643 research outputs found
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
Data-Oriented Parsing with discontinuous constituents and function tags
Statistical parsers are e ective but are typically limited to producing projective dependencies or constituents. On the other hand, linguisti- cally rich parsers recognize non-local relations and analyze both form and function phenomena but rely on extensive manual grammar development. We combine advantages of the two by building a statistical parser that produces richer analyses.
We investigate new techniques to implement treebank-based parsers that allow for discontinuous constituents. We present two systems. One system is based on a string-rewriting Linear Context-Free Rewriting System (LCFRS), while using a Probabilistic Discontinuous Tree Substitution Grammar (PDTSG) to improve disambiguation performance. Another system encodes the discontinuities in the labels of phrase structure trees, allowing for efficient context-free grammar parsing.
The two systems demonstrate that tree fragments as used in tree-substitution grammar improve disambiguation performance while capturing non-local relations on an as-needed basis. Additionally, we present results of models that produce function tags, resulting in a more linguistically adequate model of the data. We report substantial accuracy improvements in discontinuous parsing for German, English, and Dutch, including results on spoken Dutch
Data-Oriented Parsing with Discontinuous Constituents and Function Tags
Statistical parsers are e ective but are typically limited to producing projective dependencies or constituents. On the other hand, linguisti- cally rich parsers recognize non-local relations and analyze both form and function phenomena but rely on extensive manual grammar development. We combine advantages of the two by building a statistical parser that produces richer analyses. We investigate new techniques to implement treebank-based parsers that allow for discontinuous constituents. We present two systems. One system is based on a string-rewriting Linear Context-Free Rewriting System (LCFRS), while using a Probabilistic Discontinuous Tree Substitution Grammar (PDTSG) to improve disambiguation performance. Another system encodes the discontinuities in the labels of phrase structure trees, allowing for efficient context-free grammar parsing. The two systems demonstrate that tree fragments as used in tree-substitution grammar improve disambiguation performance while capturing non-local relations on an as-needed basis. Additionally, we present results of models that produce function tags, resulting in a more linguistically adequate model of the data. We report substantial accuracy improvements in discontinuous parsing for German, English, and Dutch, including results on spoken Dutch
Hybrid grammars for parsing of discontinuous phrase structures and non-projective dependency structures
We explore the concept of hybrid grammars, which formalize and generalize a range of existing frameworks for dealing with discontinuous syntactic structures. Covered are both discontinuous phrase structures and non-projective dependency structures. Technically, hybrid grammars are related to synchronous grammars, where one grammar component generates linear structures and another generates hierarchical structures. By coupling lexical elements of both components together, discontinuous structures result. Several types of hybrid grammars are characterized. We also discuss grammar induction from treebanks. The main advantage over existing frameworks is the ability of hybrid grammars to separate discontinuity of the desired structures from time complexity of parsing. This permits exploration of a large variety of parsing algorithms for discontinuous structures, with different properties. This is confirmed by the reported experimental results, which show a wide variety of running time, accuracy and frequency of parse failures.Publisher PDFPeer reviewe
Learning Efficient Disambiguation
This dissertation analyses the computational properties of current
performance-models of natural language parsing, in particular Data Oriented
Parsing (DOP), points out some of their major shortcomings and suggests
suitable solutions. It provides proofs that various problems of probabilistic
disambiguation are NP-Complete under instances of these performance-models, and
it argues that none of these models accounts for attractive efficiency
properties of human language processing in limited domains, e.g. that frequent
inputs are usually processed faster than infrequent ones. The central
hypothesis of this dissertation is that these shortcomings can be eliminated by
specializing the performance-models to the limited domains. The dissertation
addresses "grammar and model specialization" and presents a new framework, the
Ambiguity-Reduction Specialization (ARS) framework, that formulates the
necessary and sufficient conditions for successful specialization. The
framework is instantiated into specialization algorithms and applied to
specializing DOP. Novelties of these learning algorithms are 1) they limit the
hypotheses-space to include only "safe" models, 2) are expressed as constrained
optimization formulae that minimize the entropy of the training tree-bank given
the specialized grammar, under the constraint that the size of the specialized
model does not exceed a predefined maximum, and 3) they enable integrating the
specialized model with the original one in a complementary manner. The
dissertation provides experiments with initial implementations and compares the
resulting Specialized DOP (SDOP) models to the original DOP models with
encouraging results.Comment: 222 page
A Lexicalized Tree Adjoining Grammar for Thai
PACLIC 23 / City University of Hong Kong / 3-5 December 200
- …