Named Entity Recognition for Daninsh in a CG

Скачать презентацию Named Entity Recognition for Daninsh in a CG

f3853f0b0a245b5a52d0152e712e50ba.ppt

Количество слайдов: 36

Named Entity Recognition for Daninsh in a CG framework Eckhard Bick Southern Denmark University lineb@hum. au. dk

Topics • Dan. Gram system overview • Distributed NER techniques: - pattern matching - lexical - CG-rules • Evaluation and outlook

System Structure

The Dan. Gram system in current numbers Lexemes in morphological base lexicon: 146. 342 (equals about 1. 000 full forms), of these: proper names: 44839 (experimental) polylexicals: 460 (+ names and certain number expressions) Lexemes in the valency and semantic prototype lexicon: 95. 308 Lexemes in the bilingual lexicon (Danish-Esperanto): 36. 001 Danish CG-rules, in all: 7. 400 morphological CG disambiguation rules: 2. 259 + 137 = 2. 396 syntactic mapping-rules: 2. 250 syntactic CG disambiguation rules: 2. 205 NER-module: 432 Attachment-CG: 117 (plus 429 bilingual rules in separate MT grammars) Danish PSG-rules: 605 (for generating syntactic tree structures) Performance: At full disambiguation (i. e. , maximal precision), the system has an average correctness of 99% for word class (Po. S), and about 95% for syntactic function tags (depending, on how fine grained an annotation scheme is used). From raw news text, 50%-70% of sentences produce well-formed syntactic trees. Speed: full CG-parse: ca. 400 words/sec for larger texts (start up time 3 -6 sec) morphological analysis alone: ca. 1000 words/sec

Running CG-annotation Da (When) den (the) gamle (old) sælger (salesman) kørte (drove) hjem (home) i (in) sin (his) bil (car) , kunne (could) han (he) se (see) mange (many) rådyr (deer) på (in) de (the) våde (wet) marker (fields) [da] [den] [gammel] [sælger] [køre] [hjem] [i] [sin] [bil] KS ART UTR S DEF ADJ n. G S DEF NOM N UTR S IDF NOM V IMPF AKT N NEU P IDF NOM PRP DET UTR S N UTR S IDF NOM @SUB @>N @SUBJ> @FS-ADVL> @N @P< [se] [han] [se] [mange] [rådyr] [på] [den] [våd] [mark] V IMPF AKT PERS UTR 3 S NOM V INF AKT DET n. G P NOM N NEU P IDF NOM &ACI-SUBJ PRP ART n. G P DEF ADJ n. G P n. D NOM N UTR P IDF NOM @FAUX @N @N @P<

Constituent trees (high level)

NER target classes Feature bundling in the major name categories (synopsis)

1. NE string recognition at raw text level, pattern based dancorp. avis, dancorp. pre, dan. pre 1. 1. Name format Recognition of author and source names (scanning & corpus impurities): København N. Førerbevis til ældre LUXEMBORG I over 700 år har. . . RADIO & TV Københavns Sommerunderholdning Bedste da. resultat er opnået af Kurt Nielsen, der kom i finalen i 1953 og 1955. Ku. Ni INFORMATION. Det er ikke sandt. . . Kontokort Af BENNY SELANDER Stadig flere. . . HELGE ADAM MØLLER Medlem af folketinget Headline separation problems: Glimrende rolle Det er en. . . Forholdet til Walesa "Mit forhold til Walesa er, som det var for ti år siden, " siger. . . Uppercase highlighting vs. Abbrebiations MENS børnene venter, JOURNALIST Michael Larsen NATO, UNPROFOR, USA (organisations), VHS (brand), DNA (chemical) number of letters? Author position? Key small words? lower-casing after triage

1. 2. Name pattern recognition Main principle: Fuse upper case strings person names: Nyrup=Rasmussen institutions: Odder=Lille=Friskole organisations: ABN-AMRO=Asia=Equities events: Australian=Open Name chaining particles: prepositions, coordinators, … Personal names: Maria dos Santos, Paul la Cour, Peter the Great, Margarete den Anden, Ras al Kafji, Osama bin Laden, initially: da Vinci, van Gogh, de la Vega, ten Haaf, von Weizsäcker organisation names: Dansk Selskab for Akupunktur, University of Michigan, Golf-Centeret for Strategiske Studier, Organisationen af Olieeksporterende Lande place names: Place de la Concorde brands: Muscat de Beaumes de Venise media: le Nouvel Observateur events: Slaget på Reden Mid-sentence name chain initiators: Det ny Lademann Aps.

1. 3. Name-internal punctuation as opposed to abbreviations and clause punctuation • • · initials: P. =Rostrup=Bøjesen, Carl=Th. =Pilipsen · web-addresses: http: //www. corp. hum. sdu. dk · e-mails: lineb@hum. au. dk · personal name additions: Mr. =Bush, frk. =Nielsen, Bush=jr. /sr. , hr. =Jensen, . . . · professional titles: Dr. =A. =Clarke, cand. =polit. , mag. =art. · company names: Aps. , D/S=Isbjørn · vehicles: H. M. S. =Polaris · geographicals: Nr. =Nissum, Kbh. =K, St. =Bernhard. Mt. =Everest

1. 4. Name-internal numerals • • • · yearly events: Landsstævne='98 · card names: Ru=9, Sp=E · license plates: TF=34=322 · town adresses: 8260=Viby=J · house addresses: 14 a, =st. =tv. · kings: Christian IV · dated or versionized products: Windows 98 · vehicle names: Honda Civic 1, 4 GL sedan, Citroën ZX Aura 1. 6 i, Peugeot 206, 1. 6 LX van, 1, 9 TDI, DC 10 fly, købe 50 V 44 vindmøller · news channels: TV 2, DR 1, Channel 4 · bible quotes: Mt 28, 1 -10

1. 5. In-name '&' and '/' or coordination? • • • K/S Storkemarken Munster & Co. , Møller & Baruah (fused company names? ) Hartree & V. Booth: Safety in Biological Laboratories (separate authors? ) NATO/FN 1560 k. J/420 kcal 1. 6. In-name apostrophe or quote? Quotes make title-recognition easier … Med den melankolske 'Light Years' anslås. . . Filmen "Stay Cool" blev trukket. . . Han havde læst Den uendelige historie. … but create their own problems: • · genitive: 'The Artist's Album', Bush' korte besked. 'Big Momma's House' • · company names: Kellogg's • · ellision: Pic du Midi d'Osseau, Côte d'Azur, Montfort l'Amaury • · fixed naming systems: O'Connor, O'Neill

2. NE string recognition at raw text level, lexicon supported dan. pre 2. 1. Low level lexicon: specialised word contexts • • • Left or right context: selskab/forening/institut …. For Name-internal 'og' vs. Coordination: (a) pattern: Petersen & Co. (b)lexical governed: Told og Skat, Sol og Strand, Se og Hør. NOT name integrating numbers: (a) car companies: Peter købte en Peugeot=206. * Den gang købte Peter=206 pakkegaver. (b) unit words to the right: . . . tjente Toyota 6, 8 procent tallet for Europa er 60 procent og for USA 75 procent

2. 2. Sentence initial non-name small words (also used for sentence separation) • fordi, derfor, siden, ifølge, har Fordi=Peter=Jensen ikke havde sendt… Fordi Peter=Jensen ikke havde sendt … 2. 3. Words, prefixes and suffixes with <+name> valency: • adjunkt, . . . chef, historiker, institut, kollega, . . . erske, . . . trice, virksomhed, . . . ør, Hoved. . . , Vice. . lektor i børsret ved Københavns Universitet # Per Schaumburg Müller • • Landsstyreformand # Jonathan Motzfeldt Styresystemet # Windows Stillehavsøen # Okinawa Vicelagmand # Oli Nilsson

2. 4. High level lexicon: A lexicon based chunk splitter • • • Old alternative: split [A-Z]…erne + uppercase, [A-Z]…ede + uppercase with negative list of "forbidden words": Horsens, Jens, Vincens, Enschede New alternative: check all potential substrings of a polylexical name candidate, AND the whole string, against the full name lexicon (44. 000 entries) Genitive splitting: allowed if first half is , , , (= humanoids) Sonofons # GSM 900 -net Richard Strauss' # Zarathustra New Yorks # Manhattan Beach Boys' # Brian Wilson Refuse split genitives with certain second half geographicals: Jensens Plads, Rådmans Boulevard Variable split points (more lexicon checks necessary than in genitives): Kommende ambassadør i Kairo # Christian Oldenburg Bagefter hentede Peter # Maria Så ansatte IBM # Kevin Mondale Derfor forlod Jensen (Peter Jensen) # (FC) København

3. NE word recognition, morphological analyzer with lexical data and compositional rules Dantag, danpost 3. 1. Recognized names Full lexicon match ? Partial lexicon match ? 6 major & 20 minor categories: person names organisations place names events semantic product names brands, objects e. g. known first, unknown second name known company name with geographic extension Morten Kaminski, Toshiba Denmark 1. Name – other: Hans, Otte 2. Name NOM – Name GEN Ambiguity 3. Cross type: Lund (place/person), Audi (company/vehicle) 4. Systematic/underspecified: = /

3. 2. Compositional analysis No name recognized Lower case run Full inflexional & derivational analysis for all word classes Compositional analysis ANC-kontor N, G 8 -mødet N Talebanstyret N, Martinicocktails N, Marsåret N AB'ernes [AB'er] N, AGF'ere N EU-godkendt ADJ, Heisenberg'ske ADJ Inflected names 1. PROP N: EMSen (N DEF), EMS'en (N DEF) 2. Frozen usage (lexicon): Sovjetunionen, Folketinget (PROP) Heuristics (a) Hyphen : Jean-Pierre Wallez, Blomster-Jensen but: hovedvej Pec-Prizren , Lolland-Falster Al-Qaida , CO-Industri , Jyllands-Posten (b) Iterations with exchanged and omitted '-' and '=': Al=Qaida (c) Heuristic full-string name reading

4. 1. NE semantic type prediction, semi-lexical compositional heuristics Cg 2 adapt. dansk Respects non-heuristic types from the analyzer/lexicon Tries to verify/falsify semi-lexical type analyzes Uses patterns, suffixes, clue-word lists to predict types To prevent interference between individual sections: ordering type predictions (e. g. early, late) iterating certain classes (e. g. ) NOT-conditions quoting partial or overlapping patterns that would indicate other semantic name classes. Prepares cg-level: prediction non-alphabetic characters, in-word capitals, coordinators (og, eller), certain English function words (of), non-human suffixes (-tion) etc.

4. 2. NE semantic type predictor: Patterns e. g. quotes, in-name function words (articles, pronouns etc. ), "semantic things" (-loven, -brev, -song, report, Circulære=, Redegørelse=, Dictionary=. . . ) e. g. -avis, -bladet, -tidende, Ugeskrift=, Kanal=, Channal=, Nyt=. . . e. g. Expedition, -freden, -krig, -krise, =Rundt, Projekt=, Konference=, Slaget= e. g. Boeing/Mercedes/Toyota=, =Combi, =Sedan, HMS=, USS=, M/S=. . . e. g. Macintosh/Phillips/Sanyo=[0 -9], wine types: =Appelation, =Cru, =Sec, Edition, Yamaha/Siemens=, quality markers: =Extra, =de=Luxe, =Ultra. . . e. g. suffixes: -sen, -sson, -sky, -owa, infixes: ibn, van, ter, y, zu, di, abbreviated and part-of-name titles: frk. , hr. , Madame, Mlle, Morbror, jr. , sr. , Mc=, Al=, =Khan e. g. [A. Z][a-z]+(=[a-z]+([ae]ns|ea|is|um|us))+ e. g. =SSR/Republik, =Town/Ville, suffixes: -ager, -borough, -bølle, -dorf, -hausen, -løse, -ville, -polis (a number of these will receive both and tags for later disambiguation e. g. =Bahnhof, =Bakker, =Kirke, =Manor, =Sund, =Prospekt, Islas=, Ciudad=, Gammel=, Lake=, Rio=, Sønder/Vester/Øster/Nørre=, suffixes: -fors, -kanten, -kvarteret, -marken, addresses: -stien, -strasse, -torv, -gade, -vej (the latter are also used by dantag) e. g. in-word capitals: [a-z][A-Z] (Media. Soft), "suffixes": Amba, Gmb. H, A/S, AG, Bros. , & Co. . . , type indicators: =Holding, =Organisation, =Society, =Network, Bank=of=, Banco=d[eiao], K/S, I/S, Klub=, Fonden=, morphological indicators: -con, -com, -ex, -rama, -tech, -soft e. g. =Ambassade, =Airport? , =Børnehave, =Institut, =Universitet, =Bibliotek, =Hotel, Chez=, morphologicals: -eriet, -værk, -handel e. g. -[cpt]am, -[cz]id, -lax, -vent, Retard=, uppercase + number (NO 2, H 2 O) e. g. =Collection, =Samling, Ugens=, cards: Spa? =, Ru=

5. NE word class and case disambiguation, rule and context based dancg. morf (ca 2. 400 rules) Context based decisions are safer than pattern based predictions, and support each other Full valency and semantic class context can be drawn upon Iterated disambiguation creates safer context for more dangerous decisions · <+name> valency of preceding noun: filmen Tornfuglene PROP • · semantic product class in preceding noun: Lynda La Plantes tv-serie "Mistænkt" • · topologicals rather than topological-derived nouns: Amagerbrogade PROP • · establishment NOM (not hum-GEN), if no np-head to the right: vi spiste på Marion's i går • · GEN - GEN and NOM - NOM coordination matches: Peters NOM og Jensen kom kørende • · NOM name readings are discarded in favor of GEN names, if there is an IDF noun or NOM name to the right with only matching prenominals in between: Australiens mest kendte sangere. • · Sentence-initially, names are discarded in favor of verbs and function words, if followed by an np • · non-compound nouns are favoured over heuristic names • · heuristic names are favoured over compound names in a left lower case context • non-heuristic names are favoured over compound or derived nouns sentence-initially or in left upper case context

6. NE chaining, a repair mechanism for faulty NE string recognition at levels (1) and (2) cleanmorf. dan Performs chunking choices too hard or too ambiguous to make before CG: • Fuses Hans=Jensen og Otte=Nielsen, but keeps Hans Porsche and Otte PC'er using CG-recognition of Jensen PROP , Nielsen PROP , Porsche PROP and PC'er N • Fuses PROP and certain semantic N-types, if upper case and so far unrecognized: PROP + N -> PROP : Betty=Nansen Broen PROP + N -> PROP : Betty=Nansen Foreningen PROP + N -> PROP : Betty=Nansen Prisen • Repairs erroneous PROP splitting by the preprocessor, if later contextual typing asks for fusion: PROP + PROP : Dansk=Røde=Kors Afrika PROP + PROP : Danmarks Monetære Institut

7. NE function classes, mapped and disambiguated by context based rules dancg. syn (ca. 4. 400 rules) Handles, among other things, the syntactic function and attachment of names. The following are examples of functions relevant to the subsequent type mapper: (i) @N< (nominal dependents) præsident Bush, filmen "The Matrix" (ii) @APP (identifying appositions) Forældrebestyrelsens forman, Kurt Chistensen, anklager borgmester. . . (iii) @N

8. NE semantic types, mapped and disambiguated by context based rules dancg. prop (428 rules) (a) (b) Type mapper (introduces ambiguity, instantiates earlier tags) Type disambiguator (reduces ambiguity) • Uses the same 6 major and 20 subcategories used by the lexicon and pattern based name predictor Draws on syntactic relations, sentence context and lexical knowledge Can override previously assigned type readings Can disambiguate previously ambiguous readings • • •

8. 1 Cross-nominal prototype transfer • • • Post-nominal attachment: i byen Rijnsburg MAP () TARGET (PROP @N<) (-1(N NOM) LINK 0 N-TOP) ; Missing hyphen: Uppenskij katedralen MAP () TARGET (PROP) (1 @N) (0 @SUBJ>) (*1 @MV LINK 0 LINK *1 @) (0 NOM) (*1 ( INDP @SUBJ>) BARRIER NON-KOMMA LINK *1 VFIN LINK 0 @FS-N< LINK -1 ALL LINK *1 @MV LINK 0 LINK *1 @ LINK *1 @MV LINK 0 ("kalde" AKT) LINK *1 N-TOP BARRIER NON-PREN/ADV LINK 0 @ overrides previous MAP (%tit) TARGET (PROP NOM) (0 @P< OR @AS<) (-1 ("som") LINK 0 @N< OR @AS-N<) (-2 N-SEM) (NOT -2 N-HUM) ;

8. 2 Coordination based type inference 1. Maps "close coordinators" (&KC-CLOSE): ADD (&KC-CLOSE) TARGET (KC) (*1 @SUBJ> BARRIER @NON->N) (-1 @SUBJ>) 2. Then uses this tags in disambiguation rules: e. g. Arafat @SUBJ> og hans Palæstinas=Selvstyre @SUBJ> REMOVE %non-h (0 %hum-all) (*-1 &KC-CLOSE BARRIER @NON->N LINK -1 C %hum OR N-HUM LINK 0 NOM); SELECT () (1 &KC-CLOSE) (*2 C BARRIER @NON->N) ; 3. Danish has -only and pronouns: SELECT %hum (0 @SUBJ>) (1 KC) (2 ("han" GEN) OR ("hun" GEN)) (*3 @SUBJ> BARRIER @NON->N/KOMMA) ; # Hejberg og hans skole REMOVE %hum (0 @SUBJ>) (1 KC) (2 ("den" GEN) OR ("det" GEN)) (*3 @SUBJ> BARRIER @NON->N/KOMMA) ; # Anden Verdenskrig og dens mange slag

8. 3 PP-contexts • • Word-specific narrow context MAP () TARGET (PROP) (-1 ("for" PRP)) (-2 ("syd") OR ("vest") OR ("nord") OR ("øst")) ; Np-level vs. Clause level function ADD () TARGET (PROP @P<) (-1 ("i" PRP)) (NOT -1 @PIV) (NOT -2 <+i>) ; (safe, early rule) REMOVE () (0 @P<) (-1 ("i" PRP)) (-2 (<+i>)); (heuristic, later rule) Pp-attachment inference, class based: godt 40 km fra Madras REMOVE %non-top (-1 ("fra" PRP) OR ("til" PRP)) (-2 N-DIST) (-3 NUM) ; Pp-attchment inference, word list based MAP (%org) TARGET (PROP NOM @P<) (-1 ("i" PRP)) (-2 ("afdelingsleder") OR ("ansat") OR ("chef") OR ("direktør") OR ("forvaltningschef") OR ("koordinator") OR ("personalechef") OR ("souschef")) (NOT 0 OR ) ;

8. 4 Genitive mapping • • • MAP (%org) TARGET (GEN @>N) (*1 (N IDF) BARRIER @NON->N LINK 0 GEN-ORG) (NOT 0 OR OR OR OR ) ; # Microsofts generalforsamling/aktiekurs ("hard" GEN-ORG set) MAP (%org) TARGET (GEN @>N) (*1 (N IDF) BARRIER @NON->N LINK 0 GEN-ORG/HUM) (NOT 0 OR OR OR OR OR ) ; # Microsofts/ Bill=Gates advokat/hjemmeside ("soft" GEN-ORG set) REMOVE %non-h (0 GEN LINK 0 %h) (*1 N BARRIER @NON>N/KOMMA LINK 0 (
) OR ()) ; # owning thoughts and "thought products". %non-h respects also "humanoids", , etc.

8. 5 Prenominal context: Using adjective classes Uses semantic adjective classes, e. g. 1. Type based, more general, less safe: LIST ADJ-HUM = ; 2. Word based, more specific and safer: LIST ADJ-HUM& = "adfærdsvanskelig" "adspredt" "affektlabil" "afklaret" "afmægtig" "afslappet" "afstumpet" "afvisende" "agtbar" "agtpågivende" "agtsom" "alert" "alfaderlig" "alkærlig" "altopgivende" "altopofrende" "alvorsfuld". . MAP (%hum) TARGET ( PROP NOM) (-1 AD LINK 0 ADJHUM&) (*-2 (ART S DEF) BARRIER @NON->N) ; # Den langlemmede Kanako=Yonekura ADD (%hum) TARGET ( PROP NOM) (-1 AD LINK 0 ADJHUM) (*-2 (ART S DEF) BARRIER @NON->N) ; # Den langlemmede Kanako=Yonekura

Evaluation CG-annotation for Danish news text Recall Precision F-score All word classes[1] 98. 6 98. 7 98. 65 All syntactic functions 95. 4 94. 6 94. 9 [1] Verbal subcategories (present PR, past IMPF, infinitive INF, present and past participle PCP 1/2) and pronoun subcategories (inflecting DET, uninflecting INDP and personal PERS) were counted as different Po. S.

Performance statistics Korpus 90

Performance statistics Korpus 2000

Cross-class and class-internal name type errors

Comparisons • LTG (Mikheev et. al. 1998) achieved an overall F-measure of 93. 39, using hybrid techniques involving both probabilistics/HMM, name/suffix lists and sgml-manipulating rules • MENE (Borthwick et. al. 1998), maximum entropy training: in-domain/same-topic F-scores of up to 92. 20 97. 12% for a hybrid system integrating other MUC-7 -systems cross-topic formal test, F-scores: 84. 22 (pure MENE), 92 (hybrid MENE) possible weakness of trained systems: heavy training data bias? Korpus 90/2000, which was used for the evaluation of the rule based system presented here, is a mixed-genre corpus, even its newstexts are highly cross-domain/cross-topic, since sentence order has been randomized for copyright reasons.

What is it being used for? • Enhance ordinary grammatical analysis - noun-disambiguation - semantic selection restriction fillers • Corpus research on names • Enhance IR-systems: e. g. Question-answering

Outlook • Future direct comparison might corroborate the intuition that a handcrafted system is less likely to have a domain/topic bias than automated learning systems with limited training data. • Balancing strengths and weaknesses, future work should also examine to which degree automated learning / probabilistic systems can interface with or supplement Constraint Grammar based NER systems • For large chunks, Text/Discourse based memory should be used for name type disambiguation, so clear cases and a majority vote could determine the class of unknown names • With a larger window of analysis, anaphora resolution across sentence boundaries might help NER ("human" pronouns, definite np's, …)

Where to reach us: http: //beta. visl. sdu. dk - http: //corp. hum. sdu. dk