Semantic Roles and Disambiguation CS 4705 Today Semantic

Semantic Roles and Disambiguation CS 4705

Today • • • Semantic Networks: Wordnet Thematic Roles Selectional Restrictions Selectional Association Conceptual Dependency

Semantic Networks • Used to represent lexical relationships – e. g. Word. Net (George Miller et al) – Most widely used hierarchically organized lexical database for English – Synset: set of synonyms, a dictionary-style definition (or gloss), and some examples of uses --> a concept – Databases for nouns, verbs, and modifiers • Applications can traverse network to find synonyms, antonyms, hierarchies, . . . – Available for download or online use – http: //www. cogsci. princeton. edu/~wn

Using WN, e. g. in Question-Answering • Pasca & Harabagiu ’ 01 results on TREC corpus – Parses questions to determine question type, key words (Who invented the light bulb? ) – Person question; invent, light, bulb – The modern world is an electrified world. It might be argued that any of a number of electrical appliances deserves a place on a list of the millennium's most significant inventions. The light bulb, in particular, profoundly changed human existence by illuminating the night and making it hospitable to a wide range of human activity. The electric light, one of the everyday conveniences that most affects our lives, was invented in 1879 simultaneously by Thomas Alva Edison in the United States and Sir Joseph Wilson Swan in England. • Finding key words is not enough

• Compare expected answer ‘type’ to potential answers – For questions of type person, expect answer is person – Identify potential person names (NEs) in passages retrieved by IR – Check in WN to find which of these are hyponyms of instance? Person? • Or, Consider reformulations of question: Who invented the light bulb – For key words in query, look for WN synonyms – E. g. Who fabricated the light bulb? – Use this query for initial IR • Results: improve system accuracy by 147% (on some question types)

Thematic Roles • E w, x, y, z {Giving(x) ^ Giver(w, x) ^ Givee(z, x) ^ Given(y, x)} • A set of roles for each event: – Agent: volitional causer -- John hit Bill. – Experiencer: experiencer of event – Bill got a headache. – Force: non-volitional causer – The concrete block struck Bill on the head. – Theme/patient: most affected participant – John hit Bill. – Result: end product – Bill got a headache. – Content: proposition of propositional event – Bill thought he should take up martial arts.

– Instrument: instrument used -- John hit Bill with a bat – Beneficiary: qui bono – John hit Bill to avenge his friend – Source: origin of object of transfer event – Bill fled from New York to Timbuktu – Goal: destination of object -- Bill led from New York to Timbuktu • But there a lot of verbs, with a lot of frames… • Framenet encodes frames for many verb categories

Thematic Roles and Selectional Restrictions • Selectional restrictions: semantic constraint that a word (lexeme) imposes on the concepts that go with it George hit Bill with …. John/a gun/gusto. Jim killed his philodendron/a fly/Bill. ? His philodenron killed Jim. The flu/Misery killed Jim.

• In practical use: – Given e. g. a verb and a corpus (plus Frame. Net) – What conceptual roles are likely to accompany it? – What lexemes are likely to fill those roles? Assassinate Give Imagine Fall Serve

Disambiguation via Selectional Restrictions • “Verbs are known by the company they keep” – Different verbs select for different thematic roles wash the dishes (takes washable-thing as patient) serve delicious dishes (takes food-type as patient) • Method: another semantic attachment in grammar – Semantic attachment rules are applied as sentences are syntactically parsed VP --> V NP V serve <theme> {theme: food-type} – Selectional restriction violation: no parse

• But this means we must: – Write selectional restrictions for each sense of each predicate – or use Frame. Net • Serve alone has 15 verb senses – Hierarchical type information about each argument (using Word. Net) • How many hypernyms does dish have? • How many lexemes are hyponyms of dish? • But also: – Sometimes selectional restrictions don’t restrict enough (Which dishes do you like? ) – Sometimes they restrict too much (Eat dirt, worm! I’ll eat my hat!) • Can we take a statistical approach?

Selectional Association (Resnik ‘ 97) • Selectional Preference Strength: how much does a predicate tell us about the word class of its argument? George is a monster, George cooked a steak – SR(v): How different is p(c), the probability that any direct object will be a member of some class c, from p(c|v), the probability that a direct object of a specific verb will fall into that class? • Estimate conditional probabilities of word senses from a parsed corpus, counting how often each predicate occurs with an object argument – e. g. How likely is dish to be an object of served? Jane served/V the dish/Obj • Then estimate the strength of association between each predicate and the super-class (hypernym) of the argument in Wordnet

– E. g. For each object of serve (e. g. ragout, Mary, dish) • Look up all its hypernym classes in Word. Net (e. g dish isa piece of crockery, dish isa food item, …) • Distribute “credit” for dish (with serve) among all hypernym classes (≈sense) to which dish belongs (1/n for n classes) – Pr(c|v) is estimated at count(c, v)/count(v) – Why does this work? • Ambiguous words have many superordinate classes John served food/the dish/tuna/curry • There is a common sense among these which gets “credit” in each instance, eventually dominating the likelihood score

– How can we use this in wsd? • Choose the class (sense) of the direct object with the highest probability, given the verb Mary served the dish proudly. • Results: – Baselines: • random choice of word sense is 26. 8% • choose most frequent sense (NB: requires senselabeled training corpus) is 58. 2% – Resnik’s: 44% correct with only pred/arg relations labeled

Schank's Conceptual Dependency • Eleven predicate primitives represent all predicates – Atrans: abstract transfer of possession or ctrl from x to y – Ptrans: physical transfer of object from one place to another – Mtrans: transfer of mental concepts – Mbuild: creation of new information w/in entity – Propel, Move, Ingest, Expel, Speak, Attend

• Objects decomposed into primitive categories and modifiers • But few predicates result in very complex representations of simple things Ex, y Atrans(x) ^ Actor(x, John) ^ Object(x, Book) ^ To(x, Mary) ^ Ptrans(y) ^ Actor(y, John) ^ Object(y, Book) ^ To(y, Mary) John gave Mary a Book

Next time • Chapter 15: 5 -6 • Homework III (the last, now) assigned