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Datalingvistiska tillämpningar

Datalingvistiska tillämpningar. Maskinöversättning Dialogsystem (ev. talad dialog, ev. multimodal) Textförståelsesystem Informationsextraktion Informationssökning Grammatikkontroll Datorstödd språkinlärning etc. Datalingvistiska ‘komponentteknologier’. Analys och generering av tal

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Datalingvistiska tillämpningar

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  1. Datalingvistiska tillämpningar • Maskinöversättning • Dialogsystem (ev. talad dialog, ev. multimodal) • Textförståelsesystem • Informationsextraktion • Informationssökning • Grammatikkontroll • Datorstödd språkinlärning • etc. FST - Torbjörn Lager, UU

  2. Datalingvistiska ‘komponentteknologier’ • Analys och generering av tal • Ordklasstaggning • Morfologisk analys och generering • Syntaktisk analys (parsning) • Semantisk tolkning • Referenslösning • Planering och planigenkänning • Kunskapsrepresentation och inferens • etc. FST - Torbjörn Lager, UU

  3. Ordklasstaggning: Exempel 1 • He can can a can • He/pron can/aux can/vb a/det can/n • He/{pron} can/{aux,n} can/{vb} a/{det} can/{n,vb} FST - Torbjörn Lager, UU

  4. Ordklasstaggning: Exempel 2 • I can light a fire and you can open a can of beans. Now the can is open and we can eat in the light of the fire. • I/PRP can/__ light/__ a/DT fire/NN and/CC you/PRP can/__ open/__ a/DT can/__ of/IN beans/NNS ./. Now/RB the/DT can/__ is/VBZ open/__ and/CC we/PRP can/__ eat/VB in/IN the/DT light/__ of/IN the/DT fire/NN ./. FST - Torbjörn Lager, UU

  5. Ordklasstaggning: Exempel 2 • I can light a fire and you can open a can of beans. Now the can is open and we can eat in the light of the fire. • I/PRP can/MD light/VB a/DT fire/NN and/CC you/PRP can/MD open/VB a/DT can/NN of/IN beans/NNS ./. Now/RB the/DT can/NN is/VBZ open/JJ and/CC we/PRP can/MD eat/VB in/IN the/DT light/NN of/IN the/DT fire/NN ./. FST - Torbjörn Lager, UU

  6. Olika typer av relevant information • lexikal information • kontextuell information FST - Torbjörn Lager, UU

  7. Varför ordklasstagga? • Korpuslingvistisk forskning • Ett försteg till ordbetydelsebestämning • Ett försteg till parsning • ? FST - Torbjörn Lager, UU

  8. Knowledge Processor POS tagged text Text Part-of-speech tagging • Needed:- some strategy for representing the knowledge - some method for acquiring the knowledge - some method of applying the knowledge FST - Torbjörn Lager, UU

  9. Knowledge Processor POS tagged text Text Some POS-tagging issues • Accuracy • Speed • Space requirements • Robustness • Learning FST - Torbjörn Lager, UU

  10. Vanliga indelningar • Taggningmetoder • regelbaserade • statistiska • Inlärningsmetoder • 'Supervised learning' • 'Unsupervised learning' FST - Torbjörn Lager, UU

  11. Formella verktyg • Formell logik • Sannolikhetsteori och statistik • Automatateori och matematisk lingvistik • Algoritm- och komplexitetsteori FST - Torbjörn Lager, UU

  12. Taggningsmetoder • HMM-taggning • statistikbaserad (probabilistisk) • 'supervised learning' • Brilltaggning • regelbaserad • 'supervised learning' • Constraint-Grammar tagging • regelbaserad • ingen inlärning FST - Torbjörn Lager, UU

  13. Hidden Markov Modelling • statistikbaserad • Den modiga ansatsen: "På basis av en sammanvägning av tillgänglig lexikal och kontextuell information, gissa! FST - Torbjörn Lager, UU

  14. FST - Torbjörn Lager, UU

  15. Brilltaggning • Strategi: "Gissa först, men ändra sen om nödvändigt" • Enkelt "heuristiskt" lexikon • En sekvens av transformationsregler betingade på lokal kontext: • Regelexempel: tag:vb>nn <- tag:dt@[-1] FST - Torbjörn Lager, UU

  16. Brilltaggning steg 1 • I can light a fire and you can open a can of beans. Now the can is open and we can eat in the light of the fire. • I/PRP can/MD light/JJ a/DT fire/NN and/CC you/PRP can/MD open/JJ a/DT can/MD of/IN beans/NNS ./. Now/RB the/DT can/NN is/VBZ open/JJ and/CC we/PRP can/MD eat/VB in/IN the/DT light/JJ of/IN the/DT fire/NN ./. FST - Torbjörn Lager, UU

  17. Transformation-based tagging • Representational strategy: • Simple lexica • Ordered lists of transformations, conditioned on (small amounts) of local context • Learning strategy: Transformation-based learning FST - Torbjörn Lager, UU

  18. Transformation-based tagging • Three steps: • Lexical look-up • Lexical rule application for unknown words • Contextual rule application FST - Torbjörn Lager, UU

  19. blue yellow blue brown red red blue blue brown green Transformation-based tagging K. Samuel 1998 FST - Torbjörn Lager, UU

  20. I PRP Now RB a DT and CC beans NNS can MD eat VB fire NN in IN is VBZ light JJ of IN open JJ the DT we PRP you PRP . . Lexikon för Brilltaggning FST - Torbjörn Lager, UU

  21. 'Constraint-Grammar'-taggning • Regelbaserad • Den försiktiga ansatsen: "Gissa inte! Eliminera bara det omöjliga!" FST - Torbjörn Lager, UU

  22. Ordklasstaggning: Exempel 2 • I can light a fire and you can open a can of beans. Now the can is open and we can eat in the light of the fire. • I/{PRP} can/{MD,NN} light/{JJ,NN,VB} a/{DT} fire/{NN} and/{CC} you/{PRP} can/{MD,NN} open/{JJ,VB} a/{DT} can/{MD,NN} of/{IN} beans/{NNS} ./{.} Now/{RB} the/{DT} can/{MD,NN} is/{VBZ} open/{JJ,VB} and/{CC} we/{PRP} can/{MD,NN} eat/{VB} in/{IN} the/{DT} light/{JJ,NN,VB} of/{IN} the/{DT} fire/{NN} ./{.} FST - Torbjörn Lager, UU

  23. Problem • Ambiguitet • Okända ord • Ovanliga ord • Ovanliga kontexter FST - Torbjörn Lager, UU

  24. Assessing the Brill tagger PARAMETER Accuracy 96.5% Speed Very fast Space req. Moderate Robustness Robust Learning Yes FST - Torbjörn Lager, UU

  25. Ordklasstaggning: Några ansatser • Den modiga ansatsen: "På basis av en sammanvägning av tillgänglig information, gissa! • Den försiktiga ansatsen: "Gissa inte! Eliminera bara det omöjliga!" • Den vankelmodiga ansatsen: "Gissa först, men ändra sen om nödvändigt" FST - Torbjörn Lager, UU

  26. Parsning • 'Klassisk' parsning med frasstrukturgrammatik • Ytparsning FST - Torbjörn Lager, UU

  27. Fragmentlisa springer lisa skjuter en älg Grammatiks --> np, vp. np --> pn.np --> det, n. vp --> v.vp --> v, np. pn --> [kalle].pn --> [lisa].det --> [en].n --> [älg].v --> [springer].v --> [skjuter]. En enkel frasstrukturgrammatik FST - Torbjörn Lager, UU

  28. Igenkänning och Parsning • Igenkänning ?- s([lisa,springer],[]).yes ?- s([springer,lisa],[]).no • Parsning • ?- s(Tree,[lisa,springer],[]).Tree = s(np(pn(lisa)),vp(v(springer))) FST - Torbjörn Lager, UU

  29. Parsning Frasstruktur FST - Torbjörn Lager, UU

  30. Grammatik s(s(NP,VP)) --> np(NP),vp(VP). np(np(PN)) --> pn(PN). np(np(DET,N)) --> det(DET),n(N). vp(vp(V)) --> v(V). vp(vp(V,NP)) --> v(V), np(NP). pn(pn(lisa)) --> [lisa]. det(det(en)) --> [en]. n(n(älg)) --> [älg]. v(v(går)) --> [går]. v(v(skjuter)) --> [skjuter]. Bygga träd i ett argument FST - Torbjörn Lager, UU

  31. Parsning ?- s(Tree,[lisa,skjuter,en,älg],[]). Tree = s( np( pn(lisa)), vp( v(skjuter), np( det(en), n(älg)))) Bygga träd i ett argument FST - Torbjörn Lager, UU

  32. Parsning med meta-interpretator s --> np, vp. det --> [en]. np --> pn. n --> [älg]. np --> det, n. tv --> [skjuter]. vp --> v, np. pn --> [lisa]. ? - parse(s,[lisa,skjuter,en,älg],[],Tree). Tree = s/(np/pn/lisa,vp/(v/skjuter,np/(det/en,n/älg))) FST - Torbjörn Lager, UU

  33. Parsning med meta-interpretator parse(A,P0,P,A/Trees) :- (A --> B), parse(B,P0,P,Trees). parse((B,Bs),P0,P,(Tree,Trees)) :- parse(B,P0,P1,Tree), parse(Bs,P1,P,Trees). parse([Word],[Word|P],P,Word). FST - Torbjörn Lager, UU

  34. Strukturell ambiguitet • Den gamla damen träffade killen med handväskan • John saw a man in the park with a telescope • Råttan åt upp osten och hunden och katten jagade råttan FST - Torbjörn Lager, UU

  35. Lokal ambiguitet • The old man the boats • The horse raced past the barn fell FST - Torbjörn Lager, UU

  36. Knowledge Processor Parsed text Text Some parsing issues • Accuracy • Speed • Space requirements • Robustness • Learning FST - Torbjörn Lager, UU

  37. Problems with traditional parsers • Correct lowlevel parses are often rejected because they do not fit into a global parse -> brittleness • Ambiguity -> indeterminism -> search -> slow parsers • Ambiguity -> sometimes hundreds of thousands of parse trees, and what can we do with these? FST - Torbjörn Lager, UU

  38. Another strategy (Abney) • Start with the simplest constructions (’easy-first parsing’) and be as careful as possible when parsing them -> ’islands of certainty’ • ’islands of certainty’ -> do not reject these parses even if they do not fit into a global parse -> robustness • When you are almost sure of how to resolve an ambiguity, do it! -> determinism • When you are uncertain of how to resolve an ambiguity, don’t even try! -> ’containment of ambiguity’ -> determinism • determinism -> no search -> speed FST - Torbjörn Lager, UU

  39. Shallow syntax • analyses less complete than conventional parser output • identifies some phrasal constituents (e.g. NPs), without indicating their internal structure and their function in the sentence. • or identifies the functional role of some of the words, such as the main verb, and its direct arguments. FST - Torbjörn Lager, UU

  40. Deterministic bottom-up parsing • Adapted from Karttunen 1996: define NP [(d) a* n+] ; regex NP @-> “[NP” ... “]” .o. v “[NP” NP “]” @-> “[VP” ... “]” ; apply down dannvaan [NP dann][VP v [NP aan]] • Note the use of the longest-match operator! FST - Torbjörn Lager, UU

  41. FST - Torbjörn Lager, UU

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