ( SMU/UTD/LCC ) Statistical systems starting to catch up AskMSR system stressed how much could be achieved by very simple methods with enough text (and now various copycats) People are experimenting with machine learning methods Middle ground is to use large collection of surface matching patterns (ISI) Example QA System:  Example QA System This system contains many components used by other systems, but more complex in some ways Most work completed in 2001; there have been advances by this group and others since then. Next slides based mainly on: Paşca and Harabagiu, High-Performance Question Answering from Large Text Collections, SIGIR’01. Paşca and Harabagiu, Answer Mining from Online Documents, ACL’01. Harabagiu, Paşca, Maiorano: Experiments with Open-Domain Textual Question Answering. COLING’00 QA Block Architecture:  QA Block Architecture Question Processing Passage Retrieval Answer Extraction WordNet NER Parser WordNet NER Parser Document Retrieval Keywords Passages Question Semantics Q A Question Processing Flow:  Question Processing Flow Q Question parsing Construction of the question representation Answer type detection Keyword selection Question semantic representation AT category Keywords Question Stems and Answer Types:  Question Stems and Answer Types Other question stems: Who, Which, Name, How hot... Other answer types: Country, Number, Product... Identify the semantic category of expected answers Detecting the Expected Answer Type:  Detecting the Expected Answer Type In some cases, the question stem is sufficient to indicate the answer type (AT) Why  REASON When  DATE In many cases, the question stem is ambiguous Examples What was the name of Titanic’s captain ? What U.S. Government agency registers trademarks? What is the capital of Kosovo? Solution: select additional question concepts (AT words) that help disambiguate the expected answer type Examples captain agency capital Answer Type Taxonomy:  Answer Type Taxonomy Encodes 8707 English concepts to help recognize expected answer type Mapping to parts of Wordnet done by hand Can connect to Noun, Adj, and/or Verb subhierarchies Answer Type Detection Algorithm:  Answer Type Detection Algorithm Select the answer type word from the question representation. Select the word(s) connected to the question. Some “content-free” words are skipped (e.g. “name”). From the previous set select the word with the highest connectivity in the question representation. Map the AT word in a previously built AT hierarchy The AT hierarchy is based on WordNet, with some concepts associated with semantic categories, e.g. “writer”  PERSON. Select the AT(s) from the first hypernym(s) associated with a semantic category. Answer Type Hierarchy:  Answer Type Hierarchy PERSON PERSON Understanding a Simple Narrative:  Understanding a Simple Narrative Feb. 18, 2004 Yesterday Holly was running a marathon when she twisted her ankle. David had pushed her. Temporal Awareness of the Narrative Time Events, Activities, and States Anchoring the Events Ordering the Events Temporal Aspects of Narrative Text:  Temporal Aspects of Narrative Text Feb. 18, 2004 Yesterday Holly was running a marathon when she twisted her ankle. David had pushed her. 1. When did the running occur? Yesterday. 2. When did the twisting occur? Yesterday, during the running. 3. Did the pushing occur before the twisting? Yes. 4. Did Holly keep running after twisting her ankle? 5. Probably not. Temporal Assumptions :  Temporal Assumptions Time primitives are temporal intervals. No branching into the future or the past 13 basic (binary) interval relations [b,a,eq,o,oi,s,si,f,fi,d,di,m,mi], (six are inverses of the other six) Supported by a transitivity table that defines the conjunction of any two relations. All 13 relations can be expressed using meet: Before (X, Y)  Z , (meets(X, Z)  (meets (Z, Y)) Allen’s 13 Temporal Relations:  Allen’s 13 Temporal Relations A B A B A B A B A B A B A B A FINISHES B B is FINISHED by A A is BEFORE B B is AFTER A A MEETS B B is MET by A A OVERLAPS B B is OVERLAPPED by A A STARTS B B is STARTED by A A is EQUAL to B B is EQUAL to A A DURING B B CONTAINS A Allen’s Temporal Ontology:  Allen’s Temporal Ontology Properties hold over every subinterval of an interval —> Holds(p, T) e.g., ”John was sick for a day." Events hold only over an interval and not over any subinterval of it. —> Occurs(e, T) e.g., ”Mary wrote a letter this afternoon." Processes hold over some subintervals of the interval they occur in. —> Occuring(p, T) e.g., ”Mary is writing a letter today." Desiderata for Temporal Specification Language:  Desiderata for Temporal Specification Language Linguistic Expressiveness: Tense and Aspect Aspectual Classes Anchoring relations Ordering relations Temporal reference and reasoning Design Features XML Open architecture Compliant with other annotation schemes Transparent Semantics Temporal Expression Types:  Temporal Expression Types Times 3 o’clock mid-morning Dates Fully Specified June 11, 1989 Summer, 2002 Underspecified Monday Next month Last year Two days ago Durations Three months Two years Sets Every month Every Tuesday Events:  Events Can have temporal a/o spatial locations Can have types assassinations, bombings, joint ventures, etc. Can have people a/o other objects as participants Can be hypothetical Can have not happened… Different Notions of Events:  Different Notions of Events Topic: “well-defined subject” for searching document- or collection-level Template: structure with slots for participant named entities document-level Mention: linguistic expression that expresses an underlying event phrase-level (verb/noun) TIMEX3 Annotation Schema:  TIMEX3 Annotation Schema Time Points <TIMEX3 tid=“t1” type=“TIME” value=“T24:00”>midnight</TIMEX3> <TIMEX3 tid=“t2” type=“DATE” value=“2005-02-15” temporalFunction=“TRUE” anchorTimeID=“t0”>tomorrow</TIMEX3> Durations <TIMEX3 tid="t6" type="DURATION" value="P2W" beginPoint="t61" endPoint="t62">two weeks</TIMEX3> from <TIMEX3 tid="t61" type="DATE" value="2003-06-07">June 7, 2003</TIMEX3> <TIMEX3 tid="t62" type="DATE" value="2003-06-21" temporalFunction="true" anchorTimeID="t6"/> Sets <TIMEX3 tid=”t1” type=”SET” value=”P1M” quant=”EVERY” freq=”P3D”> three days every month</TIMEX3> <TIMEX3 tid=”t1” type=”SET” value=”P1M” freq=”P2X”> twice a month</TIMEX3> Example TIMEX3 Markup:  Example TIMEX3 Markup midnight type=“TIME” value=“T24:00” > tid=“t1” <TIMEX3 </TIMEX3> Example TIMEX3 Markup:  Example TIMEX3 Markup twice a month type=“SET” value=“P1M” > tid=“t1” <TIMEX3 </TIMEX3> freq=“P2X” Events and Times:  Events and Times Event expressions: tensed verbs; has left, was captured, will resign; stative adjectives; sunken, stalled, on board; event nominals; merger, Military Operation, lecture; Dependencies between events and times: Anchoring; John left on Monday. Orderings; The party happened after midnight. Embedding; John said Mary left. Features of TimeML:  Features of TimeML Extends TIMEX2 annotation; Temporal Functions: three years ago Anchors to events and other temporal expressions: three years after the Gulf War Identifies signals determining interpretation of temporal expressions; Temporal Prepositions: for, during, on, at; Temporal Connectives: before, after, while. Identifies event expressions; tensed verbs; has left, was captured, will resign; stative adjectives; sunken, stalled, on board; event nominals; merger, Military Operation, Gulf War; Creates dependencies between events and times: Anchoring; John left on Monday. Orderings; The party happened after midnight. Embedding; John said Mary left. Representing Time and Events in Text:  Representing Time and Events in Text AP-NR-08-15-90 1337EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediation effort, the Soviet Union said today it had sent an envoy to the Middle East on a series of stops to include Baghdad. Soviet officials also said Soviet women, children and invalids would be allowed to leave Iraq. Temporal Expressions:  Temporal Expressions AP-NR-08-15-90 1337EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediation effort, the Soviet Union said today it had sent an envoy to the Middle East on a series of stops to include Baghdad. Soviet officials also said Soviet women, children and invalids would be allowed to leave Iraq. TimeML Event Classes:  TimeML Event Classes Occurrence: die, crash, build, merge, sell, take advantage of, .. State: Be on board, kidnapped, recovering, love, .. Reporting: Say, report, announce, I-Action: Attempt, try,promise, offer I-State: Believe, intend, want, … Aspectual: begin, start, finish, stop, continue. Perception: See, hear, watch, feel. Event Expressions:  Event Expressions AP-NR-08-15-90 1337EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediation effort, the Soviet Union said today it had sent an envoy to the Middle East on a series of stops to include Baghdad. Soviet officials also said Soviet women, children and invalids would be allowed to leave Iraq. Links:  Links Temporal Relations Anchoring to a Time Ordering between Time and Events Aspectual Relations Phases of an event Subordinating Relations Events that syntactically subordinate other events TLINK Temporal Anchoring/Ordering:  TLINK Temporal Anchoring/Ordering Simultaneous (happening at the same time) Identical: (referring to the same event) John drove to Boston. During his drive he ate a donut. Before the other: In six of the cases suspects have already been arrested. After the other: Immediately before the other: All passengers died when the plane crashed into the mountain. Immediately after than the other: Including the other: John arrived in Boston last Thursday. Being included in the other: Exhaustively during the duration of the other: John taught for 20 minutes. Beginning of the other: John was in the gym between 6:00 p.m. and 7:00 p.m. Begun by the other: Ending of the other: John was in the gym between 6:00 p.m. and 7:00 p.m. Ended by the other: Linking Timex to Timex:  Linking Timex to Timex AP-NR-08-15-90 1337EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediation effort, the Soviet Union said today it had sent an envoy to the Middle East on a series of stops to include Baghdad. Soviet officials also said Soviet women, children and invalids would be allowed to leave Iraq. Anchoring Event to Timex:  Anchoring Event to Timex AP-NR-08-15-90 1337EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediation effort, the Soviet Union said today it had sent an envoy to the Middle East on a series of stops to include Baghdad. Soviet officials also said Soviet women, children and invalids would be allowed to leave Iraq. Ordering Events:  Ordering Events AP-NR-08-15-90 1337EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. Past < Tuesday < Today < Indef Future _______________________________________________________________________________ war(I,I) say(Bush,S) arrive(H,DC) withdraw(Saddam) captured(sold) Seek(Saddam,peace) release(Saddam,soldiers) extend(US,quarantine) ALINK:  ALINK ALINK or Aspectual Link represent the relationship between an aspectual event and its argument event. Examples of the possible aspectual relations we will encode are: 1. Initiation: John started to read. 2. Culmination: John finished assembling the table. 3. Termination: John stopped talking. 4. Continuation: John kept talking. Aspectual Links:  Aspectual Links President Bush today denounced Saddam's ``ruinous policies of war,'' and said the United States is ``striking a blow for the principle that might does not make right.'' In a speech delivered at the Pentagon, Bush seemed to suggest that American forces could be in the gulf region for some time. ``No one should doubt our staying power or determination,'' he said. The U.S. military buildup in Saudi Arabia continued at fever pace, with Syrian troops now part of a multinational force camped out in the desert to guard the Saudi kingdom from any new thrust by Iraq. In a letter to President Hashemi Rafsanjani of Iran, read by a broadcaster over Baghdad radio, Saddam said he will begin withdrawing troops from Iranian territory a week from tomorrow and release Iranian prisoners of war. Iran said an Iraqi diplomatic delegation was en route to Tehran to deliver Saddam's message, which it said it would review ``with optimism.'' SLINK:  SLINK SLINK or Subordination Link is used for contexts introducing relations between two events, or an event and a signal, of the following sort: Factive: Certain verbs introduce an entailment (or presupposition) of the argument's veracity. They include forget in the tensed complement, regret, manage: John forgot that he was in Boston last year. Mary regrets that she didn't marry John. Counterfactive: The event introduces a presupposition about the non-veracity of its argument: forget (to), unable to (in past tense), prevent, cancel, avoid, decline, etc. John forgot to buy some wine. John prevented the divorce. Evidential: Evidential relations are introduced by REPORTING or PERCEPTION: John said he bought some wine. Mary saw John carrying only beer. Negative evidential: Introduced by REPORTING (and PERCEPTION?) events conveying negative polarity: John denied he bought only beer. Conditional: Introduced when a conditional is present: If Mary leaves today, John will leave tomorrow. Subordinated Links:  Subordinated Links AP-NR-08-15-90 1337EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediation effort, the Soviet Union said today it had sent an envoy to the Middle East on a series of stops to include Baghdad. Soviet officials also said Soviet women, children and invalids would be allowed to leave Iraq. SLINK: Reported Speech:  SLINK: Reported Speech AP-NR-08-15-90 1337EDT Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi border, today sought peace on another front by promising to withdraw from Iranian territory and release soldiers captured during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in Washington seeking to mediate the Persian Gulf crisis. President Bush on Tuesday said the United States may extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off Iraq's last unhindered trade route. In another mediatio

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