3,435 research outputs found
Morphological Disambiguation by Voting Constraints
We present a constraint-based morphological disambiguation system in which
individual constraints vote on matching morphological parses, and
disambiguation of all the tokens in a sentence is performed at the end by
selecting parses that receive the highest votes. This constraint application
paradigm makes the outcome of the disambiguation independent of the rule
sequence, and hence relieves the rule developer from worrying about potentially
conflicting rule sequencing. Our results for disambiguating Turkish indicate
that using about 500 constraint rules and some additional simple statistics, we
can attain a recall of 95-96% and a precision of 94-95% with about 1.01 parses
per token. Our system is implemented in Prolog and we are currently
investigating an efficient implementation based on finite state transducers.Comment: 8 pages, Latex source. To appear in Proceedings of ACL/EACL'97
Compressed postscript also available as
ftp://ftp.cs.bilkent.edu.tr/pub/ko/acl97.ps.
A Machine learning approach to POS tagging
We have applied inductive learning of statistical decision trees
and relaxation labelling to the Natural Language Processing (NLP)
task of morphosyntactic disambiguation (Part Of Speech Tagging).
The learning process is supervised and obtains a language
model oriented to resolve POS ambiguities. This model consists
of a set of statistical decision trees expressing distribution of
tags and words in some relevant contexts.
The acquired language models are complete enough to be directly
used as sets of POS disambiguation rules, and include more complex
contextual information than simple collections of n-grams usually
used in statistical taggers.
We have implemented a quite simple and fast tagger that has been
tested and evaluated on the Wall Street Journal (WSJ) corpus with
a remarkable accuracy.
However, better results can be obtained by translating the trees
into rules to feed a flexible relaxation labelling based tagger.
In this direction we describe a tagger which is able to use
information of any kind (n-grams, automatically acquired constraints,
linguistically motivated manually written constraints, etc.), and in
particular to incorporate the machine learned decision trees.
Simultaneously, we address the problem of tagging when only
small training material is available, which is crucial in any process
of constructing, from scratch, an annotated corpus. We show that quite
high accuracy can be achieved with our system in this situation.Postprint (published version
Inducing Constraint Grammars
Constraint Grammar rules are induced from corpora. A simple scheme based on
local information, i.e., on lexical biases and next-neighbour contexts,
extended through the use of barriers, reached 87.3 percent precision (1.12
tags/word) at 98.2 percent recall. The results compare favourably with other
methods that are used for similar tasks although they are by no means as good
as the results achieved using the original hand-written rules developed over
several years time.Comment: 10 pages, uuencoded, gzipped PostScrip
CHR Grammars
A grammar formalism based upon CHR is proposed analogously to the way
Definite Clause Grammars are defined and implemented on top of Prolog. These
grammars execute as robust bottom-up parsers with an inherent treatment of
ambiguity and a high flexibility to model various linguistic phenomena. The
formalism extends previous logic programming based grammars with a form of
context-sensitive rules and the possibility to include extra-grammatical
hypotheses in both head and body of grammar rules. Among the applications are
straightforward implementations of Assumption Grammars and abduction under
integrity constraints for language analysis. CHR grammars appear as a powerful
tool for specification and implementation of language processors and may be
proposed as a new standard for bottom-up grammars in logic programming.
To appear in Theory and Practice of Logic Programming (TPLP), 2005Comment: 36 pp. To appear in TPLP, 200
Using Multiple Sources of Information for Constraint-Based Morphological Disambiguation
This thesis presents a constraint-based morphological disambiguation approach
that is applicable to languages with complex morphology--specifically
agglutinative languages with productive inflectional and derivational
morphological phenomena. For morphologically complex languages like Turkish,
automatic morphological disambiguation involves selecting for each token
morphological parse(s), with the right set of inflectional and derivational
markers. Our system combines corpus independent hand-crafted constraint rules,
constraint rules that are learned via unsupervised learning from a training
corpus, and additional statistical information obtained from the corpus to be
morphologically disambiguated. The hand-crafted rules are linguistically
motivated and tuned to improve precision without sacrificing recall. In certain
respects, our approach has been motivated by Brill's recent work, but with the
observation that his transformational approach is not directly applicable to
languages like Turkish. Our approach also uses a novel approach to unknown word
processing by employing a secondary morphological processor which recovers any
relevant inflectional and derivational information from a lexical item whose
root is unknown. With this approach, well below 1% of the tokens remains as
unknown in the texts we have experimented with. Our results indicate that by
combining these hand-crafted, statistical and learned information sources, we
can attain a recall of 96 to 97% with a corresponding precision of 93 to 94%,
and ambiguity of 1.02 to 1.03 parses per token.Comment: M.Sc. Thesis submitted to the Department of Computer Engineering and
Information Science, Bilkent University, Ankara, Turkey. Also available as:
ftp://ftp.cs.bilkent.edu.tr/pub/tech-reports/1996/BU-CEIS-9615ps.
Treebank-based acquisition of LFG parsing resources for French
Motivated by the expense in time and other resources to produce hand-crafted grammars, there has been increased interest in automatically obtained wide-coverage grammars from treebanks for natural language processing. In particular, recent years have seen the growth in interest in automatically obtained deep resources that can represent information absent from simple CFG-type structured treebanks
and which are considered to produce more language-neutral linguistic representations, such as dependency syntactic trees. As is often the case in early pioneering work on natural language processing, English has provided the focus of first efforts towards acquiring deep-grammar resources, followed by successful treatments of, for example, German, Japanese, Chinese and Spanish. However, no comparable large-scale automatically acquired deep-grammar resources have been obtained for French to date. The goal of this paper is to present the application of treebank-based language acquisition to the case of French. We show that with modest changes to the established parsing architectures, encouraging results can be obtained for French, with a best dependency structure f-score of 86.73%
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