Kentaro Torisawa

An HPSG parser with CFG filtering

This article presents an HPSG parser using a technique called CFG filtering. The parser predicts possible parse trees using a CFG generated automatically from a given HPSG-based grammar. Parsing costs are reduced because unification is applied only to the predicted parse trees. In other words, parsing is speeded up because the parser avoids unnecessary unification by eliminating impossible parse trees. We show the method for generating a CFG from an HPSG-based grammar and outline a parsing scheme using the CFG. The effectiveness of the parsing scheme is shown through experimental results obtained by using several HPSG-based grammars, including the LinGO grammar.

LiLFeS - Towards a Practical HPSG Parser

This paper presents the LiLFeS system, an efficient feature-structure description language for HPSG. The core engine of LiLFeS is an Abstract Machine for Attribute-Value Logics, proposed by Carpenter and Qu. Basic design policies, the current status, and performance evaluation of the LiLFeS system are described. The paper discusses two implementations of the LiLFeS. The first one is based on an emulator of the abstract machine, while the second one uses a native-code compiler and therefore is much more efficient than the first one.

HPSG-Style Underspecified Japanese Grammar with Wide Coverage

This paper describes a wide-coverage Japanese grammar based on HPSG. The aim of this work is to see the coverage and accuracy attainable using an underspecified grammar. Underspecification, allowed in a typed feature structure formalism, enables us to write down a wide-coverage grammar concisely. The grammar we have implemented consists of only 6 ID schemata, 68 lexical entries (assigned to functional words), and 63 lexical entry templates (assigned to parts of speech (POSs) ). Furthermore, word-specific constraints such as subcategorization of verbs are not fixed in the grammar. However, this grammar can generate parse trees for 87% of the 10000 sentences in the Japanese EDR corpus. The dependency accuracy is 78% when a parser uses the heuristic that every bunsetsu is attached to the nearest possible one.

A hybrid Japanese parser with hand-crafted grammar and statistics

This paper describes a hybrid parsing method for Japanese which uses both a hand-crafted grammar and a statistical technique. The key feature of our system is that in order to estimate likelihood for a parse tree, the system uses information taken from alternative partial parse trees generated by the grammar. This utilization of alternative trees enables us to construct a new statistical model called Triplet/Quadruplet Model. We show that this model can capture a certain tendency in Japanese syntactic structures and this point contributes to improvement of parsing accuracy on a shallow level. We report that, with an underspecified HPSG-based grammar and a maximum entropy estimation, our parser achieved high accuracy: 88.6% accuracy in dependency analysis of the EDR annotated corpus, and that it outperformed other purely statistical parsing methods on the same corpus. This result suggests that proper treatment of hand-crafted grammars can contribute to parsing accuracy on a shallow level.

The LiLFeS Abstract Machine and its evaluation with the LinGO grammar

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.

Computing phrasal-signs in HPSG prior to parsing

This paper describes techniques to compile lexical entries in HPSG (Pollard and Sag, 1987; Pollard and Sag, 1993)-style grammar into a set of finite state automata. The states in automata are possible signs derived from lexical entries and contain information raised from the lexical entries. The automata are augmented with feature structures used by a partial unification routine and delayed/frozen definite clause programs.

An Efficient Parallel Substrate for Typed Feature Structures on Shared Memory Parallel Machines

This paper describes an efficient parallel system for processing Typed Feature Structures (TFSs) on shared-memory parallel machines. We call the system Parallel Substrate for TFS (PSTFS). PSTFS is designed for parallel computing environments where a large number of agents are working and communicating with each other. Such agents use PSTFS as their low-level module for solving constraints on TFSs and sending/receiving TFSs to/from other agents in an efficient manner. From a programmers point of view, PSTFS provides a simple and unified mechanism for building high-level parallel NLP systems. The performance and the flexibility of our PSTFS are shown through the experiments on two different types of parallel HPSG parsers. The speed-up was more than 10 times on both parsers.