An indexing scheme for typed feature structures

This paper presents a formalization of proportional analogy using typed feature structures, which retains all key elements of analogical models of morphology. With the Kasem number system as an example, I show that using this model it is possible to express partial analogies which are unified into complete analogies. The analysis presented is accompanied by a complete TRALE implementation.

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An Optimized Prolog Encoding of Typed Feature Structures

Logic Programming ◽

10.7551/mitpress/4304.003.0017 ◽

1999 ◽

Keyword(s):

Feature Structures ◽

Typed Feature Structures

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The LiLFeS Abstract Machine and its evaluation with the LinGO grammar

Natural Language Engineering ◽

10.1017/s1351324900002400 ◽

2000 ◽

Vol 6 (1) ◽

pp. 47-61 ◽

Cited By ~ 7

Author(s):

YUSUKE MIYAO ◽

TAKAKI MAKINO ◽

KENTARO TORISAWA ◽

JUN-ICHI TSUJII

Keyword(s):

Large Scale ◽

Experimental Results ◽

Instruction Set ◽

Abstract Machine ◽

Empirical Results ◽

Negation As Failure ◽

Feature Structures ◽

Efficient Processing ◽

Typed Feature Structures ◽

Definite Clause

This article evaluates the efficiency of the LiLFeS abstract machine by performing parsing tasks with the LinGO English resource grammar. The instruction set of the abstract machine is optimized for efficient processing of definite clause programs and typed feature structures. LiLFeS also supports various tools required for efficient parsing (e.g. efficient copying, a built-in CFG parser) and the constructions of standard Prolog (e.g. cut, assertions, negation as failure). Several parsers and large-scale grammars, including the LinGO grammar, have been implemented in or ported to LiLFeS. Precise empirical results with the LinGO grammar are provided to allow comparison with other systems. The experimental results demonstrate the efficiency of the LiLFeS abstract machine.

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Feature Forest Models for Probabilistic HPSG Parsing

Computational Linguistics ◽

10.1162/coli.2008.34.1.35 ◽

2008 ◽

Vol 34 (1) ◽

pp. 35-80 ◽

Cited By ~ 84

Author(s):

Yusuke Miyao ◽

Jun'ichi Tsujii

Keyword(s):

Maximum Entropy ◽

Data Structures ◽

Dynamic Programming Algorithm ◽

Probabilistic Modeling ◽

Complex Data ◽

Forest Model ◽

Forest Models ◽

Feature Structures ◽

Maximum Entropy Estimation ◽

Typed Feature Structures

Probabilistic modeling of lexicalized grammars is difficult because these grammars exploit complicated data structures, such as typed feature structures. This prevents us from applying common methods of probabilistic modeling in which a complete structure is divided into sub-structures under the assumption of statistical independence among sub-structures. For example, part-of-speech tagging of a sentence is decomposed into tagging of each word, and CFG parsing is split into applications of CFG rules. These methods have relied on the structure of the target problem, namely lattices or trees, and cannot be applied to graph structures including typed feature structures. This article proposes the feature forest model as a solution to the problem of probabilistic modeling of complex data structures including typed feature structures. The feature forest model provides a method for probabilistic modeling without the independence assumption when probabilistic events are represented with feature forests. Feature forests are generic data structures that represent ambiguous trees in a packed forest structure. Feature forest models are maximum entropy models defined over feature forests. A dynamic programming algorithm is proposed for maximum entropy estimation without unpacking feature forests. Thus probabilistic modeling of any data structures is possible when they are represented by feature forests. This article also describes methods for representing HPSG syntactic structures and predicate-argument structures with feature forests. Hence, we describe a complete strategy for developing probabilistic models for HPSG parsing. The effectiveness of the proposed methods is empirically evaluated through parsing experiments on the Penn Treebank, and the promise of applicability to parsing of real-world sentences is discussed.

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