Discussion Slides for Computational Semantics Reading Group

16 December 2009

Dustin Smithdustin@media.mit.edu

Unsupervised Learning of Narrative Schema and their Participants. Nathanael

Chambers & Dan Jurafsky. ACL 2009

Objectives: Weekly, concise, no entry barriers (try to read,

but still come if you didnʼt), interdisciplinary (discussion leader should give an brief intro to the topic)

Mailing list: machinereading@mit.edu

Organization: time limits - 20 min: overview of paper, discussion questions - 40 min: group discussion

Questions: - location? time? same (wed noon e15-383), next meeting jan 6th.

- archiving, scribe? Dustin will try to make presentations available online.

Machine Reading Reading Group

Trilogy of Semantics

Linguistics has divided semantics into three parts:

1. lexical-semantics: the meaning of individual word units, located “in the lexicon” [but see Jeffery Elmanʼs CogSci 2009]

2. sentential semantics: sentence level meaning, usually event representations: semantic frames/events/predicates determined by underlying semantic theory.

3. discourse semantics: larger units of meaning, e.g., ordering of sentence-level representations.

Motivating idea: different sentence constructions refer to same underlying events,

verbs organize sentence and put constraints on arguments:

Sentential Meanings

How can we organize these events and arguments?

- Manually construct a verb+argument repository: FrameNet [Baker, Fillmore, Lowe 1998] - Cluster verb+arguments by syntactic patterns, VerbNet [Levin; Kipper-Schuler 2006] or PropBank [Kingsbury, Palmer, 2002] - Unsupervised clustering of verb+arguments [Poon, Domingos 2009]

See the Unified Verb Index: http://verbs.colorado.edu/verb-index/

John sold flowers to Susan.Susan bought flowers from John.

murder(X,Y) implies X acted intentionally and Y is dead

Gerald and Marvin sat in the front row of the Devo concert.

(a) They did not pay for their tickets.(b) They did not come on to stage until midnight.

Beyond the sentence #1: Coreference

John loved/hated Mary. They walked together down the street. A truck drove toward them.John pushed Mary [in front of the truck] [away from the truck]

Beyond the sentence #2:

(some meaning representation)

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Joan grabs a cup.

Beyond the sentence #3: Directing Inference

(some meaning representation)

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Joan grabs a cup.

- How did she grab the cup?- What objects are involved in the scene?- What will happen next?- What did Joan use to grab the cup?- Where did she grab the cup from?- Where is Joan?- Why did Joan grab the cup?- What kind of cup?- Does Joan own the cup?- What color is the cup?- How old is Joan?- Is Joan a male or female?- Is Joan doing something imitable/good?...

Beyond the sentence #3: Directing Inference

(some meaning representation)

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Joan grabs a cup.

- How did she grab the cup?- What objects are involved in the scene?- What will happen next?- What did Joan use to grab the cup?- Where did she grab the cup from?- Where is Joan?- Why did Joan grab the cup?- What kind of cup?- Does Joan own the cup?- What color is the cup?- How old is Joan?- Is Joan a male or female?- Is Joan doing something imitable/good?...

Beyond the sentence #3: Directing Inference

Given text, a reader could ask any number of questions. How do we determine which questions to ask? Use a representation that encodes relevant relations between events: e.g. plans, scripts

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Chambers & Jurafsky 08/09 Motivation

- Semantic roles involves information between sentences, not just inside single sentences. -Approach: try to learn sentence and discourse representations at the same time.

- sentence level: “typed” events: semantic roles + extensional set of arguments: eat(animal,food), animal={a,b,c} food={b,c,d} - discourse level: ordering of events, permits disjunction. pay-> eat -> clean

narratives: event sequences that all have one shared argument, called “the protagonist”

narrative schemas: a set of typed events, with ordering constraints

Narrative Schemas

- and role extensions: A = {cop, police, fuzz, pig}

- Difference from 2008 paper: addition of types for arguments of event slots.

- typed narrative chain = a partially ordered set of event slots that share an argument, (L,P,O). L: event slots, O: partial ordering, P: argument types (head words) of a single role.

L = {(hunt X), (X use), (suspect X), (accuse X), (search X)} P = {person, government, company, criminal...} O= {(hunt,use), (suspect,search), (suspect,accuse)...}

Narrative Schemas

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Chambers & Jurafsky 091. Dependency parse and resolve coreferences of NYT portion of the Gigaword Corpus (1 million articles)2. Extract typed narrative chains3. Merge chains4. Evaluate using narrative cloze (leave-one-event-out)

Results:

PMI(w1, w2) = logP (w1 and w2)P (w1)P (w2)

- PMI: an information theoretic measure of the degree of statistical dependence between two variables (e.g. two nearby words). - the amount of information we acquire about one word when we observe another.

Pointwise Mutual Information

- PMI = 0 when both words are independent.- equal to -log(p(w1)) if w1 and w2 are perfectly associated.

- PMI requires TMI?

Narrative Schema

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Selective results

Questions from discussion: Why do all schemas have only 6 steps? (arbitrary beta parameter) Why do most schemas have only 2 arguments?

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Narrative Cloze

Questions from discussion: - This graph is confusing. What is ranked position out of? Slot-filling nouns are not all equivalent (some are better substitutions than others), but ranked position does not reflect this.

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Some discussion questions1. Given some text, what determines the questions that are appropriate to ask? - Evaluating question generation before answer retrieval?

2. Approaches for learning events. - How to determine when do two verbs refer to distinct representations? - Supervised versus unsupervised approaches? Any problems learning semantic theories from many authors? - Connections with plan learning in AI? Factoring action/state space?