Natural Language Processing Applications

1. Natural Language Processing Applications Fabienne Venant Universit� Nancy2 / Loria 2008/2009

2. Introduction to NLP

3. NLP aims at : making computers talk� endowing computers with the linguistics ability of humans

4. Dialog systemFiction

5. Dialog systemreality E-commerce: AINI

6. Dialog systemreality E-teaching : autotutor (http://www.autotutor.org/what/what.htm ) Intelligent tutoring system that helps student learn by holding a conversationnal in natural language Animated agent : synthesis speech, intonation, facial expressions, and gestures demo (from 2002)

7. Machine translation Automatically translate a document from one language to another Very useful on the web Far from solved problem

8. Question - answering Generalization of simple Web search Ask complete questions What does divergent mean? How many states were in Europe in 2007? What is the occupation of Bill Clinton�s wife ? What do scientist think about global warming?

9. Linguistic knowledge in NLP

10. Linguistic knowledge in NLP What would HAL need to engage in this dialog? Dave Bowman: Hello, HAL do you read me, HAL? HAL: Affirmative, Dave, I read you. Dave Bowman: Open the pod bay doors, HAL. HAL: I'm sorry Dave, I'm afraid I can't do that. Dave Bowman: What's the problem? HAL: I think you know what the problem is just as well as I do. Dave Bowman: What are you talking about, HAL? HAL: This mission is too important for me to allow you to jeopardize it. Dave Bowman: I don't know what you're talking about, HAL? HAL: I know you and Frank were planning to disconnect me, and I'm afraid that's something I cannot allow to happen. Dave Bowman: Where the hell'd you get that idea, HAL? HAL: Dave, although you took thorough precautions in the pod against my hearing you, I could see your lips move.

11. Speech recognition / speech synthesis phonetics, phonology : how words are pronounced in terms of sequences of sounds How each of these sounds is realized acoustically Morphology : can�t, I�m, were, lips... Producing and recognizing variations of individual words The way words break down into component parts that carry meaning (like sg / pl)

12. PhoneticsStudy of the physical sounds of human speech /i:/, /?:/, /?:/, /?:/ and /u:/ 'there' =>�/�e?/ 'there on the table' => /�e?r ?n �? te?bl / Exercices

13. Phonetics 2 Articulory phonetics : production

14. Phonology Describe the way sounds function to encode meaning Phoneme : speech sound that helps us constructing meaning /r/ : rubble ?double, Hubble, fubble, wubble. /u/ : rubble ? rabble, rebel, Ribble, robble... Phoneme can be realized in different forms depending on context (allophones) /l/ : lick [l] / ball [?] Speech synthesis uses allophones Speackjet

15. Morphology Study the structure of words Inflected forms ? lemma walks, walking, walked ? walk Lemma + part of speech = lexeme Walk, walking, walked ? walk Walker, walkers ? walker Flectional morphology : decomposes a word into a lemma and one or more affixes giving informations abouts tense, gender, number Cats? lemma: cat + affixe s (plural) Derivational morphology: decomposes a word into a lemma and one or more affixes giving informations about meaning and category Unfair ? prefix (un, semantic: non) + lemma: fair Exceptions and irregularities ? Women ? woman, pl Aren�t ? Are not

16. MorphologyMethods Lemmatisation : process of grouping together the different inflected forms of a word so they can be analysed as a single item Need to determine the part of speech of a word in a sentence (requiring grammar knowledge) Stemming: operates on a single word without knowledge of the context cannot discriminate between words which have different meanings depending on part of speech easier to implement and run faster, reduced accuracy may not matter for some applications Examples better ? lemma : good, missed in the stemming walking ?lemma: walk, matched in both stemming and lemmatization.

17. MorphologyMethod and applications Method Finite state transducer Applications to resolve anaphora: Sarah met the women in the street. She did not like them. [She (sg) = Sarah (sg) ; them (pl) = the women (pl) ] for spell checking and for generation * The women (pl) is (sg) For information retrieval Google search ...

18. Syntax I�m sorry Dave, I can�t do that

19. Syntax structure of language I�m I do, sorry that afraid Dave I�m can�t Languages have structure: not all sequences of words over the given alphabet are valid when a sequence of words is valid (grammatical ), a natural structure can be induced on it.

20. Syntax Describes the constituent structure of NL expressions (I (am sorry)), Dave, ( I ((can�t do) that)) Grammars are used to describe the syntax of a language Syntactic analysers and surface realisers assign a syntactic structure to a string/semantic representation on the basis of a grammar

21. Syntax It is useful to think of this structure as a tree: represents the syntactic structure of a string according to some formal grammar. the interior nodes are labeled by non-terminals of the grammar, while the leaf nodes are labeled by terminals of the grammar.

22. Syntax tree example

23. Methods in syntax Words ? syntactic tree Algorithm: parser A parser checks for correct syntax and builds a data structure. Resources used: Lexicon + Grammar Symbolic : hand-written grammar and lexicon Statistical : grammar acquired from treebank Treebank : text corpus in which each sentence has been annotated with syntactic structure. Syntactic structure is commonly represented as a tree structure, hence the name treebank. Difficulty: coverage and ambiguity

24. Syntaxapplications For spell checking *its a fair exchange ? No syntactic tree It�s a fair exchange ? ok syntactic tree To construct the meaning of a sentence To generate a grammatical sentence

25. Syntax ? meaning John loves Mary love(j,m) Agent = Subject ?Mary loves John love(m,l) Agent = Subject =Mary is loved by John love(j,m) Agent = By-Object

26. Semantics Where the hell �d you get that idea HAL Dave, although you took thorough precautions in the pod against my hearing you, I could see your lips move

27. Lexical semanticsMeaning of words come to have or hold; receive. succeed in attaining, achieving, or experiencing; obtain. experience, suffer, or be afflicted with. move in order to pick up, deal with, or bring. bring or come into a specified state or condition. catch, apprehend, or thwart. come or go eventually or with some difficulty. move or come into a specified position or state ...

28. Lexical semantics

29. Compositional semantics Where the hell did you get that idea?

30. Semantics issues in NLP Definition and representation of meaning Meaning construction Semantic relations Interaction between semantic and syntax

31. Semantic relations Paradigmatic relation (substitution) synonymy: sofa=couch=divan=davenport antonymy: good/bad, life/death, come/go contrast: sweet/sour/bitter/salty, solid/liquid/gas hyponymy, or class inclusion: cat<mammal<animal meronymy, or the part-whole relation: line<stanza<poem

32. Semantic relations Syntagmatic relations: relations between words that go together in a syntactic structure. Collocation : heavy rain, to have breakfast, to deeply regret... Useful for generation Argumental structure Someone breaks something with something Difficulty: number of arguments ? Can an argument be optional ? John brokes the window John brokes the window with a hammer The window brokes ? semantic argument ? syntactic argument Thematic roles : agent, patient, goal, experiencer, theme...

33. semantic / syntaxlexicon Sub categorisation frames to run: SN1 to eat : SN1, SN2 To give : SN1, SN2, SP3 (to) envious : SN1, SP2 (of)

34. Semantic / syntaxlexicon Argumental structure Logic representation: eat (x, y), give (x,y,z) Thematic roles : to give [agent, theme, go k], to buy [agent, theme, source], to love [experiencer, patient] Link with syntax: break (Agent:, Instrument, Patient:) Agent <=> subj Instrument <=> subj, with-pp Patient <=> obj, subj Selectional restrictions: semantics features on arguments To eat [agent : animate, theme : comestible, solid] John eats bread l th�me [+solide] [+comestible] *The banana eats ? filtering * John eats wine But : ? John eats soup

35. Semantics in NLP For machine translation Le robinet fuit / Le voleur fuit -> leak/run away For information retrieval (and cross Language Information Retrieval) Search on word meaning rather than word form Keywords disambiguation Query expansion (synonyms) ? more relevance

36. Semantics in NLP QA: Who assassinated President McKinley? Keywords: assassinated President McKinley /Answer named entity : Person / Answer thematic role : Agent of target synonymous with \assassinated False positive (1): In [ne=date 1904], [ne=person description President] [ne=person Theodore Roosevelt], who had succeeded the [target assassinated] [role=patient [ne=person William McKinley]], was elected to a term in his own right as he defeated [ne=person description Democrat] [ne=person Alton B. Parker]? Correct Answer (8): [role=temporal In [ne=date 1901]], [role=patient [ne=person description President] [ne=person William McKinley]] was [target shot] [role=agent by [ne=person description anarchist] [ne=person Leon Czolgosz]] [role=location at the [ne=event Pan-American Exposition] in [ne=us city Bu_alo], [ne=us state N.Y.]

37. Pragmatics Dave Bowman: Open the pod bay doors, HAL. HAL: I'm sorry Dave, I'm afraid I can't do that.

38. Pragmatics Knowledge about the kind of actions that speakers intend by their use of sentences REQUEST: HAL, open the pod bay door. STATEMENT: HAL, the pod bay door is open. INFORMATION QUESTION: HAL, is the pod bay door open? Speech act analysis (politeness, irony, greeting, apologizing...)

39. Discourse Where the hell'd you get that idea, HAL? Dave and Frank were planning to disconnect me ? Much of language interpretation is dependent on the preceding discourse/dialogue

40. Linguistics knowledge in NLPsummary Phonetics and Phonology �knowledge about linguistic sounds Morphology �knowledge of the meaningful components of word Syntax �knowledge of the structural relationships between word Semantics �knowledge of meaning Pragmatics � knowledge of the relationship of meaning to the goals and intentions of the speaker Discourse �knowledge about linguistic units larger than a single utterance

41. Ambiguity Most tasks in speech and language processing can be viewed as resolving ambiguity at one of these levels Ambiguous item ? multiple, alternative linguistic structures can be built for it.

42. Ambiguity I made her duck I cooked waterfowl for her. I cooked waterfowl belonging to her. I created the (plaster?) duck she owns. I caused her to quickly lower her head or body.

43. Ambiguity I made her duck Morphological ambiguity : duck : verb / noun her: dative pronoun / possessive pronoun Semantical ambiguity Make: create / cook Syntatic ambiguity: Make: transitive/ ditransitive [her duck ] / [her][duck]

44. Ambiguity Sound-to- text issues: Recognise speech. Wreck a nice peach. Speech act interpretation Can you switch on the computer?� Question or request?

45. Ambiguity vs paraphrase Ambiguity : the same sentence can mean different things Paraphrase: There are many ways of saying the same thing. Beer, please. Can I have a beer? Give me a beer, please. I would like beer. I�d like a beer, please. In generation (Meaning?Text), this implies making choices

46. Models and algorithms

47. Models and algorithms The various kind of knowledge can be captured through the use of a small number of formal models or theories Models and theories are all drawn for the standard toolkit of computer science, mathematics and linguistics

48. Models and algorithms Models State machines Rule systems Logic Probalistic models Vector-space models Algorithms Dynamic programming Machine learning Classifiers / sequence models Expectation-maximization (EM) Learning algorithms

49. Models State machine : simplest formulation State, transition among state, input representation Finite-state automata Deterministic Non deterministic Finite-state transducers

50. Models Formal rules systems Regular grammars Context-free grammars Feature augmented grammars

51. Models State machines and formal rule systems are the main tools used when dealing with knowledge of phonology, morphology,and syntax.

52. Models Models based on logics First Order Logic / predicate calculus Lamda-calculus, feature structures, semantic primitives These logical representations have traditionally been used for modeling semantics and pragmatics, although more recent work has tended to focus on potentially more robust techniques drawn from non-logical lexical semantics.

53. Models Probabilistic models crucial for capturing every kind of linguistic knowledge. Each of the other models can be augmented with probabilities. Example, the state machine augmented with probabilities can become weighted automaton, or Markov model. hidden Markov models (HMMs) : part-of-speech tagging, speech recognition, dialogue understanding, text-to-speech, machine translation.... Key advantage of probabilistic models : ability to solve the many kinds of ambiguity problems almost any speech and language processing problem can be recast as �given N choices for some ambiguous input, choose the most probable one�.

54. Models Vector space models based on linear algebra Information-retrieval Word meanings

55. Models Language processing : search through a space of states representing hypotheses about an input Speech recognition : search through a space of phone sequences for the correct word. Parsing : search through a space of trees for the syntactic parse of an input sentence. Machine translation : search through a space of translation hypotheses for the correct translation of a sentence into another language.

56. Models Machine learning models: classifiers, sequence models Based on attributes describing each object Classifier : attempts to assign a single object to a single class Sequence model: attempts to jointly classify a sequence of objects into a sequence of classes. Example, deciding whether a word is spelled correctly : classifiers : decision trees, support vector machines, Gaussian mixture models + logistic regression ? make a binary decision (correct or incorrect) for one word at a time. Sequence models : hidden Markov models, maximum entropy Markov models + conditional random fields ? assign correct/incorrect labels to all the words in a sentence at once.

57. Brief history

58. Brief history 1940�s - 1950�s : foundational insights 1950- 1970 : symbolic / statistical 1970 � 1983 : four paradigms 1983 � 1993 : empiricism and finite state models 1994 � 1999: field unification 2000 -2008 : empiricist trends

59. 1940�s ? 1950�s Automaton Probabilistic / information � theoretic models

60. 1940�s ? 1950�sAutomaton Turing�s (1936) : model of algorithmic computation McCulloch-Pitts neuron (McCulloch and Pitts, 1943) : a simplified model of the neuron as a kind of computing element (propositional logic) Kleene (1951) and (1956) : finite automata and regular expressions. Shannon (1948) : probabilistic models of discrete Markov processes to automata for language. Chomsky (1956) : finite state machines as a way to characterize a grammar Formal language theory (algebra and set theory): Context-free grammar for natural languages Chomsky (1956) Backus (1959) and Naur et al. (1960) : ALGOL programming language.

61. 1940�s ? 1950�sProbalistic algorithms Speech and language processing, Shannon metaphor of the noisy channel entropy as a way of measuring the information capacity of a channel, or the information content of a language, first measure of the entropy of English by using probabilistic techniques. Sound spectrograph (Koenig et al., 1946), Foundational research in instrumental phonetics First machine speech recognizers (early 1950s). 1952, Bell Lab, statistical system that could recognize any of the 10 digits from a single speaker (Davis et al., 1952).

62. 1940�s ? 1950�sMachine translation One of the earliest applications of computers Major attempts in US and USSR Russian to English and reverse George Town University, Washington system: Translated sample texts in 1954 The ALPAC report (1964) Assessed research results of groups working on MTs Concluded: MT not possible in near future. Funding should cease for MT ! Basic research should be supported. Word to word translation does not work Linguistic Knowledge is needed

63. 1950�s ? 1970�sTwo camps Symbolic paradigm Statistical paradigm

64. 1950�s ? 1970�sSymbolic paradigm 1 Formal language theory and generative syntax 1957 Noam Chomsky's Syntactic Structures A formal definition of grammars and languages Provides the basis for an automatic syntactic processing of NL expressions Montague's PTQ Formal semantics for NL. Basis for logical treatment of NL meaning 1967 : Woods procedural semantics A procedural approach to the meaning of a sentence Provides the basis for a automatic semantic processing of NL expressions

65. 1950�s ? 1970�sSymbolic paradigm 2 Parsing algorithms top-down and bottom-up dynamic programming. Transformations and Discourse Analysis Project (TDAP) Harris, 1962 Joshi and Hopely (1999) and Karttunen (1999), cascade of finite-state transducers.

66. 1950�s ? 1970�sSymbolic paradigm 3 AI Summer of 1956 :John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester work on reasoning and logic Newell and Simon ? the Logic Theorist and the General Problem Solver Early natural language understanding systems Domains Combination of pattern matching and keyword search Simple heuristics for reasoning and question-answering Late 1960s ? more formal logical systems

67. 1950�s ? 1970�sStatistical paradigm 1 Bayesian method to the problem of optical character recognition. Bledsoe and Browning (1959) : Bayesian text-recognition a large dictionary compute the likelihood of each observed letter sequence given each word in the dictionary Joshi and Hopely (1999) and Karttunen (1999) cascade of finite-state transducers likelihoods for each letter. Bayesian methods to the problem of authorship attribution on The Federalist papers Mosteller and Wallace (1964) Testable psychological models of human language processing based on transformational grammar Ressources First online corpora: the Brown corpus of American Englis DOC (Dictionary on Computer) an on-line Chinese dialect dictionary.

68. Symbolic vs statistical approaches Symbolic Based on hand written rules Requires linguistic expertise No frequencey information More brittle and slower than statistical approaches Often more precise than statistical approaches Error analysis is usually easier than for statistical approaches Statistical Supervised or non-supervised Rules acquired from large size corpora Not much linguistic expertise required Robust and quick Requires large size (annotated) corpora Error analysis is often difficult

69. Four paradigms: 1970-1983 Statistical Logic-based paradigms Natural language understanding Discourse modeling

70. 1970-1983Statistical paradigm Speech recognition algorithms Hidden Markov model (HMM) and the metaphors of the noisy channel and decoding Jelinek, Bahl, Mercer, and colleagues at IBM�s Thomas J. Watson Research Center, Baker at Carnegie Mellon University Baum and colleagues at the Institute for Defense Analyses in Princeton AT&T�s Bell Rabiner and Juang (1993) ? descriptions of the wide range of this work.

71. 1970-1983Logic-based paradigm Q-systems and metamorphosis grammars (Colmerauer, 1970, 1975) Definite Clause Grammars (Pereira and Warren, 1980) Functional grammar (Kay,1979) Lexical Functional Grammar (LFG) (Bresnan and Kaplan�s,1982) ?importance of feature structure unification

72. 1970-1983Natural language understanding1 SHRDLU system : simulated a robot embedded in a world of toy blocks (Winograd, 1972a). natural-language text commands Move the red block on top of the smaller green one complexity and sophistication first to attempt to build an extensive (for the time) grammar of English (based on Halliday�s systemic grammar) Ok for parsing Semantic and discourse?

73. 1970-1983Natural language understanding2 Yale School : series of language understanding programs conceptual knowledge (scripts, plans, goals..) human memory organization network-based semantics (Quillian, 1968) case roles (Fillmore, 1968) representations of case roles (Simmons, 1973).

74. 1970 - 1083 Unification of logic-based and natural-language-understanding paradigms in systems such as the LUNAR question-answering system (Woods, 1967, 1973) ? uses predicate logic as a semantic representation

75. 1970-1983Discourse Modelling Four key areas in discourse: Substructure in discourse A discourse focus Automatic reference resolution (Hobbs, 1978) BDI (Belief-Desire-Intention) framework for logic-based work on speech acts (Perrault and Allen,1980; Cohen and Perrault, 1979).

76. Return of state models Finite-state phonology and morphology (Kaplan and Kay, 1981) Finite-state models of syntax by Church (1980). Return of empiricism Probabilistic models throughout speech and language processing, IBM Thomas J. Watson Research Center: probabilistic models of speech recognition. Data-driven approaches Speech ? part-of-speech tagging, parsing, attachment ambiguities, semantics. New focus on model evaluation, Held-out data Quantitative metrics for evaluation, Comparison of performance on these metrics with previous published research. Considerable work on natural language generation 1983-1993

77. 1994-1999 Major changes. Probabilistic and data-driven models had become quite standard Parsing, part-of-speech tagging, reference resolution, and discourse processing Algorithms incorporate probabilities Evaluation methodologies from speech recognition and information retrieval. Increases in the speed and memory of computers commercial exploitation (speech recognition, spelling and grammar correction) Augmentative and Alternative Communication (AAC) Rise of the Web need for language-based information retrieval and information extraction.

78. 1994-1999Ressources and corpora Disk space becomes cheap Machine readable text becomes uniquitous US funding emphasises large scale evaluation on ��real data�� 1994 : The British National Corpus is made available A balanced corpus of British English Mid 1990s : WordNet (Fellbaum & Miller) A computational thesaurus developed by psycholinguists The World Wide Web used as a corpus

79. 2000-2008Empiricist trends 1 Spoken and written material widely available Linguistic Data Consortium (LDC) ... Annotated collections (standard text sources with various forms of syntactic, semantic, and pragmatic annotations) Penn Treebank (Marcus et al., 1993),) PropBank (Palmer et al., 2005), TimeBank (Pustejovsky et al., 2003b) .... More complex traditional problems castable in supervised machine learning Parsing and semantic analysis Competitive evaluations Parsing (Dejean and Tjong Kim Sang, 2001), Information extraction (NIST, 2007a; Tjong Kim Sang, 2002; Tjong Kim Sang and De Meulder, 2003) Word sense disambiguation (Palmer et al., 2001; Kilgarriff and Palmer, 2000) Question answering (Voorhees and Tice, 1999), and summarization (Dang, 2006).

80. 2000-2008Empiricist trends 2 More serious interplay with the statistical machine learning community Support vector machines (Boser et al., 1992; Vapnik, 1995) Maximum entropy techniques (multinomial logistic regression) (Berger et al., 1996) Graphical Bayesian models (Pearl, 1988)

81. 2000-2008Empiricist trends 2 Largely unsupervised statistical approaches Statistical approaches to machine translation (Brown et al., 1990; Och and Ney, 2003) t Topic modeling (Blei et al., 2003) ? Effective applications could be constructed from systems trained on unannotated data alone. ? Use of unsupervised techniques

82. Language and intelligence

83. Language and intelligence Is a talking machine a thinking machine? Use of language intertwined with our general cognitive abilities. Turing test (1950), "Computing Machinery and Intelligence�� What does that mean that a machine thinks? Imprecion in the terms ��machine�� and ��think��

84. Turing test The computer�s use of language forms the basis for determining if the machine could think. If the machine could win the game, it would be judged intelligent. Three participants two people and a computer. a human judge engages in a natural language conversation with one human and one machine each of which try to appear human If the judge cannot reliably tell which is which, then the machine is said to pass the test.

85. Eliza Eliza, the first chatterbot : a computer program that mimics human conversation. Joseph Weizenbaum, Massachusetts Institute of Technology User types in natural language ELIZA then analyzes the user's statement and generates some response which it types out. http://www-ai.ijs.si/eliza/eliza.html

86. Men are all alike. IN WHAT WAY? They're always bugging us about something or other. CAN YOU THINK OF A SPECIFIC EXAMPLE? Well, my boyfriend made me come here. YOUR BOYFRIEND MADE YOU COME HERE He says I'm depressed much of the time. I AM SORRY TO HEAR YOU ARE DEPRESSED It's true. I am unhappy DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE UNHAPPY I need some help, that much seems certain. WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP Perhaps I could learn to get along with my mother. TELL ME MORE ABOUT YOUR FAMILY ELIZASample conversation

87. Eliza's strategyKeywords Input read and inspected for the presence of a keyword When keyword is found, the sentence is transformed according to a rule associated with the keyword Else a content-free remark or, under certain conditions, an earlier transformation is retrieved. The text so computed or retrieved is then printed out

88. ELIZAExample transformations "I am X" ? "How long have you been X?�� I am very unhappy. HOW LONG HAVE YOU BEEN VERY UNHAPPY? "X YOU Y ME" ? "What makes you think I Y you?�� It seems that you hate me. WHAT MAKES YOU THINK THAT I HATE YOU?

89. ELIZAWhy does it work? Rogerian psychotherapist ? Can pretend to know almost nothing of the real world "I went for a long boat ride" "Tell me about boats" Not a real dialog (no real communication) ELiza generates a reaction (often a rather non commital one) to the user's statement The user cannot ask for information. The user cannot issue an order

90. ELIZADoes it really work? Eliza: Hello. I am ELIZA. How can I help you? You: I went to Paris yesterday Eliza: Can you elaborate on that? You: I took the train Eliza: Can you elaborate on that? You: The train was late Eliza: Please go on. You: All the seats were full Eliza: Please go on. You: A little boy was having a tantrum Eliza: Please go on.

91. ELIZADoes it really work? Indexicals (I, you, here, now) User: I am having fun right now. Eliza: Does it please you to believe that I am having fun right now. World Knowledge User: I am a human. Eliza: How long have you been a human?

92. When Eliza fails Yes/No Questions User: Are you a good therapist? Eliza: Would you prefer it if I were a good therapist? User: I'm wondering about your skills as a therapist; Eliza: Does it please you to believe I am wondering about my skills of therapist? Wh-Questions User: Who are you? Eliza: Would you prefer it if I were not? User: What do you want me to tell? Eliza: Does that question interest you? Complex Assertions User: I don't have a problem. I'm quite happy, I have a girlfriend, I study. Everything is OK! Eliza: Why do you not have any problem, I'm quite happy, you have a girlfriend, you study. Everything is OK!

93. Eliza is not intelligent but... Regardless of what people believe or know about the inner workings of computers, they talk about them and interact with them as social entities. Weizenbaum (1976) notes that many people continued to believe in ELIZA�s abilities even after the program�s operation was explained to them. People act toward computers as if they were people and expect that computers should be able to understand their needs and be capable of interacting with them naturally. Given these predispositions, speech- and language-based system are not supposed to be intelligent But they may provide users with the most natural interface for many applications So what about turing test?

94. NLP applications Three main types of applications: Language input technologies Language processing technologies Language output technologies

95. Language input technologies Speech recognition Optical character recognition Handwriting recognition Retroconversion

96. Language input technologies Speech recognition Two main types of Applications Desktop control: dictation, voice control, navigation Telephony-based transaction: travel reservation, remote banking, pizza ordering, voice control 60-90% accuracy. Speech recognition is not understanding! Based on statistical techniques and very large corpora Cf. the Parole team (Yves Laprie)

97. Language input technologies Speech recognition Desktop control Philips FreeSpeech (www.speech.philips.com) IBM ViaVoice (www.software.ibm.com/speech) Scansoft's DragonNaturallySpeaking (www.lhsl.com/naturallyspeaking) demo See also google category: http://directory.google.com/Top/Computers/SpeechTechnology/

98. Language input technologies Dictation Dictation systems can do more than just transcribe what was said: leave out the 'ums' and 'eh� implement corrections that are dictated fill the information into forms rephrase sentences (add missing articles, verbs and punctuation; remove redundant or repeated words and self corrections) ? Communicate what is meant, not what is said Speech can be used both to dictate content or to issue commands to the word processing applications (speech macros eg to insert frequently used blocks of text or to navigate through form)

99. Language input technologies Dictation and speech recognition Telephony-based elded products Nuance (www.nuance.com) ScanSoft (www.scansoft.com) Philips (www.speech.philips.com) Telstra directory enquiry (tel. 12455) See also google category : http://directory.google.com/Top/Computers/SpeechTechnology/Telephony/

100. Language input technologies Optical character recognition Key focus Printed material ? computer readable representation Applications Scanning (text ) digitized format) Business card readers (to scan the printed information from business cards into the correct fields of an electronic address book : www.cardscan.com Website construction from printed documents Fielded products Caere's OmniPage (www.scansoft.com) Xerox' TextBridge (www.scansoft.com) ExperVision's TypeReader (www.expervision.com)

101. Language input technologies Handwriting recognition Key focus Human handwriting ? computer readable representation Applications Forms processing Mail routing Personal digital agenda (PDA)

102. Language input technologies Handwritting recognition Isolated letters Palm's Grati (www.palm.com) Computer Intelligence Corporation's Jot (www.cic.com) Cursive scripts Motorola's Lexicaus ParaGraph's Calligraphper (www.paragraph.com) cf. the READ team (Abdel Belaid)

103. Language input technologies Retroconversion Key focus: identify the logical and physical structure of the input text Applications Recognising tables of contents Recognising bibliographical references Locating and recognising mathematical formulae Document classication

104. Language processing technologies Spelling and grammar checking Spoken Language Dialog System Machine Translation Text Summarisation Search and Information Retrieval Question answering systems

105. Spoken Language Dialog Systems Goal a system that you can talk to in order to carry out some task. Key focus Speech recognition Speech synthesis Dialogue Management Applications Information provision systems: provides information in response to query (request for timetable information, weather information) Transaction-based systems: to undertake transaction such as buying/selling stocs or reserving a seat on a plane.

106. SLDSs - Some problems No training period possible in Phone-based systems Error handling remains difficult User initiative remains limited (or likely to result in errors)

107. SLDS state of the art Commercial systems operational for limited transaction and information services Stock broking system Betting service American Airlines information system Limited (finite-state) dialogue management NL Understanding is poor

108. SLDS commercial systems Nuance (www.nuance.com) SpeechWorks (www.scansoft.com) Philips (www.speech.philips.com) See also google category : http://directory.google.com/Top/Computers/SpeechTechnology/

109. Machine translation Key focus Translating a text written/spoken in one language into another language Applications Web based translation services Spoken language translation services

110. Existing MT system Bowne's iTranslator (www.itranslator.com) Taum-Meteo (1979): (English/French) Domain of weather reports Highly successful Systran: (among several European languages) Human assisted translation Rough translation Used over the internet through AltaVista http://babelsh.altavista.com

111. MT state of the art Broad coverage systems already available on the web (Systran) Reasonable accuracy for specic domains (TAUM Meteo) or controlled languages Machine aided translation is mostly used

112. Text summarisation Key issue Text ? Shorter version of text Applications To decide whether it's worth reading the original text To read summary instead of full text to automatically produce abstract

113. Text summarisation Three main steps Extract \important sentences" (compute document keywords and score document sentences wrt these keywords) Cohesion check: Spot anaphoric references and modify text accordingly (eg add sentence containing pronoun antecedent; remove dicult sentences; remove pronoun) Balance and coverage: modify summary to have an appropriate text structure (delete redundant sentences; harmonize tense of verbs; ensure balance and proper coverage)

114. Text summarisation State of the Art Sentences extracted on the basis of: location, linguistic cues, statistical information Low discourse coherence Commercial systems British Telecom's ProSum (transend.labs.bt.com) Copernic (www.copernic.com) MS Word's Summarisation tool See also http://www.ics.mq.edu.au/~swan/summarization/projects.htm

115. Information Extraction / Retrieval and QA Given a NL query and a document (e.g., web pages), Retrieve document containing answer (retrieval) Fill in template with relevant information (extraction) Produce answer to query (Q/A) Limited to factoid questions Excludes: how-to questions, yes-no questions, questions that require complex reasoning Highest possible accuracy estimated at around 70%

116. Information Extraction / Retrieval and QA IR systems : google, yahoo, etc. QA systems AskJeeves (www.askjeeves.com) Articial life's Alife Sales Rep (www.articial-life.com) Native Minds'vReps (www.nativeminds.com) Soliloquy (www.soliloquy.com)

117. Language output technologies Text-to-Speech Tailored document generation

118. Language output technologies Text to speech Key focus Text ? Natural sounding speech Applications Spoken rendering of email via desktop and telephone Document proofreading Voice portals Computer assisted language learning

119. Language output technologies Text to speech Requires appropriate use of intonation and phrasing Existing systems Scansoft's RealSpeak (www.lhsl.com/realspeak) British Telecom's Laureate AT&T Natural Voices (http://www.naturalvoices.att.com)

120. Language output technologies Tailored document generation Key focus Document structure + parameters ? Individually tailored documents Applications Personalised advice giving Customised policy manuals Web delivered dynamic documents

121. Language output technologies KnowledgePoint (www.knowledgepoint.com) Tailored job descriptions CoGenTex (www.cogentex.com) Project status reports Weather reports

122. NLP application summary NLP application process language using knowledge about language All levels of linguistic knowledge are relevant Two main problems: ambiguity and paraphrase NLP applications use a mix of symbolic and statistical methods Current applications are not perfect as Symbolic processing is not robust/portable enough Statistical processing is not accurate enough Applications should be classied into two main types: aids to human users (e.g., spell checkers, machine aided translations) and agents in their own right (e.g., NL interfaces to DB, dialogue systems) Useful applications have been built since the late 70s Commercial success is harder to achieve

123. Sources http://cslu.cse.ogi.edu/HLTsurvey/HLTsurvey.html