15,204 research outputs found
Automatic treebank-based acquisition of Arabic LFG dependency structures
A number of papers have reported on methods for the automatic acquisition of large-scale, probabilistic LFG-based grammatical resources from treebanks for English (Cahill and al., 2002), (Cahill and al., 2004), German (Cahill and al., 2003), Chinese (Burke, 2004), (Guo and al.,
2007), Spanish (O’Donovan, 2004), (Chrupala and van Genabith, 2006) and French (Schluter and van Genabith, 2008). Here, we extend the LFG grammar acquisition approach to Arabic and the Penn Arabic Treebank (ATB) (Maamouri and
Bies, 2004), adapting and extending the methodology
of (Cahill and al., 2004) originally developed for English. Arabic is challenging because of its morphological richness and syntactic complexity.
Currently 98% of ATB trees (without FRAG and X) produce a covering and connected f-structure.
We conduct a qualitative evaluation of our annotation
against a gold standard and achieve an f-score of 95%
Message-Passing Protocols for Real-World Parsing -- An Object-Oriented Model and its Preliminary Evaluation
We argue for a performance-based design of natural language grammars and
their associated parsers in order to meet the constraints imposed by real-world
NLP. Our approach incorporates declarative and procedural knowledge about
language and language use within an object-oriented specification framework. We
discuss several message-passing protocols for parsing and provide reasons for
sacrificing completeness of the parse in favor of efficiency based on a
preliminary empirical evaluation.Comment: 12 pages, uses epsfig.st
Interaction Grammars
Interaction Grammar (IG) is a grammatical formalism based on the notion of
polarity. Polarities express the resource sensitivity of natural languages by
modelling the distinction between saturated and unsaturated syntactic
structures. Syntactic composition is represented as a chemical reaction guided
by the saturation of polarities. It is expressed in a model-theoretic framework
where grammars are constraint systems using the notion of tree description and
parsing appears as a process of building tree description models satisfying
criteria of saturation and minimality
Concurrent Lexicalized Dependency Parsing: The ParseTalk Model
A grammar model for concurrent, object-oriented natural language parsing is
introduced. Complete lexical distribution of grammatical knowledge is achieved
building upon the head-oriented notions of valency and dependency, while
inheritance mechanisms are used to capture lexical generalizations. The
underlying concurrent computation model relies upon the actor paradigm. We
consider message passing protocols for establishing dependency relations and
ambiguity handling.Comment: 90kB, 7pages Postscrip
Data-Oriented Language Processing. An Overview
During the last few years, a new approach to language processing has started
to emerge, which has become known under various labels such as "data-oriented
parsing", "corpus-based interpretation", and "tree-bank grammar" (cf. van den
Berg et al. 1994; Bod 1992-96; Bod et al. 1996a/b; Bonnema 1996; Charniak
1996a/b; Goodman 1996; Kaplan 1996; Rajman 1995a/b; Scha 1990-92; Sekine &
Grishman 1995; Sima'an et al. 1994; Sima'an 1995-96; Tugwell 1995). This
approach, which we will call "data-oriented processing" or "DOP", embodies the
assumption that human language perception and production works with
representations of concrete past language experiences, rather than with
abstract linguistic rules. The models that instantiate this approach therefore
maintain large corpora of linguistic representations of previously occurring
utterances. When processing a new input utterance, analyses of this utterance
are constructed by combining fragments from the corpus; the
occurrence-frequencies of the fragments are used to estimate which analysis is
the most probable one.
In this paper we give an in-depth discussion of a data-oriented processing
model which employs a corpus of labelled phrase-structure trees. Then we review
some other models that instantiate the DOP approach. Many of these models also
employ labelled phrase-structure trees, but use different criteria for
extracting fragments from the corpus or employ different disambiguation
strategies (Bod 1996b; Charniak 1996a/b; Goodman 1996; Rajman 1995a/b; Sekine &
Grishman 1995; Sima'an 1995-96); other models use richer formalisms for their
corpus annotations (van den Berg et al. 1994; Bod et al., 1996a/b; Bonnema
1996; Kaplan 1996; Tugwell 1995).Comment: 34 pages, Postscrip
Treebank-based acquisition of wide-coverage, probabilistic LFG resources: project overview, results and evaluation
This paper presents an overview of a project to acquire wide-coverage, probabilistic Lexical-Functional Grammar
(LFG) resources from treebanks. Our approach is based on an automatic annotation algorithm that annotates “raw” treebank trees with LFG f-structure information approximating to basic predicate-argument/dependency structure. From the f-structure-annotated treebank
we extract probabilistic unification grammar resources. We present the annotation algorithm, the extraction of
lexical information and the acquisition of wide-coverage and robust PCFG-based LFG approximations including
long-distance dependency resolution.
We show how the methodology can be applied to multilingual, treebank-based unification grammar acquisition. Finally
we show how simple (quasi-)logical forms can be derived automatically from the f-structures generated for the treebank trees
Arabic parsing using grammar transforms
We investigate Arabic Context Free Grammar parsing with dependency annotation comparing lexicalised and unlexicalised parsers. We study how morphosyntactic as well as function tag information percolation in the form of grammar transforms (Johnson, 1998, Kulick et al., 2006) affects the performance of a parser and helps dependency assignment. We focus on the three most frequent functional
tags in the Arabic Penn Treebank: subjects, direct objects and predicates . We merge these functional tags with their phrasal categories and (where appropriate) percolate case information to the non-terminal (POS) category to train the parsers. We then automatically enrich the output of these parsers with full dependency information in order to annotate trees with Lexical Functional Grammar (LFG)
f-structure equations with produce f-structures, i.e. attribute-value matrices approximating to basic predicate-argument-adjunct structure representations. We present a series of experiments evaluating how well lexicalized, history-based, generative (Bikel) as well as latent
variable PCFG (Berkeley) parsers cope with the enriched Arabic data. We measure quality and coverage of both the output trees and the generated LFG f-structures. We show that joint functional and morphological information percolation improves both the recovery of trees as well as dependency results in the form of LFG f-structures
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