1. Integrating Finite-state Morphologies with Deep LFG Grammars Tracy Holloway King

2. FST and deep grammars Finite state tokenizers and morphologies can be integrated into deep processing systems Integrated tokenizers –eliminate the need for preprocessing –allow the grammar writer more control over the input Morphologies –eliminate the need to list (multiple) surface forms in the lexicon –eliminate the need for lexical entries for words with predictable subcategorization frames

3. Talk outline Basic integrated system Integrating morphology FSTs Interaction of tokenization and morphology

4. Basic Architecture (Shallow markup) Tokenizing FSTs Morphology FSTs LFG grammar and lexicons Constituent-structure (tree) Functional-structure (AVM) Input string

5. Example steps through the system Input string: Boys appeared. Tokenizing: boys TB appeared TB. TB Morphology: boy + Noun +Pl appear +Verb +PastBoth +123SP. +Punct C-structure/F-structure: next slides

6. C-structure tree

7. F-structure AVM

8. The wider system: XLE Handwritten grammars for various languages –Substantial for English, German, Japanese, Norwegian –Also: Arabic, Chinese, Urdu, Korean, Welsh, Malagasy, Turkish Robustness mechanisms –Fragment grammar rules –Morphological guessers –Skimming when resource limits approached Ambiguity management (packing) –Compute all analyses (no “aggressive pruning”) –Propagate packed ambiguities across processing modules Stochastic disambiguation –MaxEnt models to select from packed (f-)structures Other processing available: –generation, semantics, transfer/rewriting Comparisons to other systems/tasks –Parsing WSJ (Riezler et al, ACL 2002) –Comparison to Collins model 3 (Riezler et al, NAACL 2004)

9. FST Morphologies Associate surface form with –a lemma (stem/canonical form) –a set of tags Process is non-deterministic –can have many analyses for one surface form –grammar has to be able to deal with multiple analyses (morphological ambiguity) –Issue: can the grammar control rampant morphological ambiguity? Arabic vowelless representations

10. Example Morphology Output turnips turnip +Noun +Pl Mary Mary +Prop +Giv +Fem +Sg falls fall +Noun +Pl fall +Verb +Pres +3sg broken break +Verb +PastPerf +123SP broken +Verb +PastPart } +Adj New York New York +Prop +Place +USAState +Prefer New York +Prop +Place +City +Prefer [ plus analyses of New and York ]

11. Morphologies and lexicons Without a morphology, need to list all surface forms in the lexicon –bad for English –horrible for languages like Finnish and Arabic With a morphology, one entry for the stem form go V XLE @(V-INTRANS go). for: go, goes, going, gone, went With additional integration, words with predictable subcategorization frames need no entry

12. Basic idea Run surface forms of words through the morphology to produce stems and tags –MorphConfig file specifies which morphologies the grammar uses Look up stems and tags in the lexicon Sublexical phrase structure rules build syntactic nodes covering the stems and tags Standard grammar rules build larger phrases

13. Lexical entries for tags boys ==> boy +Noun +Pl boy N XLE @(NOUN boy). +Noun N_SFX XLE @(PERS 3) @(EXISTS NTYPE). +Pl NNUM_SFX XLE @(NUM pl).

14. Sublexical rules for tags Build up lexical nodes from stem plus tags Rules are identical to standard phrase structure rules –Except display can hide the sublexical information N --> N_BASE N_SFX_BASE NNUM_SFX_BASE. N N_BASE boy N_SFX_BASE +Noun NNUM_SFX_BASE +Pl

15. Resulting structures N N_BASE boy N_SFX_BASE +Noun NNUM_SFX_BASE +Pl PRED 'boy' PERS 3 NUM pl NTYPE common

16. Lexical entries Stems with unpredictable subcategorization frames need entries –verbs –adjectives with obliques (proud of her) –nouns with that complements (the idea that he laughed) Most lexical items have predictable frames determined by part of speech –common and proper nouns –adjectives –adverbs –numbers

17. -unknown lexical entry Match any stem to the entry Provide desired functional information –%stem will pass in the appropriate surface form (i.e., the lemma/stem) Constrain application via morphological tag possibilities -unknown N XLE @(NOUN %stem); A XLE @(ADJ %stem); ADV XLE @(ADVERB %stem).

18. -unknown example The box boxes. Lexicon entries: box V XLE @(V-INTRANS %stem). -unknown N XLE @(NOUN %stem); ADV…; A... Morphology output: box ==> box +Noun +Sg | +Verb +Non3Sg boxes ==> box +Noun +Pl | +Verb +3Sg Build up four effective lexical entries –1 noun, 1 verb, 1 adverb, 1 adjective –adverb and adjective fail sublexically –noun and verb relevant for the sentence

19. Inflectional morphology summary Integrating FST morphologies significantly decreases lexicon development Verbs and other unpredictable items are listed only under their stem form Predictable items such as nouns are processed via –unknown and never listed in the lexicon

20. Guessers Even large industrial FST morphologies are not complete Novel words usually have regular morphology Build and FST guesser based on this –Words with capital letters are proper nouns (Saakashvili) –Words ending in –ed are past tense verbs or deverbal adjectives Guessed words will go through –unknown –no difference from standard morphological output –can add +Guessed tag for further control

21. Guessers: controlling application Apply guesser in the grammar only if there is no form in the regular morphology –don't guess unless you have to Control this with the MorphConfig –use multiple fst morphologies –stop looking once analysis if found

22. Sample MorphConfig STANDARD ENGLISH MORPHOLOGY (1.0) TOKENIZE: english.tok.parse.fst ANALYZE USEFIRST: english.infl.fst try regular morphology first english.guesser.fst if fail, guess MULTIWORD: english.standard.mwe.fst

23. Multiple morphology FSTs In addition to the regular morphology and guesser, can have other morphologies –morphology for technical terms, part numbers, etc. These can be applied in sequence or in parallel (cascaded or unioned) ANALYZE USEALL: english.infl.fst try regular morphology english.eureka.parts.fst and also part names

24. Morphology vs. surface form System always allows surface form through Lexicon can match this form for –multiword expressions –override/supplement morphological analysis Example: or as adverb (Or you could leave now.) or ADV * @(ADVERB or); CONJ XLE @(CONJ or).

25. Tokenizers Tokenizers break strings (sentences) into tokens (words) Need to (for English): –break off punctuation Mary laughs. ==> Mary TB laughs TB. TB –lower case certain letters The dog ==> the TB dog

26. Tokenization and morphology Linguistic analysis may govern tokenization Are English contracted auxiliaries: –affixes: John'll ==> no tokenization John +Noun +Proper +Fut –clitics: John'll ==> John TB 'll TB John +Noun +Proper will +Fut Arabic determiners and conjunctions –both written with adjacent words determiner as an affix giving +Def (Albint the-girl) conjunction tokenized separately (wakutub and-books)

27. Non-deterministic tokenizers: Punctuation Cannot just break off punctuation and insert a TB Comma haplology Find the dog, a poodle. ==> find TB the TB dog TB, TB a TB poodle TB, TB. TB Period haplology Go to Palm Dr. ==> go TB to TB Palm TB Dr. TB. TB Resulting tokenizer is non-deterministic System must be able to handle multiple inputs

28. Capitalization Intial capitals are optionally lower cased The boy left. ==> the boy left. Mary left. ==> Mary left. Example for both types of non-determinism Bush saw them. ==> { Bush | bush } TB saw TB them TB [, TB]*. TB Tokenization rules vary from language to language and by choice of linguistic analysis

29. Conclusions System architecture integrates FST techniques with deep LFG parsing –tokenizers –morphologies and guessers Allows generalizations to be factored out –properties of words –properties of strings Allows use of existing large-scale lexical resources –avoids redundant speficication System is actively in use in ParGram grammars

31. Shallow Markup Preprocessing with shallow markup can reduce ambiguity and speed processing Tokenizer must be able to process the markup Part of speech tagging: –I/PRP_ saw/VBD_ her/PRP_ duck/VB_. Named entities – General Mills bought it.

32. POS tagging POS tags are not relevant for tokenizing, but the tokenizer must skip them –She walks/VBZ_. should be treated like She walks. The morphology must only insert compatible tags –A mapping table states allowable combinations /VBZ_ +Verb +3sg /NN_ +Noun +Sg –These are encoded into a filtering FST –Only compatible tags are passed to the grammar

33. POS tagging example I saw her duck duck +Noun +Sg duck +Verb +Pres +Non3sg –both possibilities passed to the grammar I saw her duck/VB_. –only +Verb +Pres +Non3sg possibility is compatible with /VB_ POS tag –only this possibility is passed to the grammar

34. Named Entities Named entities appear in text as XML markup General Mills bought it. Tokenizer –creates special tag for these –puts literal spaces instead of TBs –allows version without markup for fallback General Mills TB +NamedEntity TB General TB +Title TB Mills +Proper TB Lexical entry added for +NamedEntity Sublexical N and NAME rules allows the tag

35. Sample Named Entity output