74.419 Artificial Intelligence

1. 74.419 Artificial Intelligence Speech and Natural Language Processing

2. Speech and Natural Language Processing • Communication • Natural Language • Syntax • Semantics • Pragmatics • Speech

3. Evolution of Human Language • communication for "work" • social interaction • basis of cognition and thinking (Whorff & Saphir)

4. Communication "Communication is the intentional exchange of information brought about by the production and perception of signs drawn from a shared system of conventional signs." [Russell & Norvig, p.651]

5. Natural Language - General • Natural Language is characterized by • a common or shared set of signsalphabeth; lexicon • a systematic procedure to produce combinations of signs syntax • a shared meaning of signs and combinations of signs (constructive) semantics

6. Speech and Natural Language • Speech Recognition • acoustic signal as input • conversion into phonemes and written words • Natural Language Processing • written text as input; sentences (or 'utterances') • syntactic analysis: parsing; grammar • semantic analysis: "meaning", semantic representation • pragmatics; • dialogue; discourse • Spoken Language Processing • transcribed utterances • Phenomena of spontaneous speech

7. Speech Recognition Acoustic / sound wave Filtering, FFT; Spectral Analysis Frequency Spectrum Features (Phonemes; Context) Signal Processing / Analysis Phoneme Recognition: HMM, Neural Networks Phonemes Grammar or Statistics Phoneme Sequences / Words Grammar or Statistics for likely word sequences Word Sequence / Sentence

8. Areas in Natural Language Processing • Morphology (word stem + ending) • Syntax, Grammar & Parsing (syntactic description & analysis) • Semantics & Pragmatics (meaning; constructive; context-dependent; references; ambiguity) • Pragmatic Theory of Language; Intentions; Metaphor (Communication as Action) • Discourse / Dialogue / Text • Spoken Language Understanding • Language Learning

9. NLP Syntax Analysis - Processes Part-of-Speech (POS) Tagging Morphological Analyzer Parser Grammar Rules Lexicon thethe – determinerDetNP → Det NounNP recognized NP Det Noun parse tree Linguistic Background Knowledge

10. NLP - Syntactic Analysis Part-of-Speech (POS) Tagging Morphological Analyzer Parser Grammar Rules Lexicon eat + s eat – verbVerbVP → Verb NounVP recognized 3rd singVP Verb Noun parse tree

11. Morphology A morphological analyzer determines (at least) • the stem + ending of a word, and usually delivers related information, like • the word class, • the number, • the person and • the case of the word. The morphology can be part of the lexicon or implemented as a single component, for example as a rule-based system. eatseat + sverb,singular,3rd pers dogdog noun, singular

12. Lexicon The Lexiconcontains information on words, as • inflected forms (e.g. goes, eats) or • word-stems (e.g. go, eat). The Lexicon usually assigns a syntactic category, • the word class or Part-of-Speech category Sometimes also • further syntactic information (see Morphology); • semantic information (e.g. agent); • syntactic-semantic information (e.g. verb complements like: 'give' requires a direct object).

13. Example contents: eats verb; singular, 3rd person (-s); can have direct object (verb subcategorization) dog dog, noun, singular; animal (semantic annotation) Lexicon

14. POS (Part-of-Speech) Tagging POS Tagging determines the word class or ‘part-of-speech’ category (basic syntactic categories) of single words or word-stems. The det (determiner) dog noun eats verb (3rd person; singular) the det bone noun

15. Open Word Class: Nouns Nouns denote objects, concepts, … Proper Nouns Names for specific individual objects, entities e.g. the Eiffel Tower, Dr. Kemke Common Nouns Names for categories or classes or abstracts e.g. fruit, banana, table, freedom, sleep, ... Count Nouns enumerable entities, e.g. two bananas Mass Nouns not countable items, e.g. water, salt, freedom

16. Open Word Class: Verbs Verbs denote actions, processes, states e.g. smoke, dream, rest, run Several morphological forms e.g. non-3rd person - eat 3rd person - eats progressive/ - eating present participle/ gerundive past participle - eaten Auxiliaries, e.g. be, as sub-class of verbs

17. Open Word Class: Adjectives Adjectives denote qualities or properties of objects, e.g. heavy, blue, content most languages have concepts for colour - white, green, ... age - young, old, ... value - good, bad, ... not all languages have adjectives as separate class

18. Open Word Class: Adverbs Adverbs denote modifications of actions (verbs), qualities (adjectives) e.g. walk slowly,heavily drunk Directional or Locational Adverbs Specify direction or location e.g. go home, stay here Degree Adverbs Specify extent of process, action, property e.g. extremely slow, very modest

19. Open Word Class: Adverbs 2 Manner Adverbs Specify manner of action or process e.g. walk slowly, run fast Temporal Adverbs Specify time of event or action e.g. yesterday, Monday

20. Closed Word Classes prepositions: on, under, over, at, from, to, with, ... determiners: a, an, the, ... pronouns: he, she, it, his, her, who, I, ... conjunctions: and, or, as, if, when, ... auxiliary verbs: can, may, should, are particles: up, down, on, off, in, out, numerals:one, two, three, ..., first, second, ...

21. Language and Grammar • Natural Language described as Formal LanguageL using a Formal Grammar G: • start-symbol S ≡sentence • non-terminals NT ≡syntactic constituents • terminals T ≡lexical entries/ words • production rules P ≡grammar rules • Generate sentences or recognize sentences (Parsing) of the language L through the application of grammar rules.

22. Grammar Here, POS Tags are included in the grammar rules. det the noun dog | bone verb  eat NPdet noun (NPnoun phrase) VPverb (VPverb phrase) VP  verb NP S NP VP (Ssentence) Most often we deal with Context-free Grammars, with a distinguished Start-symbol S (sentence).

23. Parsing • Parsing • derive the syntactic structure of a sentence based on a language model (grammar) • construct a parse tree, i.e. the derivation of the sentence based on the grammar (rewrite system)

24. Parsing (here: bottom-up) determine the syntactic structure of the sentence the det dog noun det noun  NP eats verb the det bone noun det noun  NP verb NP  VP NP VP S

25. Sample Grammar • Grammar (S, NT, T, P) - NT Non-Terminal; T Terminals; P Productions • Sentence Symbol S NT • Word-Classes/Part-of-Speech NT • syntactic Constituents NT • terminal words  NT • Grammar Rules P  NT  (NT  T)* • S→NP VP | Aux NP VP • NP→DetNominal|Proper-Noun • Nominal→Noun| Nominal PP • VP→Verb |VerbNP |VerbPP | VerbNP PP • PP→PrepNP • Det→ that | this | a • Noun→book | flight | meal | money • Proper-Noun→ Houston | American Airlines | TWA • Verb→ book | include | prefer • Prep→ from | to | on • Auc→ do | does

26. Sample Parse Tree • Parse"Does this flight include a meal?" • S • Aux NP VP • Det Nominal Verb NP • Noun Det Nominal • does this flight include a meal

27. Bottom-up vs. Top-Down Parsing • Bottom-up – from word-nodes to sentence-symbol • Top-down Parsing – from sentence-symbol to words • S • Aux NP VP • Det Nominal Verb NP • Noun Det Nominal • does this flight include a meal

28. Ambiguity “One morning, I shot an elephant in my pajamas. How he got into my pajamas, I don’t know.” Groucho Marx syntactical or structural ambiguity – several parse trees example: above sentence semantic or lexical ambiguity – several word meanings bank (where you get money) and (river) bank even different word categories possible (interim) He books the flight. vs. The books are here. Fruit flies from the balcony vs. Fruit flies are on the balcony.

29. Lexical Ambiguity Several word senses or word categories e.g. chase – noun or verb e.g. plant - ????

30. Syntactic Ambiguity Several parse trees e.g. “The dog eats the bone in the park.” e.g. “The dog eats the bone in the package.” Who/what is in the park and who/what is in the package? Syntactically speaking: How do I bind the Prepositional Phrase "in the ... " ?

31. Problems in Parsing Problems with left-recursive rules like NP → NP PP: don’t know how many times recursion is needed. Pure Bottom-up or Top-down Parsing is inefficient because it generates and explores too many structures which in the end turn out to be invalid. Combine top-down and bottom-up approach: Start with sentence; use rules top-down (look-ahead); read input; try to find shortest path from input to highest unparsed constituent (from left to right). →Chart-Parsing / Earley-Parser

32. Chart-Parsing / Early Algorithm Essence: • Integrate top-down and bottom-up parsing. • Keep recognized sub-structures (sub-trees) for shared use during parsing. Top-down Prediction: Start with S-symbol. Generate all applicable rules for S. Go further down with left-most constituent in rules and add rules for these constituents until you encounter a left-most node on the RHS which is a word category (POS). Bottom-up Completion: Read input word and compare. If word matches, mark as recognized and continue the recognition bottom-up, trying to complete active rules.

33. Earley Algorithm - Functions predictor generates new rules for partly recognized RHS with constituent right of • (top-down generation); • indicates how far a rule has been recognized scanner if word category (POS) is found right of the • , the Scanner reads the next input word and adds a rule for it to the chart (bottom-up mode) completer if rule is completely recognized (the • is far right), the recognition state of earlier rules in the chart advances: the • is moved over the recognized constituent (bottom-up recognition).

34. Chart S  VP . VP V NP. NP Det Nom . Nom  Noun . Det V Noun Bookthisflight

36. Semantics

37. Semantic Representation Representation of the meaning of a sentence. Generate • a logic-based representation or • aframe-based representation based on the syntactic structure, lexical entries, and particularly the head-verb (determines how to arrange parts of the sentence in the semantic representation).

38. Semantic Representation Verb-centered Representation Verb (action, head) is regarded as center of verbal expression and determines the case frame with possible case roles; other parts of the sentence are described in relation to the action as fillers of case slots. (cf. also Schank’s CD Theory) Typing of case roles possible (e.g. 'agent' refers to a specific sort or concept)

39. General Frame for "eat" Agent: animate Action: eat Patiens: food Manner: {e.g. fast} Location: {e.g. in the yard} Time: {e.g. at noon}

40. Example-Frame with Fillers Agent: the dog Action: eat Patiens: the bone / the bone in the package Location: in the park

41. General Frame for driveFrame with fillers Agent: animate Agent: she Action: drive Action: drives Patiens: vehicle Patiens: the convertible Manner:{the way it is done} Manner: fast Location: Location-spec Location: [in the] Rocky Mountains Source: Location-spec Source: [from] home Destination: Location-spec Destination: [to the] ASIC conference Time: Time-spec Time: [in the] summer holidays

42. Representation in Logic Action: eat Agent: the dog Patiens: the bone / the bone in the package Location: in the park predicate eat (dog-1, bone-1, park-1) constants

43. Representationin Logic eat (dog-1, bone-1, park-1) lexical variables general eat ( x, y, z ) syntactic eat ( NP-1, NP-2, PP ) animate-being (x) food (y) location (z) NP-1 (x) NP-2 (y) PP (z) semantic frame syntactic frame

44. Pragmatics

45. Pragmatics Pragmatics includes context-related aspects of NL expressions (utterances). These are in particular anaphoric references, elliptic expressions, deictic expressions, … anaphoric references – refer to items mentioned before deictic expressions – simulate pointing gestures elliptic expressions – incomplete expression; relate to item mentioned before

46. Pragmatics “I put the box on the top shelve.” “I know that. But I can’t find it there.” deictic expression anaphoric reference “The candy-box?” elliptic expression

47. Intentions Intentions One philosophical assumption is that natural language is used to achieve things or situations: “Do things with words.” The meaning of an utterance is essentially determined by the intention of the speaker.

48. Intentionality - Examples What was said:What was meant: “There is a terrible "Can you please draft here.” close the window." “How does it look "I am really mad; here?” clean up your room." "Will this ever end?" "I would prefer to be with my friends than to sit in class now."

49. Metaphors Metaphors The meaning of a sentence or expression is not directly inferable from the sentence structure and the word meanings. Metaphors transfer concepts and relations from one area of discourse into another area, for example, seeing time as line (in space) or seing friendship or life as a journey.

50. Metaphors - Examples “This car eats a lot of gas.” “She devoured the book.” “He was tied up with his clients.” “Marriage is like a journey.” “Their marriage was a one-way road into hell.” (see George Lakoff, Women, Fire and Dangerous Things)