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Dialogue and Discourse analysis (some slides borrowed from D. Jurafski and from S. Ponzetto)

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1 Dialogue and Discourse analysis (some slides borrowed from D. Jurafski and from S. Ponzetto)

2 Natural Language Processing: levels of representation Morphology Syntax Semantics Pragmatics Words IR IE QA D & D

3 Meaning in context Processing flow of D&D Processing Sound waves ASR Words Syntactic processing Parses Semantic processing Meaning Discourse/ Dialogue processing

4 Discourse/dialogue analysis So far we always analyzed one sentence in isolation, syntactically and/or semantically Natural languages are spoken or written as a collection of sentences In general, a sentence or utterance cannot be understood in isolation.

5 A dialog example Xu and Rudnicky (2000)

6 A discourse example John went to the bank to deposit his paycheck. He then took a train to Bill’s car dealership. He needed to buy a car. The company he works for now isn’t near any public transportation. He also wanted to talk to Bill about their softball league.

7 Issues in discourse/dialogue Dialogue – Turn-taking – Speech act – Grounding – Dialogue management Discourse – Segmentation, coherence relations Both – Anaphora – Co-reference

8 1. Turn-taking Dialogue is characterized by turn-taking. – A: – B: – A: – B: – … Resource allocation problem: – How do speakers know when to take the floor?

9 Turn-taking rules Sacks et al. (1974) At each transition-relevance place of each turn: – a. If during this turn the current speaker has selected B as the next speaker then B must speak next. – b. If the current speaker does not select the next speaker, any other speaker may take the next turn. – c. If no one else takes the next turn, the current speaker may take the next turn. BASICALLY, TURN-TAKING REQUIRES TO DEFINE A PROTOCOL BASICALLY, TURN-TAKING REQUIRES TO DEFINE A PROTOCOL

10 Issues in discourse/dialogue Dialogue – Turn-taking – Speech act – Grounding – Dialogue management Discourse – Segmentation, coherence relations Both – Anaphora – Co-reference

11 2. Speech Acts Speaker’s contribute more information than just “what is said” Speech Acts can give a principled account of additional meaning Speech Act Theory can also help us examine utterances from the perspective of their function, rather than their form

12 Classification of SA according to “force” Locutionary Force (what is said) – “Bring the chair to the dining room” Illocutionary Force (what is done) – The robot is asked to grasp a chair and change his current position Perlocutionary Force (the effect) – The current position of the robot&chair changes to “dining room” (if action is successfully performed)

13 The 3 levels of act revisited Locutionary Force Illocutionary Force Perlocutionary Force Can I have the rest of your sandwich? Or Are you going to finish that? InterrogativeStop eating the sandwich Effect: You give me sandwich (or you are amused by my quoting from “Diner”) (or etc) I want the rest of your sandwich DeclarativeStop eating the sandwich Effect: as above Give me your sandwich! DirectiveStop eating the sandwich Effect: as above.

14 Types of Speech acts (more fine- grained) Commissives (Affect Speaker, Subjective) TYPES: Oath, Offer, Promise – I promise you a new book Declaratives (Change the Macrocosmic Social World) TYPES: Baptism, Marriage – I will spend my vacations in Sardinia Directives (Change the Microcosmic Social World) TYPES: Command, Request – Move the chair near the table Expressives (Feelings of Speaker) TYPES: Apology, Thanks – Sorry, I did not understood correctly (Mey 120, Searle 1977, 34)

15 Types of Speech acts (more fine- grained) Interrogatives (Hearer Knows Best) TYPES: Closed (yes-no questions, list), Open (who? when?..) – Do you see the coffe maker somewhere in the kitchen? Imperatives (Directives) (Affect Hearer) TYPES: Request, Requirement, Threat, Warning – Go to the dining room Performatives (Affect world) TYPES: Agreement, Appointment, Baptism, Declaration of Independence, Dedication, Marriage – You are right! Representatives (Objective Descriptive Statements) TYPES: Statement that is either True or False – The coffee maker is on the table

16 Use of speech acts in dialogue: speech-act frames (Frost et al. 2010) “Go to the corner office by the lobby,”

17 Issues in discourse/dialogue Dialogue – Turn-taking – Speech act – Grounding – Dialogue management Discourse – Segmentation, coherence relations Both – Anaphora – Co-reference

18 3. Grounding Why do elevator buttons light up? Clark (1996) (after Norman 1988) – Principle of closure. Agents performing an action require evidence, sufficient for current purposes, that they have succeeded in performing it What is the “linguistic correlate” of this?

19 Grounding Need to know whether an action succeeded or failed Dialogue is also an action – a collective action performed by speaker and hearer – Common ground: set of things mutually believed by both speaker and hearer Need to achieve common ground, so hearer must ground or acknowledge speakers utterance.

20 How do speakers ground? Clark and Schaefer Continued attention: – B continues attending to A Relevant next contribution: – B starts in on next relevant contribution Acknowledgement: – B nods or says continuer like uh-huh, yeah, assessment (great!) Demonstration: – B demonstrates understanding A by paraphrasing or reformulating A’s contribution, or by collaboratively completing A’s utterance Display: – B displays verbatim all or part of A’s presentation

21 A human-human conversation

22 Grounding examples Display: – C: I need to travel in May – A: And, what day in May did you want to travel? Acknowledgement – C: He wants to fly from Boston – A: mm-hmm – C: to Baltimore Washington International – [Mm-hmm (usually transcribed “uh-huh”) is a backchannel, continuer, or acknowledgement token]

23 Grounding Examples (2) Acknowledgement + next relevant contribution – And, what day in May did you want to travel? – And you’re flying into what city? – And what time would you like to leave? The and indicates to the client that agent has successfully understood answer to the last question.

24 Grounding negative responses From Cohen et al. (2004) System: Did you want to review some more of your personal profile? Caller: No. System: Okay, what’s next? System: Did you want to review some more of your personal profile? Caller: No. System: What’s next?

25 Grounding and Dialogue Systems Grounding is not just a tidbit about humans Is key to design of conversational agent Why? – HCI researchers find users of speech-based interfaces are confused when system doesn’t give them an explicit acknowledgement signal – Stifelman et al. (1993), Yankelovich et al. (1995)

26 Example

27 Issues in discourse/dialogue Dialogue – Turn-taking – Speech act – Grounding – Dialogue management Discourse – Segmentation, coherence relations Both – Anaphora – Co-reference

28 Dialogue management A dialogue system is finalized to some purpose (e.g. a flight reservation) Unlike for discourse analysis, a structure must be determined a-priori to guide the conversation Dialogue manager: “forces” the dialogue between user and system to follow one or more structures For speech dialogue systems, most common approaches are: – Finite state dialogue manager – Frame and slot semantics – Agent-based dialogue manager

29 Finite-state dialogue managers System completely controls the conversation with the user. It asks the user a series of questions Ignoring (or misinterpreting) anything the user says that is not a direct answer to the system’s questions

30 Finite State Dialogue Manager

31 System forces the user to follow the structure

32 Finite-state approach Pros – simple to write – very robust and quick Cons – System direct entire conversation – User actions very limited

33 33 Frame-based Approach Frame-based system –Asks the user questions to fill slots in a template in order to perform a task (form-filling task) –Permits the user to respond more flexibly to the system’s prompts (see Example 2) –Recognizes the main concepts in the user’s utterance Example 1) System: What is your destination? User: London. System: What day do you want to travel? User: Friday Example 2) System: What is your destination? User: London on Friday around 10 in the morning. System: I have the following connection …

34 Frame/Slot semantics Show me morning flights from Boston to SF on Tuesday. SHOW: FLIGHTS: ORIGIN: CITY: Boston DATE: Tuesday TIME: morning DEST: CITY: San Francisco

35 Frame/slot Semantics (multiple sentences) – SlotQuestion – IDENTIFYWhat is your name? – ORIGINWhat city are you leaving from? – DEST Where are you going? – DEPT DATEWhat day would you like to leave? – DEPT TIMEWhat time would you like to leave? – AIRLINEWhat is your preferred airline? Use the structure of the frame itself to guide dialogue

36 36 Advantages –The ability to use natural language, multiple slot filling –The system processes the user’s over- informative answers and corrections Disadvantages –Appropriate for well-defined tasks in which the system takes the initiative in the dialog –Difficult to predict which rule is likely to fire in a particular context Frame-based approaches

37 37 Properties –Complex communication using unrestricted natural language –Mixed-Initiative –Co-operative problem solving –Theorem proving, planning, distributed architectures –Conversational agents Examples User : I’m looking for a job in the Calais area. Are there any servers? System : No, there aren’t any employment servers for Calais. However, there is an employment server for Pasde-Calais and an employment server for Lille. Are you interested in one of these?  System attempts to provide a more co-operative response that might address the user’s needs. Agent-based approaches

38 38 Agent-based Approach Advantages –Suitable to more complex dialogues –Mixed-initiative dialogues Disadvantages –Much more complex resources and processing –Sophisticated natural language capabilities –Complicated communication between dialogue modules

39 CMU-systems (Olympus)

40 Issues in discourse/dialogue Dialogue – Turn-taking – Speech act – Grounding – Dialogue management Discourse – Segmentation – coherence relations Both – Anaphora – Co-reference

41 Discourse segmentation Separating a document into a linear sequence of subtopics – For example: scientific articles are segmented into Abstract, Introduction, Methods, Results, Conclusions – Note: this is a simplification – a discourse might have a more complex structure Applications: – Summarization: summarize each segment separately – Information Retrieval / Information Extraction: Apply to an appropriate, i.e. relevant segment

42 Discourse segmentation Example: 21 paragraph article called Stargazers Source: Hearst (1997)

43 Unsupervised Discourse Segmentation Unsupervised = uses no training data Typically cohesion-based: segment text into subtopics in which sentences/paragraphs are cohesive with each other Cohesion: use of linguistic devices to establish links between textual units Lexical Cohesion: use of same or similar (e.g. hypernyms, hyponyms, etc.) words to link text units

44 Textiling (Hearst, 1997) An unsupervised, cohesion based algorithm – compare adjacent blocks of text – look for shifts in vocabulary Three main steps – Tokenization – Lexical score determination – Boundary identification

45 Textiling (Hearst, 1997) sentence numbersterm frequency

46 TextTiling: Pre-processing Convert text stream into terms (words) Remove stop-words – E.g. “the”, “a”, “of” … Reduce each word to its root form (inflectional morphology) – Nouns: singular to plural (sports -> sport) – Verbs: inflected to base form (coming -> come) Divide text into token sequences (pseudo-sentences) of equal length (say 20 words)

47 TextTiling: Pre-processing Convert text stream into terms (words) Remove stop-words – E.g. “the”, “a”, “of” … Reduce each word to its root form (inflectional morphology) – Nouns: singular to plural (sports -> sport) – Verbs: inflected to base form (coming -> come) Divide text into token sequences (pseudo-sentences) of equal length (say 20 words)

48 TextTiling: Lexical score determination Compute lexical cohesion score at each gap – Similarity of the blocks before and after the gap – Each block is made of k pseudo-sentences – Cosine similarity between the blocks’ word vectors Gap

49 TextTiling: Lexical score determination Compute lexical cohesion score at each gap – Similarity of the blocks before and after the gap – Each block is made of k pseudo-sentences – Cosine similarity between the blocks’ word vectors Gap Similarity

50 TextTiling: Boundary identification Compute the depth scores of each gap – Distance from the peaks on both sides: (a-b)+(c-b) Assign segmentation if the depth score is larger than a boundary cutoff (e.g. avg-sd) a b c valley

51 TextTiling: Boundary identification Compute the depth scores of each gap – Distance from the peaks on both sides: (a-b)+(c-b) Assign segmentation if the depth score is larger than a boundary cutoff (e.g. avg-sd)

52 Textiling (Hearst, 1997) Source: Hearst (1994)

53 Supervised Discourse Segmentation Supervised = uses training labeled data Easy to get labeled data for some segmentation tasks – Paragraph segmentation: use tags from Web docs Model as a (binary) classification task … – Classify if the sentence boundary is a paragraph boundary – Use any classifier, e.g. SVM, Naïve Bayes, Maximum Entropy etc.

54 Features: – Use cohesion features: word overlap, word cosine similarity, etc. – Additional features: discourse markers or cue word Discourse marker or cue word: a word or phrase that signal discourse structure – “good evening”, “joining us now” in at the beginning of broadcast news; “coming up next” at the end of a segment, – Tend to be domain-specific – “company Incorporated” at the beginning of a segment (WSJ) Either hand-code or automatically determine by feature selection Supervised Discourse Segmentation

55 Issues in discourse/dialogue Dialogue – Turn-taking – Speech act – Grounding – Dialogue management Discourse – Segmentation – coherence relations Both – Anaphora – Co-reference

56 Text Coherence A collection of independent sentences do not make a discourse because they lack coherence – Meaning relation between two units of text – how the meaning of different units of text combine to build meaning of the larger unit John hid Bill’s car keys. He was drunk. John hid Bill’s car keys. He likes spinach. Explanation ???

57 Coherence Relations Other relations (Hobbs, 1979): The Tin Woodman was caught in the rain. His joints rusted. The scarecrow wanted some brains. The Tin Woodman wanted a heart. Dorothy was from Kansas. She lived in the midst of the great Kansas prairies. Dorothy picked up the oil-can. She oiled the Tin Woodman’s joints. Result Parallel Elaboration Occasion

58 Discourse Structure The hierarchical structure of a discourse according to the coherence relations Analogous to syntactic tree structure A node in a tree represents locally coherent sentences: discourse segment

59 Discourse Structure: example S1: John went to the bank to deposit his paycheck. S2: He then took a train to Bill’s car dealership. S3: He needed to buy a car. S4: The company he works for now isn’t near any public transportation. S5: He also wanted to talk to Bill about their softball league.

60 Discourse Structure: example

61 Source: Marcu (2000a)

62 Rhetorical Structure Theory (RST) (Mann, Matthiessen, and Thompson ‘89) One theory of discourse structure, based on identifying relations between parts of the text – Identify clauses and clause-like units that are unequivocally the nucleus or satellite of a rhetorical relation – Nucleus/satellite notion encodes asymmetry

63 Discourse Parsing Coherence Relation Assignment: determine automatically the coherence relations between units of a discourse Discourse Parsing: find automatically the discourse structure of an entire discourse Example of approaches: – Unsupervised based on cue phrases (or discourse markers) – Supervised, based on discourse treebanks – cf. the Penn Discourse Treebank (http://www.seas.upenn.edu/~pdtb)

64 Automatic Coherence Assignment Shallow cue-phrase-based algorithm: 1.Identify cue phrases in a text 2.Segment text into discourse segments using cue phrases 3.Assign coherence relations between consecutive discourse segments

65 Automatic coherence assignment Step 1: identification of cue phrases that signal coherence relations – Discourse connectives: “because”, “although”, “example”, “with”, “and” … Connectives are ambiguous – With its distant orbit, Mars exhibits frigid weather conditions – We can see Mars with an ordinary telescope Use some simple heuristics, e.g. capitalization of with, etc. or more complex disambiguation techniques

66 Automatic coherence assignment Step 2: segment the text into discourse segments – Typically, sentences may suffice BUT clauses are often more appropriate (Sporleder and Lapata, 2004) – With its distant orbit, Mars exhibits frigid weather conditions Step 3: apply rules based on the connectives – Example: a sentence beginning with “because” indicates Explanation relation with the next segment Explanation

67 Identifying RS Automatically (Marcu 1999) A supervised parser trained on a discourse treebank – 90 rhetorical structure trees, hand-annotated for rhetorical relations (RRs) – Elementary discourse units (EDUs) linked by RRs – Parser learns to identify Nucleus, Satellite and their RR – Features: Wordnet-based similarity, lexical, structural Uses discourse segmenter to identify discourse units – Trained to segment on hand-labeled corpus (C4.5) – Features: 5-word POS window, presence of discourse markers, punctuation, seen a verb?, …

68 Issues in discourse/dialogue Dialogue – Turn-taking – Speech act – Grounding – Dialogue management Discourse – Segmentation, coherence relations Both – Anaphora – Co-reference

69 IDENTIFYING WHICH MENTIONS REFER TO THE SAME (DISCOURSE) ENTITY Task definition

70 Chains of mentions in text (COREFERENCE CHAIN) Toni Johnson pulls a tape measure across the front of what was once a stately Victorian home. A deep trench now runs along its north wall, exposed when the house lurched two feet off its foundation during last week's earthquake. Once inside, she spends nearly four hours measuring and diagramming each room in the 80-year-old house, gathering enough information to estimate what it would cost to rebuild it. While she works inside, a tenant returns with several friends to collect furniture and clothing. One of the friends sweeps broken dishes and shattered glass from a countertop and starts to pack what can be salvaged from the kitchen. (WSJ section of Penn Treebank corpus)

71 Anaphora ≠ Coreference COREFERENT, not ANAPHORIC – two mentions of same object in different documents – Obama was interviewed last night. The President.. ANAPHORIC, not COREFERENT – identity of sense: John bought a shirt, and Bill got ONE, too

72 Nominal anaphoric expressions – REFLEXIVE PRONOUNS: John bought himself an hamburger – PRONOUNS: Definite pronouns: Ross bought {a radiometer | three kilograms of after-dinner mints} and gave {it | them} to Nadia for her birthday. (Hirst, 1981) Indefinite pronouns: Sally admired Sue’s jacket, so she got one for Christmas. (Garnham, 2001) – DEFINITE DESCRIPTIONS: A man and a woman came into the room. The man sat down. Epiteths: A man ran into my car. The idiot wasn’t looking where he was going. – DEMONSTRATIVES: Tom has been caught shoplifting. That boy will turn out badly.

73 Non-nominal anaphoric expressions PRO-VERBS: – Daryel thinks like I do. GAPPING: – Nadia brought the food for the picnic, and Daryel _ the wine. TEMPORAL REFERENCES: – In the mid-Sixties, free love was rampant across campus. It was then that Sue turned to Scientology. (Hirst, 1981) LOCATIVE REFERENCES: – The Church of Scientology met in a secret room behind the local Colonel Sanders’ chicken stand. Sue had her first dianetic experience there. (Hirst, 1981)

74 Types of anaphoric relations Identity of REFERENCE – Ross bought {a radiometer | three kilograms of after-dinner mints} and gave {it | them} to Nadia for her birthday. Identity of SENSE – Sally admired Sue’s jacket, so she got one for Christmas. (Garnham, 2001) – (PAYCHECK PRONOUNS): The man who gave his paycheck to his wife is wiser than the man who gave it to his mistress. (Karttunen, 1976?) BOUND anaphora – No Italian believes that World Cup referees treated his team fairly ASSOCIATIVE / indirect anaphoric relations (‘bridging’) – The house …. the kitchen

75 Associative anaphora (a type of BRIDGING) Toni Johnson pulls a tape measure across the front of what was once a stately Victorian home. A deep trench now runs along its north wall, exposed when the house lurched two feet off its foundation during last week's earthquake. Once inside, she spends nearly four hours measuring and diagramming each room in the 80-year-old house, gathering enough information to estimate what it would cost to rebuild it. While she works inside, a tenant returns with several friends to collect furniture and clothing. One of the friends sweeps broken dishes and shattered glass from a countertop and starts to pack what can be salvaged from the kitchen. (WSJ section of Penn Treebank corpus)

76 Not all ‘anaphoric’ expressions always anaphoric Expletives – It is half past two. – There is an engine at Avon First mention definites References to visual situation (‘exophora’) – pick that up and put it over there.

77 Interpreting anaphoric expressions Interpreting (‘resolving’) an anaphoric expressions involves at least three aspects: 1. Deciding whether the expression is in fact anaphoric 2. Identifying its antecedent (possibly not introduced by a nominal) 3. Determining its meaning (cfr. identity of sense vs. identity of reference) (not necessarily taken in this order!)

78 Factors that affect the interpretation of anaphoric expressions Factors: – Morphological features (agreement) – Syntactic information (Binding) – Salience – Lexical and commonsense knowledge Distinction often made between CONSTRAINTS (must, e.g. agreement in gender as: John...he, the book...it) and PREFERENCES

79 Classification – Train a classifier to determine whether two mentions are coreferent or not coreferent [Israel] will ask the US to … [the Jewish state] … [Iraqi],... coref ? not coref ? coref ? Supervised learning for coreference resolution

80 Clustering pairwise coreference decisions Israel the Jewish state its A jittery public Clustering Algorithm Iraq Iraqi US United States Iraq USA [Israel], will ask the US [the Jewish state] [Iraqi]... coref not coref not coref Israel Supervised learning for coreference resolution

81 Soon et al. (2001) : Features Features Used – Number Agreement Feature: are i and j both singular or both plural – Semantic Class Agreement Feature: true if the sem class of i and j are the same or if one is the parent of the other; false or unknown otherwise – Gender Agreement Feature: are i and j of the same gender, based on designator (”Mr.”) or pronoun – Both-Proper-Names Feature: true if i and j are proper names – Alias Feature: true if i is an alias of j or vice versa – Appositive Feature: true if i or j is a proper name and i and j are separated by a comma and no verb

82 Soon et al. (2001) : semantic class agreement PERSON FEMALEMALE OBJECT DATEORGANIZATION TIMEMONEYPERCENT LOCATION SEMCLASS = true iff semclass(i) <= semclass(j) or viceversa

83 Soon et al. (2001) : Features Example

84 Lexical and commonsense knowledge Nominals the most common type of anaphoric expression Main source of errors with nominals: lack of commonsense knowledge Semantics has been pointed out as being relevant since seminal work: – Cf. e.g. Charniak (1973); Hobbs (1978)

85 Lexical and commonsense knowledge: examples Which kind of semantics is useful for coreference resolution? – synonymy relations – i.e. different expressions used to refer to the same concept

86 Synonymy relations and coreference resolution Toni Johnson pulls a tape measure across the front of what was once [a stately Victorian home]. ….. The remainder of [the house] leans precariously against a sturdy oak tree. to merge their U.S. satellite TV operations with Primestar Partners, [the nation]’s largest satellite TV company, sources familiar with the inquiry say. [... ] The slot is highly valuable because it is one of only three available in [this country] from which a satellite can beam TV programs across most of North America simultaneously.

87 Lexical and commonsense knowledge: examples Which kind of semantics is useful for coreference resolution? – instance-of relations – i.e. that an individual belongs to a certain class (e.g. Obama is an istance of U.S. President, Fido is an instance of dog, Italy is an istance of Nation)

88 Instantiation relations and coreference resolution [The FCC] took [three specific actions] regarding [AT&T]. By a 4-0 vote, it allowed AT&T to continue offering special discount packages to big customers, called Tariff 12, rejecting appeals by AT&T competitors that the discounts were illegal. ….. ….. [The agency] said that because MCI's offer had expired AT&T couldn't continue to offer its discount plan.

89 Lexical and commonsense knowledge: examples Which kind of semantics is useful for coreference resolution? – isa relations – i.e. subsumption between concepts (cat is-a feline, car is-a vehicle)

90 Subsumption relations and coreference resolution [Petrie Stores Corporation, Secaucus NJ] said an uncertain economy and faltering sales probably will result in a second quarter loss and perhaps a deficit for the first six months of fiscal 1994 [The women’s apparel specialty retailer] said sales at stores open more than one year, a key barometer of a retain concern strength, declined 2.5% in May, June and the first week of July. [The company] operates 1714 stores. In the first six months of fiscal 1993, [the company] had net income of $1.5 million ….

91 Lexical and commonsense knowledge: examples Which kind of semantics is useful for coreference resolution? – alias relations – i.e. abbreviation-like information for individuals (UNO = United nations organization)

92 Alias relations and coreference resolution A new report reveals more problems at [the Internal Revenue Service]. A broad review of [the agency] found it used improper tactics in evaluating [IRS] employees at many [IRS] offices across the country. Includes also more complex examples:  “GOP” and “the Republican Party”

93 Lexical and commonsense knowledge: examples Which kind of semantics is useful for coreference resolution? – meronymical relations – i.e. part-of relations between concepts (arm, body; wheels, car; kitchen,house)

94 Meronymical relations and coreference resolution Once inside, she spends nearly four hours measuring and diagramming each room in [the 80- year-old house], gathering enough information to estimate what it would cost to rebuild it. While she works inside, a tenant returns with several friends to collect furniture and clothing. One of the friends sweeps broken dishes and shattered glass from a countertop and starts to pack what can be salvaged from [the kitchen].

95 Early work: the semantic network assumption (Charniak, Sidner) CAR VEHICLE WHEELS ISA HAS I saw [a car] come in. … THE VEHICLE was moving very slowly … THE WHEELS were moving very slowly …

96 Lexical and commonsense knowledge sources Using semantic networks, e.g. WordNet: – Poesio & Vieira (1998); Harabagiu et al. (2001) Extracting knowledge from corpora – Poesio et al. (2002); Kehler (2004); Yang et al. (2005); Markert & Nissim (2005); Versley (2007) Extracting knowledge from resources such as lexica and encyclopedias, e.g. from Wikipedia – Ponzetto & Strube (2006; 2007)

97 Conclusion of NLP course Basic tasks: POS tagging, chunking, parsing, semantic analysis TOOLS/RESOURCES/METHODS: Many available algorithms on-line, many linguistic resources (WordNet, Framenet, Dbpedia..) + Machine learning algorithms (weka) Complex tasks: entailment, anaphora resolution, structural analysis Applications: Information Extraction, Question Answering, Text Classification (including opinion mining), Dialogue, Summarization, Profiling, Information filtering, Image Classification, Language Generation..NATURAL LANGUAGE IS PERVASIVE

98 THE END


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