Deep Distillation from Natural Language Text

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Information about Deep Distillation from Natural Language Text
Technology

Published on March 19, 2014

Author: nashish

Source: slideshare.net

Description

Invited Talk at Text Analytics World TAW 2014

Deep Distillation from Text Naveen Ashish University of Southern California & Cognie Inc., March 18th 2014

This is about ….. “DEEP TEXT DISTILLATION” The hard nut of having computers “understand” natural language (text) ….  Pushing the boundaries of what we can achieve …. "It's (the problem of computers understanding natural language) ambitious ...in fact there's no more important project than understanding intelligence and recreating it.“ - Ray Kurzweil (2013) Alan Turing based the Turing Test entirely on written language….To really master natural language …that’s the key to the Turing Test–to a human requires the full scope of human intelligence. …So the point is that natural language is a very profound domain to do artificial intelligence in. - Ray Kurzweil (2013)

Why ….  the problem is far from solved ….. !!!!  unstructured data everywhere 95 % ! search text analytics big data analytics health informatics social-media intelligence

Introduction About myself Associate Professor (Informatics), Keck School of Medicine, University of Southern California Cognie Inc., Work leverages Information extraction work and systems developed at UC Irvine  XAR, UCI-PEP Advisory consulting engagements with several companies and start-ups

Outline Deep distillation: What is and why State-of-the-art Fundamentals Approach Details Expressions, Entities, Sentiment Case studies Retail, Health, Risk assessment Conclusions

What is “Deep” text distillation ?

Data Abstract This paper describes the results of a study investigating …. ….. We conclude that salt and diabetes are largely unrelated.

Deep Distillation The abstract, not explicitly mentioned ! What falls in this category Expressions Contextual sentiment Aspect classification I think you need better chefs  SUGGESTION The mocha is too sweet  NEGATIVE I used to take Lipitor for … PERSONAL EXPERIENCE The dim lights have a cozy effect …. AMBIENCE

A Common Intersection Distill at sentence level Aggregate to entire feedback, post, comment or thread Three primary elements Expression/Intent Entities/Aspects (and Classes) Sentiment

Why Deeper ?  Goal: Get actionable insights from data !  Hypothesis: Deeper extraction  Better insights ! The top advice items advised for skin rash are aloe vera, vitamin E oil and oatmeal Complaints comprise 36% of the overall feedback with top issues being slow service, drinks and coffee

Context COGNIETM: A PLATFORM for text analytics COGNIE TM XAR UCI-PEP SHIP SURVEY ANALYTICS RETAIL ANALYTICS RISK ASSESSMENT

Expressions Beyond entities and sentiment : EXPRESSSIONS EXPRESSIONS Introduced in [Ashish et al, 2011]

Expressions You should try Vitamin E oil …  ADVICE ..I have had arthritis since 1991…  EXPERIENCE HEALTH ..for me lipitor worked like a charm…  OUTCOME

Expressions …showers had no hot water !…  COMPLAINT ..you should have more veggie options…  SUGGESTION RETAIL/ENTERPRISE ..meats on special this weekend…  ANNOUNCEMENT ..this is the best store on the west side…  ADVOCACY There is hardly any evidence to suggest a link between salt and diabetes  - This results confirm that high intake of salt leads to increase in BP + RISK ASSESSMENT

The Landscape

Text Analytics Spectrum Wide offering of  Text analytics engines  Text analysis tools – many open-source Largely still for “spotting things”  entities, concepts, sentiment, topics, emotions …. Going deeper  Luminoso  Attensity (Intents) Deep Learning for Sentiment  Stanford  Recursive Neural Networks

Approach

Approach natural language processing machine learning semantics

Architecture: COGNIE TM Platform Segmentation POS Tagging Entity extraction Anaphora Parsing Gram analysis Existing (DMOZ, SNOMED,UMLS) Creation Declarative Naïve-Bayes MaxEnt TFIDF CRF RNN Deep Learning ENSEMBLE NLP Machine Learning Knowledge Engineering

The Indicators: “Give Aways” A combination of multiple types of elements ! …showers had no hot water !… COMPLAINT (You) should have more veggie options… SUGGESTION ..i have been on lipitor… EXPERIENCE ..this is the best store on the west side… ADVOCACY

Approach: Given Indicators NLP Identification of individual elements  Unsupervised Relationships between elements Semantics Identification of individual elements  Knowledge driven Machine Learning Classification Combine elements  classify

Natural Language Processing  UIMA and GATE  Stanford NLP Tools POS tagging  Parsing  NE Recognizer  Geo-tagger  ….

Natural Language Processing  Text Segmentation In many cases the “unit” if distillation is a sentence  Segmentation  UIMA (or GATE)  Custom  Complex sentence segmentation  Breakup into individual clauses

NLP  Part-of-speech tags are key indicators Expression distillation  Entity extraction Names, Locations, Organizations  Parsing If required  Anaphora

NGram Analysis Unigram and Bigram analysis Obtain Grams Frequency Entropy Grams of tokens as well as POS Patterns VB VBD

Before Automated Classification: Manual Patterns SoL: Sequences of Labels Labels LEX-FOODADJ  spicy LEX-EXCESS  too, very ONT-FOOD POS-NOUN Sequences (Patterns) ANY LEX-EXCESS LEX-FOODADJ ANY  POS-VB POS-MD ….

Classification: Machine Learning  Classification tasks Expression (Contextual) Sentiment Aspect category Frameworks Weka Mallet

Baseline Classifiers  Mallet and Weka NaiveBayes MaxEnt CRF  Gram-based Uni, Bi and Trigram features Baseline ~ 10% accuracy

Expression Classification: Features  Features Polar words Punctuations Ngrams POS patterns Length ! Beginning Ontology …

Classifiers  Trees Decision Tree (J48) Functions Logistic Regression SVM Sequence Tagging CRF: Conditional Random Fields

Expression Classification: Results Have achieved 75% precision and recall for all expressions considered Factors Feature engineering Classifier selection Knowledge engineering

Contextual Sentiment  (Just) polar words can be misleading ! Polar words many not be present at all ! Combination of elements The mocha is too sweet Wait time is over an hour Aisles are too narrow Service is slow

Semantics: Ontologies  Health  Drugs  Conditions  Procedures  Symptoms  … Retail (Dining)  Food/Entrees  Service  Ambience  ….

Leverage Existing Knowledge Sources Health informatics  UMLS  NCI Thesaurus  SNOMED Retail  DMOZ Many other  Freebase  Wikipedia, DBPedia OpenData  data.gov

Knowledge Engineering Tools “Mini” ontology creation API access Freebase BioPortal Wrappers DMOZ, ….

Practical Requirements Confidence Measures Below threshold routed to manual transcription teams Polarity Snippets

Open-Source Leverage

COGNIE TM : Open Source Tools Framework UIMA Classification Weka Mallet NLP Stanford tools Indexing Lucene Databases MySQL, MongoDB Knowledge Engineering Protégé

Select Case Studies

Case Study: Health Informatics

Distillation

Case Study: Retail & Survey Analytics Feedback  Direct, device collected  Social-media Typically short, few sentences Strong requirement for aspect classification  [Food,Service,Ambience,Pricing,Other] Negative : “Immediate” vs “Long Term” classification …food was awesome, service needs improvement …. you need to be open longer !

Case Study: Risk Assessment  Biomedical Literature Abstracts Correlation direction (+ -) Subject Article type Features Clauses Negation and Triggers Semantic Heterogeneity

Performance

MapReduce Throughput can be an issue Complex language processing algorithms Large ontologies in some cases Hadoop MapReduce [Kahn and Ashish, 2014]

Conclusions

Conclusions Deeper distillation from text is important Can be achieved by Detecting and combining multiple elements in text  Feature engineering  Knowledge engineering  Classifier selection Does not have to be perfect Every domain, dataset has its nuances

thank you ! naveen.ashish@cognie.com

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