Text Analytics for Semantic Computing

CARTIC RAMAKRISHNAN MEENAKSHI NAGARAJAN AMIT SHETH

We have used material from several popular books, papers, course notes and presentations made by experts in this area. We have provided all references to the best of our knowledge. This list however, serves only as a pointer to work in this area and is by no means a comprehensive resource.

KNO.E.SIS knoesis.org Director: Amit Sheth knoesis.wright.edu/amit/ Graduate Students: Meena Nagarajan knoesis.wright.edu/students/meena/ [email_address] Cartic Ramakrishnan knoesis.wright.edu/students/cartic/ [email_address]

An Overview of Empirical Natural Language Processing, Eric Brill, Raymond J. Mooney Word Sequence Syntactic Parser Parse Tree Semantic Analyzer Literal Meaning Discourse Analyzer Meaning

Traditional (Rationalist) Natural Language Processing Main insight: Using rule-based representations of knowledge and grammar (hand-coded) for language study KB Text NLP System Analysis

Empirical Natural Language Processing Main insight: Using distributional environment of a word as a tool for language study KB Text NLP System Analysis Corpus Learning System

Two approaches not incompatible. Several systems use both. Many empirical systems make use of manually created domain knowledge. Many empirical systems use representations of rationalist methods replacing hand-coded rules with rules acquired from data.

Several algorithms, methods in each task, rationalist and empiricist approaches What does a NL processing task typically entail? How do systems, applications and tasks perform these tasks? Syntax : POS Tagging, Parser Semantics : Meaning of words, using context/domain knowledge to enhance tasks

Finding more about what we already know Ex. patterns that characterize known information The search/browse OR ‘finding a needle in a haystack’ paradigm Discovering what we did not know Deriving new information from data Ex. Relationships between known entities previously unknown The ‘extracting ore from rock’ paradigm

Information Extraction - those that operate directly on the text input this includes entity, relationship and event detection Inferring new links and paths between key entities sophisticated representations for information content, beyond the "bag-of-words" representations used by IR systems Scenario detection techniques discover patterns of relationships between entities that signify some larger event, e.g. money laundering activities.

They all make use of knowledge of language (exploiting syntax and structure, different extents) Named entities begin with capital letters Morphology and meanings of words They all use some fundamental text analysis operations Pre-processing, Parsing, chunking, part-of-speech, lemmatization, tokenization To some extent, they all deal with some language understanding challenges Ambiguity, co-reference resolution, entity variations etc. Use of a core subset of theoretical models and algorithms State machines, rule systems, probabilistic models, vector-space models, classifiers, EM etc.

Analysis for both these goals have many similarities Finding entities What are we interested in knowing more about? (the known) Is what we found something of interest? (the unknown) Text is structured (to some extent) Syntax, Structure Semantics, Pragmatics, Discourse Text is noisy Pre-processing is not an option in many cases Variations not uncommon

Wikipedia like text (GOOD) “ Thomas Edison invented the light bulb.” Scientific literature (BAD) “ This MEK dependency was observed in BRAF mutant cells regardless of tissue lineage, and correlated with both downregulation of cyclin D1 protein expression and the induction of G1 arrest.” Text from Social Media (UGLY) "heylooo..ano u must hear it loadsss bu your propa faabbb!!"

Illustrate analysis of and challenges posed by these three text types throughout the tutorial

WHAT CAN TM DO FOR HARRY PORTER? A bag of words

Discovering connections hidden in text UNDISCOVERED PUBLIC KNOWLEDGE

Undiscovered Public Knowledge [Swanson] – as mentioned in [Hearst99] Search no longer enough Information overload – prohibitively large number of hits UPK increases with increasing corpus size Manual analysis very tedious Examples [Hearst99] Example 1 – Using Text to Form Hypotheses about Disease Example 2 – Using Text to Uncover Social Impact

Swanson’s discoveries Associations between Migraine and Magnesium [Hearst99] stress is associated with migraines stress can lead to loss of magnesium calcium channel blockers prevent some migraines magnesium is a natural calcium channel blocker spreading cortical depression (SCD) is implicated in some migraines high levels of magnesium inhibit SCD migraine patients have high platelet aggregability magnesium can suppress platelet aggregability

Mining popularity from Social Media Goal: Top X artists from MySpace artist comment pages Traditional Top X lists got from radio plays, cd sales. An attempt at creating a list closer to listeners preferences Mining positive, negative affect / sentiment Slang, casual text necessitates transliteration ‘ you are so bad’ == ‘you are good’

Mining text to improve existing information access mechanisms Search [Storylines] IR [QA systems] Browsing [Flamenco] Mining text for Discovery & insight [Relationship Extraction] Creation of new knowledge Ontology instance-base population Ontology schema learning

Web search – aims at optimizing for top k (~10) hits Beyond top 10 Pages expressing related latent views on topic Possible reliable sources of additional information Storylines in search results [3]

TextRunner[4] A system that uses the result of dependency parses of sentences to train a Naïve Bayes classifier for Web-scale extraction of relationships Does not require parsing for extraction – only required for training Training on features – POS tag sequences, if object is proper noun, number of tokens to right or left etc. This system is able to respond to queries like "What did Thomas Edison invent?"

Castanet [1] Semi-automatically builds faceted hierarchical metadata structures from text This is combined with Flamenco [2] to support faceted browsing of content

Documents Select terms WordNet Build core tree Augment core tree Remove top level categories Compress Tree Divide into facets

Domains used to prune applicable senses in Wordnet (e.g. “dip”) frozen dessert sundae entity substance,matter nutriment dessert ice cream sundae frozen dessert entity substance,matter nutriment dessert sherbet,sorbet sherbet sundae sherbet substance,matter nutriment dessert sherbet,sorbet frozen dessert entity ice cream sundae

Biologically active substance Lipid Disease or Syndrome affects causes affects causes complicates Fish Oils Raynaud’s Disease ??????? instance_of instance_of UMLS Semantic Network MeSH PubMed 9284 documents 4733 documents 5 documents

[Hearst92] Finding class instances [Ramakrishnan et. al. 08] [Nguyen07] Finding attribute “like” relation instances

Automatic acquisition of Class Labels Class hierarchies Attributes Relationships Constraints Rules

SYNTAX, SEMANTICS, STATISTICAL NLP, TOOLS, RESOURCES, GETTING STARTED

[hearst 97] Abstract concepts are difficult to represent “ Countless” combinations of subtle, abstract relationships among concepts Many ways to represent similar concepts E.g. space ship, flying saucer, UFO Concepts are difficult to visualize High dimensionality Tens or hundreds of thousands of features

Ambiguity (sense) Keep that smile playin’ (Smile is a track) Keep that smile on! Variations (spellings, synonyms, complex forms) Illeal Neoplasm vs. Adenomatous lesion of the Illeal wall Coreference resolution “ John wanted a copy of Netscape to run on his PC on the desk in his den; fortunately, his ISP included it in their startup package,”

[hearst 97] Highly redundant data … most of the methods count on this property Just about any simple algorithm can get “good” results for simple tasks: Pull out “important” phrases Find “meaningfully” related words Create some sort of summary from documents

Concerned with processing documents in natural language Computational Linguistics, Information Retrieval, Machine learning, Statistics, Information Theory, Data Mining etc. TM generally concerned with practical applications As opposed to lexical acquisition (for ex.)in CL

Computing Resources Faster disks, CPUs, Networked Information Data Resources Large corpora, tree banks, lexical data for training and testing systems Tools for analysis NL analysis: taggers, parsers, noun-chunkers, tokenizers; Statistical Text Analysis: classifiers, nl model generators Emphasis on applications and evaluation Practical systems experimentally evaluated on real data

TYPES, WHAT THEY DO AND DON’T

Co-occurrence based Rule / knowledge based Statistical / machine-learning based Systems typically use a combination of two or more

Look for terms and posit relationships based on co-occurrence “ A word is known by the company it keeps” Non-trivial as they deal with problems of language – expression variability, ambiguity Sometimes used as a simple baseline when evaluating more sophisticated systems

Exploit real-world knowledge About language About terms in the domain About relationship between terms in the domain About variations of terms we know of etc. Spectrum of work from using hard-coded patterns (text or linguistic) for TM to using linguistic + semantic analysis for TM Harder to develop, maintain rules; comprehensiveness also an issue

Using classifiers operating on text At pos level, n-grams, parse trees Classifying documents, words, attributes/properties Typically requires hand-tagged/labeled data Supervised, semi-supervised approaches http://compbio.uchsc.edu/Hunter_lab/Cohen/Hunter_Cohen_Molecular_Cell.pdf

Computational Linguistics - Syntax Parts of speech, morphology, phrase structure, parsing, chunking Semantics Lexical semantics, Syntax-driven semantic analysis, domain model-assisted semantic analysis (WordNet), Getting your hands dirty Text encoding, Tokenization, sentence splitting, morphology variants, lemmatization Using parsers, understanding outputs Tools, resources, frameworks

Statistical NLP Mathematical foundations, some information theory Words, Statistical Inference using n-grams, language models How are these used – some examples Collocations, Lexical Acquisition, Word sense disambiguation

Several algorithms, variations for each We wont go into each algorithm That’s a whole semester course We’ll define the problem, point out general approach, show examples More importantly, we’ll show how results of these components are used by applications we know of today

POS Tags, Taggers, Ambiguities, Examples Word Sequence Syntactic Parser Parse Tree Semantic Analyzer Literal Meaning Discourse Analyzer Meaning

Word classes, syntactic/grammatical categories, parts-of-speech Comprehensive lists used by taggers 87 in the Brown Corpus tagset 45 in the Penn Treebank tagset 146 for the C7 tagset POS Tag Description Example CC coordinating conjunction and CD cardinal number 1, third DT determiner the EX existential there there is FW foreign word d'hoevre IN preposition/subordinating conjunction in, of, like JJ adjective green JJR adjective, comparative greener JJS adjective, superlative greenest LS list marker 1) MD modal could, will NN noun, singular or mass table NNS noun plural tables NNP proper noun, singular John NNPS proper noun, plural Vikings PDT predeterminer both the boys POS possessive ending friend's PRP personal pronoun I, he, it PRP$ possessive pronoun my, his

Assigning a pos or syntactic class marker to a word in a sentence/corpus. Word classes, syntactic/grammatical categories Usually preceded by tokenization delimit sentence boundaries, tag punctuations and words. Publicly available tree banks, documents tagged for syntactic structure Typical input and output of a tagger Cancel that ticket. Cancel /VB that /DT ticket /NN ./.

Lexical ambiguity Words have multiple usages and parts-of-speech A duck in the pond ; Don’ t duck when I bowl Is duck a noun or a verb? Yes, we can ; Can of soup ; I can ned this idea Is can an auxiliary, a noun or a verb? Problem in tagging is resolving such ambiguities

Information about a word and its neighbors has implications on language models Possessive pronouns (mine, her, its) usually followed a noun Understand new words Toves did gyre and gimble. On IE Nouns as cues for named entities Adjectives as cues for subjective expressions

Useful in understanding words We can guess what a new words means by looking at words around it. Toves did gyre and gimble. Toves is something than can perform an action. (from hearst tagging)

Rule-based Database of hand-written/learned rules to resolve ambiguity -EngCG Probability / Stochastic taggers Use a training corpus to compute probability of a word taking a tag in a specific context - HMM Tagger Hybrids – transformation-based The Brill tagger A comprehensive list of available taggers http://www-nlp.stanford.edu/links/statnlp.html#Taggers

Not a complete representation EngCG based on the Constraint Grammar Approach Two step architecture Use a lexicon of words and likely pos tags to first tag words Use a large list of hand-coded disambiguation rules that assign a single pos tag for each word

Sample lexicon Word POS AdditionalPOS features Slower ADJ COMPARITIVE Show V PRESENT Show N NOMINATIVE Sample rules

What is the best possible tag given this sequence of words? Takes context into account; global Example: HMM (hidden Markov models) A special case of Bayesian Inference likely tag sequence is the one that maximizes the product of two terms: probability of sequence of tags and probability of each tag generating a word

Peter /NNP is /VBZ expected /VBN to /TO race /VB tomorrow /NN to /TO race /??? t i = argmax j P(t j |t i-1 )P(w i |t j ) P(VB|TO) × P(race|VB) Based on the Brown Corpus: Probability that you will see this POS transition and that the word will take this POS P(VB|TO) = .34 × P(race|VB) = .00003 = .00001

Be aware of possibility of ambiguities Possible one has to normalize content before sending it to the tagger Pre Post Transliteration “ Rhi you were da coolest last eve” Rhi/VB you/PRP were/VBD da/VBG coolest/JJ last/JJ eve/NN “ Rhi you were the coolest last eve” Rhi/VB you/PRP were/VBD the/DT coolest/JJ last/JJ eve/NN

Understanding Phrase Structures, Parsing, Chunking Word Sequence Syntactic Parser Parse Tree Semantic Analyzer Literal Meaning Discourse Analyzer Meaning

Words don’t just occur in some order Words are organized in phrases groupings of words that clunk together Major phrase types Noun Phrases Prepositional phrases Verb phrases

Deriving the syntactic structure of a sentence based on a language model ( grammar ) Natural Language Syntax described by a context free grammar the Start-Symbol S ≡ sentence Non-Terminals NT ≡ syntactic constituents Terminals T ≡ lexical entries/ words Productions P  NT  (NT  T) + ≡ grammar rules http://www.cs.umanitoba.ca/~comp4190/2006/NLP-Parsing.ppt

Bottom-up Parsing or data-driven Top-down Parsing or goal-driven S Aux NP VP Det Nominal Verb NP Noun Det Nominal does this flight include a meal

Natural Language Parsers, Peter Hellwig, Heidelberg Constituency Parse - Nested Phrasal Structures Dependency parse - Role Specific Structures

Tagging John/NNP bought/VBD a/DT book/NN ./. Constituency Parse Nested phrasal structure (ROOT (S (NP (NNP John)) (VP (VBD bought) (NP (DT a) (NN book))) (. .))) Typed dependencies Role specific structure nsubj(bought-2, John-1) det(book-4, a-3) dobj(bought-2, book-4)

Grammar checking: sentences that cannot be parsed may have grammatical errors Using results of Dependency parse Word sense disambiguation (dependencies as features or co-occurrence vectors)

MINIPAR http://www.cs.ualberta.ca/~lindek/minipar.htm Link Grammar parser: http://www.link.cs.cmu.edu/link/ Standard “CFG” parsers like the Stanford parser http://www-nlp.stanford.edu/software/lex-parser.shtml ENJU’s probabilistic HPSG grammar http://www-tsujii.is.s.u-tokyo.ac.jp/enju/

Some applications don’t need the complex output of a full parse Chunking / Shallow Parse / Partial Parse Identifying and classifying flat, non-overlapping contiguous units in text Segmenting and tagging Example of chunking a sentence [ NP The morning flight] from [ NP Denver] [ VP has arrived] Chunking algos mention

Entity recognition people, locations, organizations Studying linguistic patterns (Hearst 92) gave NP gave up NP in NP gave NP NP gave NP to NP

Stanford and Enju parser demos; analyzing results http://www-tsujii.is.s.u-tokyo.ac.jp/enju/demo.html http://nlp.stanford.edu:8080/parser/ If you want to know how to run it stand alone Talk to one of us or see their very helpful help pages

COLORLESS GREEN IDEAS SLEEP FURIOUSLY Word Sequence Syntactic Parser Parse Tree Semantic Analyzer Literal Meaning Discourse Analyzer Meaning

When raw linguistic inputs nor any structures derived from them will facilitate required semantic processing When we need to link linguistic information to the non-linguistic real-world knowledge Typical sources of knowledge Meaning of words, grammatical constructs, discourse, topic..

Typical sources of knowledge Meanings of words Meanings of grammatical constructs Knowledge about structure of discourse Common sense knowledge about topic Knowledge about state of affairs in which discourse is occurring

Lexical Semantics The meanings of individual words Formal Semantics (Compositional Semantics or Sentential Semantics) How those meanings combine to make meanings for individual sentences or utterances Discourse or Pragmatics How those meanings combine with each other and with other facts about various kinds of context to make meanings for a text or discourse http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

Lexeme: set of forms taken by a single word run, runs, ran and running forms of the same lexeme RUN Lemma: a particular form of a lexeme that is chosen to represent a canonical form Carpet for carpets; Sing for sing, sang, sung Lemmatization: Meaning of a word approximated by meaning of its lemma Mapping a morphological variant to its root Derivational and Inflectional Morphology

Word sense: Meaning of a word (lemma) Varies with context Significance Lexical ambiguity consequences on tasks like parsing and tagging implications on results of Machine translation, Text classification etc. Word Sense Disambiguation Selecting the correct sense for a word

The study of the way words are built up from smaller meaning units. Derivational and Inflectional morphology Forming new words from old words (derivational) Suffixes (inflections) Knowing root helps us understand new words

Porter Stemming Algorithm http://tartarus.org/~martin/PorterStemmer/ Catvar http://clipdemos.umiacs.umd.edu/catvar/ Lingsoft http://www2.lingsoft.fi/cgi-bin/engtwol?word=cmputers Wordnet http://www.shiffman.net/teaching/a2z/wordnet/

Homonymy Polysemy Synonymy Antonymy Hypernomy Hyponomy Meronomy Why do we care? http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

Homonymy: share a form, relatively unrelated senses Bank (financial institution, a sloping mound) Polysemy: semantically related Bank as a financial institution, as a blood bank Verbs tend more to polysemy

Different words/lemmas that have the same sense Couch/chair One sense more specific than the other (hyponymy) Car is a hyponym of vehicle One sense more general than the other (hypernymy) Vehicle is a hypernym of car

Meronymy Engine part of car; engine meronym of car Holonymy Car is a holonym of engine

Semantic fields Cohesive chunks of knowledge Air travel: Flight, travel, reservation, ticket, departure…

Models these sense relations A hierarchically organized lexical database On-line thesaurus + aspects of a dictionary Versions for other languages are under development http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

Verbs and Nouns in separate hierarchies http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

The set of near-synonyms for a WordNet sense is called a synset ( synonym set ) Their version of a sense or a concept Duck as a verb to mean to move (the head or body) quickly downwards or away dip, douse, hedge, fudge, evade, put off, circumvent, parry, elude, skirt, dodge, duck, sidestep

IR and QnA Indexing using similar (synonymous) words/query or specific to general words (hyponymy / hypernymy) improves text retrieval Machine translation, QnA Need to know if two words are similar to know if we can substitute one for another

Most well developed Synonymy or similarity Synonymy - a binary relationship between words, rather their senses Approaches Thesaurus based : measuring word/sense similarity in a thesaurus Distributional methods: finding other words with similar distributions in a corpus

Thesaurus based Path based similarity – two words are similar if they are similar in the thesaurus hierarchy http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

We don’t have a thesaurus for every language. Even if we do, many words are missing Wordnet: Strong for nouns, but lacking for adjectives and even verbs Expensive to build They rely on hyponym info for similarity car hyponym of vehicle Alternative - Distributional methods for word similarity http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

Firth (1957): “You shall know a word by the company it keeps!” Similar words appear in similar contexts - Nida example noted by Lin: A bottle of tezgüino is on the table Everybody likes tezgüino Tezgüino makes you drunk We make tezgüino out of corn. Partial material from http://www.stanford.edu/class/cs224u/224u.07.lec2.ppt

So you want to build your own text miner!

Infrastructure intensive Luckily, plenty of open source tools, frameworks, resources.. http://www-nlp.stanford.edu/links/statnlp.html http://www.cedar.buffalo.edu/~rohini/CSE718/References2.html

Mining opinions from casual text Data – user comments on artist pages from MySpace “ Your musics the shit,…lovve your video you are so bad” “ Your music is wicked!!!!” Goal Popularity lists generated from listener’s comments to complement radio plays/cd sales lists

“ Your musics the shit,…lovve your video you are so bad” Pre-processing strip html, normalizing text from different sources.. Tokenization Splitting text into tokens : word tokens, number tokens, domain specific requirements Sentence splitting ! . ? … ; harder in casual text Normalizing words Stop word removal, lemmatization, stemming, transliterations (da == the)

‘ The smile is so wicked!!’ Syntax : Marking sentiment expression from syntax or a dictionary The/DT smile/NN is/VBZ so/RB wicked/JJ !/. !/. Semantics : Surrounding context On Lily Allen’s MySpace page. Cues for Co-ref resolution Smile is a track by Lilly Allen. Ambiguity Background knowledge / resources Using urbandictionary.com for semantic orientation of ‘wicked’

GATE - General Architecture of Text Engineering, since 1995 at University of Sheffield, UK UIMA - Unstructured Information Management Architecture, IBM Document processing tools, Components syntactic tools, nlp tools, integrating framework

The GATE (General Architecture for Text Engineering) System: http://gate.ac.uk http://sourceforge.net/projects/gate User’s Guide: http://gate.ac.uk/sale/tao/ IBM’s UIMA (Unstructured Information Management Architecture): http://www.research.ibm.com/UIMA/ http://sourceforge.net/projects/uima-framework/ Other Resources WordNet: http://wordnet.princeton.edu/ MuNPEx: http://www.ipd.uka.de/~durm/tm/munpex/

TO COME: USAGE EXAMPLES OF WHAT WE COVERED THUS FAR

SAMPLE APPLICATIONS, SURVEY OF EFFORTS IN TWO SAMPLE AREAS

This MEK dependency was observed in BRAF mutant cells regardless of tissue lineage, and correlated with both downregulation of cyclin D1 protein expression and the induction of G1 arrest. *MEK dependency ISA Dependency_on_an_Organic_chemical *BRAF mutant cells ISA Cell_type *downregulation of cyclin D1 protein expression ISA Biological_process *tissue lineage ISA Biological_concept *induction of G1 arrest ISA Biological_process Information Extraction = segmentation+classification+association+mining Text mining = entity identification+named relationship extraction+discovering association chains…. Segmentation Classification Named Relationship Extraction MEK dependency observed in BRAF mutant cells downregulation of cyclin D1 protein expression correlated with induction of G1 arrest correlated with

MEK dependency observed in BRAF mutant cells downregulation of cyclin D1 protein expression correlated with induction of G1 arrest correlated with

The task of classifying token sequences in text into one or more predefined classes Approaches Look up a list Sliding window Use rules Machine learning Compound entities Applied to Wikipedia like text Biomedical text

The simplest approach Proper nouns make up majority of named entities Look up a gazetteer CIA fact book for organizations, country names etc. Poor recall coverage problems

Rule based [Mikheev et. Al 1999] Frequency Based "China International Trust and Investment Corp” "Suspended Ceiling Contractors Ltd” "Hughes“ when "Hughes Communications Ltd.“ is already marked as an organization Scalability issues: Expensive to create manually Leverages domain specific information – domain specific Tend to be corpus-specific – due to manual process

Machine learning approaches Ability to generalize better than rules Can capture complex patterns Requires training data Often the bottleneck Techniques [list taken from Agichtein2007 ] Naive Bayes SRV [Freitag 1998], Inductive Logic Programming Rapier [Califf and Mooney 1997] Hidden Markov Models [Leek 1997] Maximum Entropy Markov Models [McCallum et al. 2000] Conditional Random Fields [Lafferty et al. 2001]

Orthographical Features CD28 a protein Context Features Window of words Fixed Variable Part-of-speech features Current word Adjacent words – within fixed window Word shape features Kappa-B replaced with Aaaaa-A Dictionary features Inexact matches Prefixes and Suffixes “ ~ase” = protein

HMMs a powerful tool for representing sequential data are probabilistic finite state models with parameters for state-transition probabilities and state-specific observation probabilities the observation probabilities are typically represented as a multinomial distribution over a discrete, finite vocabulary of words Training is used to learn parameters that maximize the probability of the observation sequences in the training data Generative Find parameters to maximize P(X,Y) When labeling X i future observations are taken into account (forward-backward) Problems Feature overlap in NER E.g. to extract previously unseen company names from a newswire article the identity of a word alone is not very predictive knowing that the word is capitalized, that is a noun, that it is used in an appositive, and that it appears near the top of the article would all be quite predictive Would like the observations to be parameterized with these overlapping features Feature independence assumption Several features about same word can affect parameters

MEMMs [McCallum et. al, 2000] Discriminative Find parameters to maximize P(Y|X) No longer assume that features are independent f<Is-capitalized,Company>(“Apple”, Company) = 1. Do not take future observations into account (no forward-backward) Problems Label bias problem

CRFs [Lafferty et. al, 2001] Discriminative Doesn’t assume that features are independent When labeling Y i future observations are taken into account Global optimization – label bias prevented The best of both worlds!

Example [ORG U.S. ] general [PER David Petraeus ] heads for [LOC Baghdad ] . Token POS Chunk Tag --------------------------------------------------------- U.S. NNP I-NP I-ORG general NN I-NP O David NNP I-NP B-PER Petraeus NNP I-NP I-PER heads VBZ I-VP O for IN I-PP O Baghdad NNP I-NP I-LOC . . O O CONLL format – Mallet Major bottleneck is training data

Context Induction approach [Talukdar2006] Starting with a few seed entities, it is possible to induce high-precision context patterns by exploiting entity context redundancy. New entity instances of the same category can be extracted from unlabeled data with the induced patterns to create high-precision extensions of the seed lists. Features derived from token membership in the extended lists improve the accuracy of learned named-entity taggers. Pruned Extraction patterns Feature generation For CRF

Machine Learning Best performance Problem Training data bottleneck Pattern induction Reduce training data creation time

Knowledge Engineering approach Manually crafted rules Over lexical items <person> works for <organization> Over syntactic structures – parse trees GATE Machine learning approaches Supervised Semi-supervised Unsupervised

Supervised BioText – extraction of relationships between diseases and their treatments [Rosario et. al 2004] Rule-based supervised approach [Rinaldi et. al 2004] Semantics of specific relationship encoded as rules Identify a set of relations along with their morphological variants (bind, regulate, signal etc.) subj(bind,X,_,_),pobj(bind,Y,to,_) prep(Y,to,_,_) => bind(X,Y). Axiom formulation was however a manual process involving a domain expert.

Hand-coded domain specific rules that encode patterns used to extract Molecular pathways [Freidman et. al. 2001] Protein interaction [Saric et. al. 2006] All of the above in the biomedical domain Notice – specificity of relationship types Amount of effort required Also notice types of entities involved in the relationships

Semantic Role Labeling Features Detailed tutorial on SRL is available By Yih & Toutanova here

Other approaches Discovering concept-specific relations Dmitry Davidov, et. al 2007, preemptive IE approach Rosenfeld & Feldman 2007 Open Information Extraction Banko et. al 2007 Self supervised approach Uses dependency parses to train extractors On-demand information extraction Sekine 2006 IR driven Patterns discovery Paraphrase

Rule and Heuristic based method YAGO Suchanek et. al, 2007 Pattern-based approach Uses WordNet Subtree mining over dependency parse trees Nguyen et. al, 2007

Entities (MeSH terms) in sentences occur in modified forms “ adenomatous ” modifies “ hyperplasia ” “ An excessive endogenous or exogenous stimulation ” modifies “ estrogen ” Entities can also occur as composites of 2 or more other entities “ adenomatous hyperplasia ” and “ endometrium ” occur as “ adenomatous hyperplasia of the endometrium”

Relationship head Subject head Object head Object head Small set of rules over dependency types dealing with modifiers ( amod, nn ) etc. subjects, objects ( nsubj, nsubjpass ) etc. Since dependency types are arranged in a hierarchy We use this hierarchy to generalize the more specific rules There are only 4 rules in our current implementation Carroll, J., G. Minnen and E. Briscoe (1999) `Corpus annotation for parser evaluation'. In Proceedings of the EACL-99 Post-Conference Workshop on Linguistically Interpreted Corpora, Bergen, Norway. 35-41. Also in Proceedings of the ATALA Workshop on Corpus Annotés pour la Syntaxe - Treebanks, Paris, France. 13-20.

Modifiers Modified entities Composite Entities

Manual Evaluation Test if the RDF conveys same “meaning” as the sentence Juxtapose the triple with the sentence Allow user to assess correctness/incorrectness of the subject, object and triple

Discovering informative subgraphs (Harry Potter) Given a pair of end-points (entities) Produce a subgraph with relationships connecting them such that The subgraph is small enough to be visualized And contains relevant “interesting” connections We defined an interestingness measure based on the ontology schema In future biomedical domain the scientist will control this with the help of a browsable ontology Our interestingness measure takes into account Specificity of the relationships and entity classes involved Rarity of relationships etc. Cartic Ramakrishnan , William H. Milnor, Matthew Perry, Amit P. Sheth: Discovering informative connection subgraphs in multi-relational graphs. SIGKDD Explorations 7 (2): 56-63 (2005)

Two factor influencing interestingness

Bidirectional lock-step growth from S and T Choice of next node based on interestingness measure Stop when there are enough connections between the frontiers This is treated as the candidate graph

Model the Candidate graph as an electrical circuit S is the source and T the sink Edge weight derived from the ontology schema are treated as conductance values Using Ohm’s and Kirchoff’s laws we find maximum current flow paths through the candidate graph from S to T At each step adding this path to the output graph to be displayed we repeat this process till a certain number of predefined nodes is reached Results Arnold schwarzenegger, Edward Kennedy Other related work Semantic Associations

Text Mining, Analysis  understanding  utilization in decision making  knowledge discovery Entity Identification  focus change from simple to compound Relationship extraction  implicit vs. explicit Need more unsupervised approaches Need to think of incentives to evaluate

Existing corpora GENIA , BioInfer many others Narrow focus Precision and Recall Utility How useful is the extracted information? How do we measure utility? Swanson’s discovery, Enrichment of Browsing experience Text types and mining Systematically compensating for (in)formality

http://www.cs.famaf.unc.edu.ar/~laura/text_mining/ http://www.stanford.edu/class/cs276/cs276-2005-syllabus.html http://www-nlp.stanford.edu/links/statnlp.html http://www.cedar.buffalo.edu/~rohini/CSE718/References2.html

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