CS 4705 Final Review

CS4705

Julia Hirschberg

Format and Coverage

• Covers only material from <date> thru <date> (i.e. beginning with Probabilistic Parsing)

• Same format as midterm:– Short answers: 2-3 sentences– True/False: for false statements provide true

correction that is not just the negation of the false statement, e.g.

– Good answer:• The exam is on Dec 14. FALSE! The exam is on

Dec 16.

– Bad answer:• The exam is on Dec 14. FALSE! The exam is not

on Dec 14..

• Exercises• Short essays: 2 essays, 3-5 paragraphs each• The final will be only slightly longer than the

midterm, although you will have the full 3h to complete it.

Probabilistic Parsing

• Problems with CFGs:

– Rules unordered, many possible parses

• Solutions:

– Weight the rules by their probabilities

– But rules aren’t sensitive to lexical items or subcategorization frames

– Add headwords to trees

– Add subcategorization probabilities

– Add complement/adjunct distinction

– Etc.

Semantics

• Meaning Representations– Predicate/argument structure and FOPC

– Problems with mapping to NL (e.g. and ^)• Frame semantics

Having Haver: S HadThing: Car– Problems with reasoning from representation

)}(),(),()({, yCarxyHadThingxSHaverxHavingyx

Subcategorization Frames and Thematic Roles

• What patterns of arguments can different verbs take?

– NP likes NP

– NP likes Inf-VP

– NP likes NP Inf-VP

• What roles can arguments take?

– Agent, Patient, Theme (The ice melted), Experiencer (Bill likes pizza), (Bill likes pizza), Stimulus (Bill likes pizza), Goal (Bill ran to Copley Square), Recipient (Bill gave the book to Mary), Instrument (Bill ate the burrito with a plastic spork), Location (Bill sits under the tree on Wednesdays)

Selectional Restrictions

George assassinated the senator.

?The spider assassinated the fly

*Cain assassinated Able.

George broke the bank.

Lexical Semantics

• Lexemes• Lexicon• Wordnet: synsets• Framenet: subcategorization frames/verb

semantics

Word Relations

• Types of word relations– Homonymy: bank/bank– Homophones: red/read– Homographs: bass/bass– Polysemy: bank/sperm bank– Synonymy: big/large– Hyponym/hypernym: poodle/dog– Metonymy: (printing press)/the press– Meronymy: (wheel)/car– Metaphor: Nothing scares Google.

Word Sense Disambiguation

Time flies like an arrow.• Tasks: all-words vs. lexical sample• Techniques:

– Supervised, semi-supervised bootstrapping, unsupervised

– Corpora needed– Features that are useful– Competitions and Evaluation methods

• Specific approaches:– Naïve Bayes, Decision Lists, Dictionary-based,

Selectional Restrictions

Discourse Structure and Coherence

• Topic segmentation– Useful Features– Hearst’s TexTiling – how does it work?– Supervised methods – how do we evaluate?

• Coherence relations– Hobbs’– Rhetorical Structure Theory – what are it’s

problems?

Reference Terminology

• Referring expressions• Discourse referents• Anaphora and cataphora• Coreference• Antecendents• Pronouns• One-anaphora• Definite and indefinite NPs• Anaphoric chains

Constraints on Anaphoric Reference

• Salience• Recency of mention: rule of 2 sentences• Discourse structure• Agreement• Grammatical function• Repeated mention• Parallel construction• Verb semantics/thematic roles• Pragmatics

Algorithms for Coreference Resolution

• Lappin & Leas• Hobbes• Centering Theory• Supervised approaches• Evaluation

Information Extraction

• Template-based IE– Named Entity Tagging– Sequence-based relation tagging: supervised

and bootstrapping– IE for Question Answering, e.g. biographical

information (Biadsy’s `bouncing’ between Wikipedia and Google)

Information Retrieval

• Vector-Space model– Cosine similarity– TF/IDF weighting

• NIST competition retrieval tasks• Techniques for improvement• Metrics

– Precision, recall, F-measure

Question Answering

• Factoid questions• Useful Features• Answer typing• UT Dallas System

Summarization

• Types and approaches to summarization– Indicative vs. informative– Generative vs. extractive– Single vs. multi-document– Generic vs. user-focused

• Useful features• Evaluation methods• Newsblaster – how does it work?

– Multi-document– Sentence fusion and ordering– Topic tracking

MT

• Multilingual challenges– Orthography, Lexical ambiguity, morphology,

syntax• MT Approaches:

– The Pyramid– Statistical vs. Rule-based vs. Hybrid

• Evaluation metrics– Human vs. Bleu score– Criteria: fluency vs. accuracy

Dialogue

• Turns and Turn-taking• Speech Acts and Dialogue Acts• Grounding • Intentional Structure: Centering• Pragmatics

– Presupposition– Conventional Implicature– Conversational Implicature

The Final

• Dec. 16, MUDD 535, 1:10-4pm• Good luck!