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)

5. ANALYSIS OF CONJUNCTIONS
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.