Jarrar: Introduction to Natural Language Processing

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Information about Jarrar: Introduction to Natural Language Processing
Technology

Published on March 3, 2014

Author: jarrar02

Source: slideshare.net

Lecture Notes, Artificial Intelligence Course, University of Birzeit, Palestine Spring Semester, 2014 (Advanced/) Artificial Intelligence Introduction to Natural Language Processing Dr. Mustafa Jarrar Sina Institute, University of Birzeit mjarrar@birzeit.edu www.jarrar.info Jarrar © 2014 1

Watch this lecture and download the slides from http://jarrar-courses.blogspot.com/2011/11/artificial-intelligence-fall-2011.html Jarrar © 2014 2

Outline Ø  NLP Applications Ø  NLP and Intelligence Ø  Linguistics Levels of ambiguity Ø  Language Models Keywords: Natural Language Processing, NLP, NLP Applications, NLP and Intelligence, Linguistics Levels of ambiguity, Language Models, Part of Speech Tagging, ‫اﻟﻠﺴﺎﻧﻴﺎت اﳊﺎﺳﻮﺑﻴﺔ ,اﻟﻐﻤﻮض اﻟﻠﻐﻮي، اﻟﺘﺤﻠﻴﻞ اﻟﻠﻐﻮي اﻵﻟﻲ,ﺗﻄﺒﻴﻘﺎت ﻟﻐﻮﻳﺔ , اﳌﻌﺎﳉﺔ اﻵﻟﻴﺔ ﻟﻠﻐﺎت اﻟﻄﺒﻴﻌﻴﺔ‬ Jarrar © 2014 3

Motivation Which NLP applications do you use every day? (èhow much money these companies are making?) •  Google, Microsoft, Yahoo, •  Job Seeking •  Google translate Systran powers Babelfish •  Myspace, Facebook, Blogspot •  Tools for “business intelligence” •  ….. è Most ideas stem from Academia, but big guys have (several) strong NLP research labs (like Microsoft, Yahoo, AT&T, IBM, etc.) Jarrar © 2014 4

Why Natural Language Processing? •  Huge amounts of data on the Internet, Intranets, desktops, •  We need applications for processing (understanding, retrieving, translating, summarizing, …) this large amounts of texts. •  Modern applications contain many NLP components. Imagine your address book without good NLP to smartly search your contacts!!! Jarrar © 2014 5

NLP Applications §  Classifiers: classify a set of document into categories, (as spam filters) §  Information Retrieval: find relevant documents to a given query. §  Information Extraction: Extract useful information from resumes; discover names of people and events they participate in, from a document. §  Machine Translation: translate text from one human language into another §  Question Answering: find answers to natural language questions in a text collection or database… §  Summarization: Produce a readable summary, e.g., news about oil today. §  Sentiment Analysis, identify people opinion on a subjective. §  Speech Processing: book a hotel over the phone, TTS (for the blind) §  OCR: both print and handwritten. §  Spelling checkers, grammar checkers, auto-filling, ….. and more Jarrar © 2014 6

Natural Language? and Intelligence? •  Artificial languages, like C# and Java •  Automatic processing of computer languages is easy! why? •  Natural Language, that people speak, like English, Arabic, French •  Automatic processing (analyzing, understanding, generating,…) of natural languages is very difficult! why? •  Intelligence: Natural? and Artificial (AI). •  Computers are called intelligent if thy are able to process (analyze, understand, learn,…) natural languages as humans do. •  Modern NLP algorithms are based on machine learning, especially statistical machine learning. Jarrar © 2014 7

NLP Current Motives •  Historically: peaks and valleys. Now is a peak, 20 years ago may have been a valley. •  Security agencies are typically interested in NLP. •  Most big companies nowadays are interested in NLP •  The internet and mobile devices are important driving forces in NLP research. Jarrar © 2014 8

Computers Lack Knowledge! Based on [1] This is how computers “see” text in English. kJfmmfj mmmvvv nnnffn333 Uj iheale eleee mnster vensi credur Baboi oi cestnitze Coovoel2^ ekk; ldsllk lkdf vnnjfj? Fgmflmllk mlfm kfre xnnn! •  People have no trouble understanding language §  Common sense knowledge §  Reasoning capacity §  Experience •  Computers have §  No common sense knowledge §  No reasoning capacity Jarrar © 2014 9

Linguistics Levels of Ambiguity/Analysis Based on [1] Speech Written language –  Phonology: sounds / letters / pronunciation (two, too. !"#$ ،!"#&) –  Morphology: the structure of words (child – children, book - books; '()*-'(‫)(0#ب-(0/، .-'-أ.-#ل، أ‬ –  Syntax: grammar, how these sequences are structured I saw the man with the telescope ‫رأ*08 7#654#رة‬ –  Semantics: meaning of the strings (table as data structure, table as furniture. 9:;-‫-@?>، =!ول‬AB-‫)=!ول‬ Ø  Dealing with all of these levels of ambiguity make NLP difficult Jarrar © 2014 10

Issues in Syntax Based on [1] Syntax does not deal with the meaning of a sentence, but it may help?! “the dog ate my homework” Who ate? àdog The important thing when we analyze a syntax is to identify the part of speech (POS): Dog = noun ; ate = verb ; homework = noun There are programs that do this automatically, called: Part of Speech Taggers. (also called grammatical tagging) Accuracy of English POS tagging: 95%. Identify collocations mother in law, hot dog Compositional versus non-compositional collocates Jarrar © 2014 11

Issues in Syntax (Part of Speech Tagging) Based on [1] Assume input sentence S in natural language L. Assume you have rules (grammar G) that describe syntactic regularities (patterns or structures). Given S & G, find syntactic structure of S. Such a structure is called a Parse Tree Pars tree: John loves Mary S(Loves(John, Mary) NP(John) VP(x Loves (x,Mary) NP(Mary) Name(John) Verb(x,y Loves(x,y))) Name(Mary) John loves Mary Helps a computer to automatically answer questions like -Who did what and when? Jarrar © 2014 12

Issues in Syntax Based on [1] Shallow Parsing: An analysis of a sentence which identifies the constituents (noun groups,verbs, verb groups, etc.), but does not specify their internal structure, nor their role in the main sentence. Example: “John Loves Mary” “John” “Loves Mary” subject predicate Identify basic structures as: NP-[John] VP-[Loves Mary] Jarrar © 2014 13

More Issues in Syntax Based on [1] Anaphora Resolution: resolving what a pronoun, or a noun phrase refers to. “The dog entered my room. It scared me” Preposition Attachment I saw the man in the park with a telescope ‫ 7#654#رة‬C6#D6‫ ا69=' ا‬F*‫رأ‬ The son asked the father to drive him home #G9HI JK-AL F5M6‫ ا‬NB ‫م‬P‫ ا‬FMQ. Jarrar © 2014 14

Issues in Semantics How to understand the meaning, specially that words are ambiguous and polysemous (may have multiple meanings) Buy this table? serve that table? sort the table? .‫ﻫﻞ رأﻳﺖ ﻫﺬه اﻟﻄﺎوﻟﺔ. ﻫﻞ ﺧﺪﻣﺖ ﻫﺬه اﻟﻄﺎوﻟﺔ‬ How to learn the meaning of words? - From available dictionaries? WordNet? - Applying statistical methods on annotated examples? How to learn the meaning (word-sense disambiguation)? Assume a (large) amount of annotated data = training Assume a new text not annotated = test Learn from previous experience (training) to classify new data (test) Decision trees, memory based learning, neural networks Jarrar © 2014 15

Language Models Three approaches to Natural Language Processing (Language Models) –  Rule-based: using a predefined set of rules (knowledge) –  Statistical: using probabilities of what normally people write or say –  Hybrid models combine the two Jarrar © 2014 16

Acknowledgement Some of the slides in this lecture are based on the following resources , but with many additions and revision: [1] Rada Mihalcea: Natural Language Processing, 2008 www.cs.odu.edu/~mukka/cs480f09/Lecturenotes/.../Intro1.ppt [2] Markus Dickinson: Introduction to Natural Language Processing (NLP), Linguistics 362 course, 2006 http://www9.georgetown.edu/faculty/mad87/06/362/syllabus.html Jarrar © 2014 17

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