NLP — semantics

1. NLP — semantics • Points • Semantic analysis • Semantic markers • Case analysis • Syntactic patterns • Case lists • An algorithm • Quantifier scope • A taste of discourse analysis • A look at pragmatics

2. Semantic analysis • Semantic analysis may follow parsing: map a parse tree (a syntactic structure) into a representation of meaning (a knowledge structure). • Semantics resides at both sides of parsing, and elements of meaning come from words. Lexical knowledge lives in dictionaries. It has two forms. • Morphological and syntactic information about the word: part-of-speech (class), number, case, gender, tense, requirements (for verbs), and so on. • Semantic information about the word, for example, a semantic marker that locates — in a hierarchy of concepts — the concept that the word denotes.

3. Semantic markers Suppose that a dictionary entry contains both syntactic and semantic information. (Verb patterns would be unused for other classes.) For example: lexicon( Word, Class, SyntCategories, Root, VerbPattern, Semantics). The word “ball” could have at least these two entries: lexicon( ball, verb, [inf, pres], ball, trans, [makeBall]). lexicon( ball, noun, [sg], ball, _unused, [sportsEquipment, dance]).

4. event physical object ... social event natural event ... ... ... artifact natural object ... ... charity entertainment equipment tool ... ... dance sports equipment Semantic markers (2) Here is a place for the noun senses of “ball” (semantic markers) in a possible hierarchy:

5. Case analysis It is one of many methods of semantic analysis, based on familiar ideas: recognize a general situation (denoted by a verb) and roles in this situation (denoted usually by noun phrases). Examples of syntactic verb patterns intransitive(subject, verb)Jim laughed. transitive(subject, verb, object) Jim found a penny. bitransitive(subject, verb, indirect object, object)Jim gave a penny to Jill. to-inf(subject, verb, infinitive clause) Jim wanted to laugh. object + to-inf(subject, verb, object, infinitive clause)Jim wanted Jill to laugh. for-object + to-inf(subject, verb, for object, infinitive clause)Jim waited for Jill to laugh.

6. Case analysis (2) Examples of semantic verb patterns Agent(if the subject is animate then the subject → Agent) laughed( Jim ) Agent + Object(if the subject is animate thenthe subject → Agent, the object → Object) found( Jim, penny ) Agent + Object + Beneficiaryif the subject is animate then the subject → Agent;if the indirect object is animate then the indirect object → Beneficiary, the object → Object) gave( Jim, penny, Jill )

7. Case analysis (3) Another example of semantic verb patterns Agent + Content(if subject is animate then subject → Agent;subordinate sentence → Content) wanted( Jim,  “Jim laugh” ) wanted( Jim,  “Jill laugh” ) We need some form of a pointer to the semantic structure for the embedded sentence. [Recall the “boxed” propositions in the conceptual graph notation.]

8. Case analysis (4) Lists of cases used in NLP systems have usually more than a few elements. Here is an example. Participant cases Accompaniment, Agent, Beneficiary, Exclusion, Experiencer, Instrument, Object, Recipient Causality cases Cause, Effect, Opposition, Purpose Spatial cases Direction, LocationAt, LocationFrom, LocationTo, LocationThrough, Orientation, Order Temporal cases Frequency, TimeAt, TimeFrom, TimeThrough, TimeTo Quality cases Content, Manner, Material, Measure

9. Case analysis (5) A case marker is a syntactic element that signals the presence of a case. A preposition (in, at, from, of, for, ...) may mark cases. A position of a noun phrase (subject, direct object, indirect object) also marks case. Subject AgentJim hit the ball. ExperiencerJim grew hungry as time passed. InstrumentThe ball broke the window. CauseThe wind broke the window with a branch. Indirect object RecipientI threw the dog a ball. BeneficiaryI wrote her a reference letter to her boss. Direct object ObjectJohn hits the ball.

10. Case analysis (6) A few examples of markers that mark exactly one case. LocationThroughWe walked aroundthe courtyard. MannerShe acted asmy agent last year. LocationAtSit besideme. ExclusionEveryone was pleased excepther. OppositionThey persisted despitemy warning. TimeFromHe has been sick sincethe accident. TimeToWe worked tilldawn.

11. Case analysis (7) Examples of markers that mark many cases: at, for. DirectionThe deer ran right atthe hunters. LocationAtI stood atthe door. TimeAtThe case will be heard atnoon. MannerThe car moves athigh speed. ContentShe is good atarts. MeasureIt stopped atfifty. CauseShe was amazed athis insolence. LocationToAim forthe heart. DirectionRun forthe train. ContentI stand forsocial responsibility. TimeThroughThey worked forthree hours. BeneficiaryI'd walk a mile forthem. PurposeThis drug is forpeople with a flu. MeasureSell it forfifty dollars. CauseHe received a medal forcourage. RecipientThis mail is foreveryone. TimeAtCall him forten o'clock.

12. Case analysis (8) An algorithm of case analysis In the parse tree, identify all case markers. Find case patterns of the main verb (assume a knowledge base of patterns!). Apply rules — based on lexical, syntactic and semantic features — to match case markers with cases. Examples of rules [see slides 6-7 for more] active sentence, animate subject: subject → Agent Jim laughed. passive sentence, inanimate subject: subject → Object The window was broken. passive sentence, animate subject: subject → Experiencer Jim was detained.

13. Quantifier scoping • Every author wrote a book. • ab author(a)  book(b)  wrote(a, b) • skolemize: a author(a)  book(s(a))  wrote(a, s(a)) • ba author(a)  book(b)  wrote(a, b) • skolemize: a author(a)  book(B0)  wrote(a, B0) • Only one scoping is correct: which one? • The man picked up all papers. • THEmp man(m)  paper(p)  pickedUp(m, p) • pTHEm man(m)  paper(p)  pickedUp(m, p) • A simple algorithm: fixed precedence, for example, • the > each > what, who, whom > every, all, some, a • But: there is no universally approved, objective ordering.

14. A taste of discourse analysis • Text units beyond sentences — examples • A story (such as a fairy tale, a drama, ...). • A news item. • Dialogue. • Technical text (manual, textbook, documentation). • A document in a document base (abstract, patent description, ...). • Links between sentences/phrases in a larger text • Textual ordering. • Temporal link (for example, an event precedes another event). • Jim saw the bus. He ran to catch it. • “saw” precedes “ran”

15. ... discourse analysis (2) • Causal link (for example, reason, effect, prerequisite). • Jim saw the bus pull away. He waved to the driver. • “waved” could be an effect of “saw” • Coreference: linking references to the same entity. • Jim bought a book. He liked it a lot. • “he” = Jim, 'it' = book (and “bought” precedes “liked”) • Jim bought a book. The price was good. • price is a property of books (and it enables buying) • Jim bought a book. He paid $10. • paying is an element of (is included in) buying • Jim bought a book. The dust-jacket was red. • dust-jackets are parts of books

16. A look at pragmatics • Focus • Here is one tiny example from a hypothetical NLP interface to an airline reservation system: • I want to fly to Vancouver tomorrow night. • There is a flight at 6. • When does it arrive? • At 8 local time. • Is it WestJet? • No, Air Canada. • Show me others. ← shift of focus • Modelling beliefs: who knows what, who believes what. • This can be done formally, in advanced forms of logic, for example in autoepistemic logic(check it out).

17. hungry needs food buys food at restaurant at market fast food formal deli burgers ... pragmatics (2) • Plan-based understanding • We can use scripts(see textbook, section 7.1.4). • Jim was hungry. He stopped at Nate’s deli. • A possible line of reasoning: Scripts (and other similar representations of plans) help fill gaps in the story.

18. ... pragmatics (3) • Speech acts • assert—inform—explain; • ask if—ask what; • order—request. • Indirect speech acts • The form disagrees with the intention: a question (interrogative) or a statement (declarative) really means something different. • Could you pass the salt? a request • Do you know that it’s raining? information • Honey, Fido needs a shower. a command ... time out — and there is still so much to tell...