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The Rule-based Parser of the NLP Group of the University of Torino

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1 The Rule-based Parser of the NLP Group of the University of Torino
Leonardo Lesmo Dipartimento di Informatica and Centro di Scienze Cognitive, Università di Torino, Italy

2 Goals Approach Wide-coverage tool Domain-independence
Extensibility to semantics Approach Manually developed rules Two phases: Chunking and subcategorization Procedural analysis of conjunctions and of identification of verbal dependents

3 TULE (Turin University Linguistic Environment)
TOKENIZER Tokens Text Token Automaton Splits the text into words, numbers, punctuation marks DICTIONARY LOOKUP Sets of lexical items Morphological dictionary Suffix tables Extracts all lexical interpretations of each token POS TAGGER Tagging rules Chooses one lexical interpretation Lexical items DEPENDENCY PARSER Parse Tree Parsing rules Verbal Caseframes Establishes the connections between lexical items

4 The grammar Rule-based dependency grammar
Chunking (non-verbal groups) + verbal subcategorization frames Output: a projective tree represented as pointers to parents, including some null elements (understood items – e.g. pro-drop - and traces)

Parser Architecture Lexical Items Splits the text into groups of strictly connected words Chunking rules CHUNKING Chunked text Connects chunks linked by conjunctions, to form larger chunks Procedural preference rules 1 ANALYSIS OF CONJUNCTIONS Chunked text Procedural preference rules 2 Determines the dependents of verbs SEGMENTATION Lexical items Verb classes VERBAL ATTACHMENT Determines the role (arc labels) of the verbal dependents Verbal Caseframes Parse Tree

6 An example Lexical Items Parse Tree Infos
Example: Slitta a Tirana la decisione sullo stato di emergenza. (The decision on the emergency status in Tirana has been delayed) 1 Slitta (SLITTARE VERB MAIN IND PRES INTRANS 3 SING) 2 a (A PREP MONO) 3 Tirana (TIRANA NOUN PROPER F SING ££CITY) 4 la (IL ART DEF F SING) 5 decisione (DECISIONE NOUN COMMON F SING DECIDERE INTRANS) 6 sullo ((SU PREP MONO) (IL ART DEF M SING)) 7 stato (STATO NOUN COMMON M SING) 8 di (DI PREP MONO) 9 emergenza (EMERGENZA NOUN COMMON F SING) (#\. PUNCT) Lexical Items [0;TOP-VERB] [1;PREP-RMOD] [2;PREP-ARG] [1;VERB-SUBJ] [4;DET+DEF-ARG] [5;PREP-RMOD] [6;PREP-ARG] [6.10;DET+DEF-ARG] [7;PREP-RMOD] [8;PREP-ARG] [1;END] Parse Tree Infos 1: Slitta Prep-rmod 2: a Verb-subj 4: la 3: Tirana Prep-arg 5; decisione Det+def-arg 6: su 6.10: lo Stato di emergenza

7 Chunking Chunking Rules
Example: Puoi dirmi che spettacoli di cabaret posso vedere domani? (Can you tell me what cabaret plays I can see tomorrow?) PuoiV-modal-2nd-sing-pres dirV-inf [miPron-1st-dative]Pron [cheAdj-interr spettacoliNoun [diPrep cabaretNoun]P-group ]N-group possoV-modal-1st-sing-pres vedereV-inf [domaniAdv]A-group? Chunking Rules Chunking rules are grouped in packets. Each packet is associated with a lexical category, and describes the “chunkable” possible dependents of words of that category. Chunkable means a dependent handled during chunking (e.g. auxiliaries, but not arguments of verbs)

8 A chunk rule (NOUN common (precedes (ADJ qualif T (#\- #\' #\"))
Packet (governing word) feature (constrains applicability) Position of dep (and possible words separating head from dep) (NOUN common (precedes (ADJ qualif T (#\- #\' #\")) (ADJ ((type qualif) (agree))) ADJC+QUALIF-RMOD)) Category of possible dep (and constraints on it) Label of connecting arc

9 Conjunctions HoV-aux incontratoV-main
When a coordinating conjunction is found, all following and preceding chunks are collected All pairs are built, and the best one is chosen according to criteria based on structural similarity and distance Special treatment for verbs Example: Ho incontrato Marco e Lucia e li ho salutati (I met Marco e Lucia and I greeted them) HoV-aux incontratoV-main [MarcoNoun-Proper]Noun eConj-coord [LuciaNoun-Proper]Noun eConj-coord [liPron-pers ]Pron hoV-aux salutatiV-main

10 Segmentation For each verb (going from left to right): Look for possible dependents (on its right and left) On the left, the search is blocked from the previous verb On the right, some “barriers” are defined to stop the search (for instance, a subordinating conjunction acts as a barrier) PuoiV-modal-2nd-sing-pres { dirV-inf [miPron-1st-dative]Pron {[cheAdj-interr spettacoliNoun [diPrep cabaretNoun]P-group ]N-group possoV-modal-1st-sing-pres {vedereV-inf [domaniAdv]A-group? } } } }

11 Verbal Subcategorization
The subcategorization classes: verbs nosubj-verbs subj-verbs obj-verbs basic-trans empty-modal modal ssubj-inf-verbs trans indobj-verbs trans-indobj subcategorization classes bisognare camminare dovere dictionary potere need walk must can

12 Example subcategorization class definitions:
(subj-verbs (intrans) (verbs) ; *** verbs with a subject. Definition of subject ( verb-subj ((noun (agree)) (art (agree)) (pron (not (word quale) (type relat)) (case lsubj) (agree)) (adj (type (indef demons deitt interr poss)) (agree)) (num (agree)) (prep (word in) (down (cat pron) (type indef)) (agree))))) (ssubj-inf-verbs () (verbs) ; *** verbs with an inf-verb sentential subject ( verb-subj ((verb (mood infinite) (agree))))) (empty-modal () (no-subj-verbs) ; *** modals without subject ( verb-indcompl-modal ((verb (mood infinite)))))

13 Example transformation:
Transformations: basic class (e.g. trans) transformed classes (e.g. trans, trans+passivization, trans+infinitivization, trans+prodrop, trans+passivization+infinitivization, ….. ) Example transformation: (infinitivization replacing (subj-verbs) (is-inf-form tr-verb v-casefr) (cancel-case s-subj))

14 Base Subcategorization
Some statistics Chunking rules Total: 295 rules Common: 250 rules English: 34 rules Italian: 7 rules Spanish + Catalan: 4 rules Base Subcategorization Total: 118 classes Abstract: 21 classes plus verbal locutions Italian: 40 classes English: 1 class Derived surface case frames 2653 case frames

15 Conclusions Test of the parser on other languages, using the same grammar augmented with extra rules (see previous slide) Partial use of semantic information (about 400 words classified according to a semantic taxonomy) The parser has been used in a project involving spoken and written linguistic interaction with a user. It has been interfaced with an repository of semantic knowledge to build a meaning representation.

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