@inproceedings{carpenter-qu-1995-abstract,
title = "An Abstract Machine for Attribute-Value Logics",
author = "Carpenter, Bob and
Qu, Yan",
editor = "Hajicova, Eva and
Lang, Bernard and
Berwick, Robert and
Bunt, Harry and
Carpenter, Bob and
Church, Ken and
Joshi, Aravind and
Kaplan, Ronald and
Kay, Martin and
Nagao, Makoto and
Nijholt, Anton and
Steedman, Mark and
Thompson, Henry and
Tomita, Masaru and
Vijay-Shanker, K. and
Wilks, Yorick and
Wittenburg, Kent",
booktitle = "Proceedings of the Fourth International Workshop on Parsing Technologies",
month = sep # " 20-24",
year = "1995",
address = "Prague and Karlovy Vary, Czech Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/1995.iwpt-1.9",
pages = "59--70",
abstract = "A direct abstract machine implementation of the core attribute-value logic operations is shown to decrease the number of operations and conserve the amount of storage required when compared to interpreters or indirect compilers. In this paper, we describe the fundamental data structures and compilation techniques that we have employed to develop a unification and constraint-resolution engine capable of performance rivaling that of directly compiled Prolog terms while greatly exceeding Prolog in flexibility, expressiveness and modularity. In this paper, we will discuss the core architecture of our machine. We begin with a survey of the data structures supporting the small set of attribute-value logic instructions. These instructions manipulate feature structures by means of features, equality, and typing, and manipulate the program state by search and sequencing operations. We further show how these core operations can be integrated with a broad range of standard parsing techniques. Feature structures improve upon Prolog terms by allowing data to be organized by feature rather than by position. This encourages modular program development through the use of sparse structural descriptions which can be logically conjoined into larger units and directly executed. Standard linguistic representations, even of relatively simple local syntactic and semantic structures, typically run to hundreds of substructures. The type discipline we impose organizes information in an object-oriented manner by the multiple inheritance of classes and their associated features and type value constraints. In practice, this allows the construction of large-scale grammars in a relatively short period of time. At run-time, eager copying and structure-sharing is replaced with lazy, incremental, and localized branch and write operations. In order to allow for applications with parallel search, incremental backtracking can be localized to disjunctive choice points within the description of a single structure, thus supporting the kind of conditional mutual consistency checks used in modern grammatical theories such as HPSG, GB, and LFG. Further attention is paid to the byte-coding of instructions and their efficient indexing and subsequent retrieval, all of which is keyed on type information.",
}
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<abstract>A direct abstract machine implementation of the core attribute-value logic operations is shown to decrease the number of operations and conserve the amount of storage required when compared to interpreters or indirect compilers. In this paper, we describe the fundamental data structures and compilation techniques that we have employed to develop a unification and constraint-resolution engine capable of performance rivaling that of directly compiled Prolog terms while greatly exceeding Prolog in flexibility, expressiveness and modularity. In this paper, we will discuss the core architecture of our machine. We begin with a survey of the data structures supporting the small set of attribute-value logic instructions. These instructions manipulate feature structures by means of features, equality, and typing, and manipulate the program state by search and sequencing operations. We further show how these core operations can be integrated with a broad range of standard parsing techniques. Feature structures improve upon Prolog terms by allowing data to be organized by feature rather than by position. This encourages modular program development through the use of sparse structural descriptions which can be logically conjoined into larger units and directly executed. Standard linguistic representations, even of relatively simple local syntactic and semantic structures, typically run to hundreds of substructures. The type discipline we impose organizes information in an object-oriented manner by the multiple inheritance of classes and their associated features and type value constraints. In practice, this allows the construction of large-scale grammars in a relatively short period of time. At run-time, eager copying and structure-sharing is replaced with lazy, incremental, and localized branch and write operations. In order to allow for applications with parallel search, incremental backtracking can be localized to disjunctive choice points within the description of a single structure, thus supporting the kind of conditional mutual consistency checks used in modern grammatical theories such as HPSG, GB, and LFG. Further attention is paid to the byte-coding of instructions and their efficient indexing and subsequent retrieval, all of which is keyed on type information.</abstract>
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%0 Conference Proceedings
%T An Abstract Machine for Attribute-Value Logics
%A Carpenter, Bob
%A Qu, Yan
%Y Hajicova, Eva
%Y Lang, Bernard
%Y Berwick, Robert
%Y Bunt, Harry
%Y Carpenter, Bob
%Y Church, Ken
%Y Joshi, Aravind
%Y Kaplan, Ronald
%Y Kay, Martin
%Y Nagao, Makoto
%Y Nijholt, Anton
%Y Steedman, Mark
%Y Thompson, Henry
%Y Tomita, Masaru
%Y Vijay-Shanker, K.
%Y Wilks, Yorick
%Y Wittenburg, Kent
%S Proceedings of the Fourth International Workshop on Parsing Technologies
%D 1995
%8 sep 20 24
%I Association for Computational Linguistics
%C Prague and Karlovy Vary, Czech Republic
%F carpenter-qu-1995-abstract
%X A direct abstract machine implementation of the core attribute-value logic operations is shown to decrease the number of operations and conserve the amount of storage required when compared to interpreters or indirect compilers. In this paper, we describe the fundamental data structures and compilation techniques that we have employed to develop a unification and constraint-resolution engine capable of performance rivaling that of directly compiled Prolog terms while greatly exceeding Prolog in flexibility, expressiveness and modularity. In this paper, we will discuss the core architecture of our machine. We begin with a survey of the data structures supporting the small set of attribute-value logic instructions. These instructions manipulate feature structures by means of features, equality, and typing, and manipulate the program state by search and sequencing operations. We further show how these core operations can be integrated with a broad range of standard parsing techniques. Feature structures improve upon Prolog terms by allowing data to be organized by feature rather than by position. This encourages modular program development through the use of sparse structural descriptions which can be logically conjoined into larger units and directly executed. Standard linguistic representations, even of relatively simple local syntactic and semantic structures, typically run to hundreds of substructures. The type discipline we impose organizes information in an object-oriented manner by the multiple inheritance of classes and their associated features and type value constraints. In practice, this allows the construction of large-scale grammars in a relatively short period of time. At run-time, eager copying and structure-sharing is replaced with lazy, incremental, and localized branch and write operations. In order to allow for applications with parallel search, incremental backtracking can be localized to disjunctive choice points within the description of a single structure, thus supporting the kind of conditional mutual consistency checks used in modern grammatical theories such as HPSG, GB, and LFG. Further attention is paid to the byte-coding of instructions and their efficient indexing and subsequent retrieval, all of which is keyed on type information.
%U https://aclanthology.org/1995.iwpt-1.9
%P 59-70
Markdown (Informal)
[An Abstract Machine for Attribute-Value Logics](https://aclanthology.org/1995.iwpt-1.9) (Carpenter & Qu, IWPT-WS 1995)
ACL
- Bob Carpenter and Yan Qu. 1995. An Abstract Machine for Attribute-Value Logics. In Proceedings of the Fourth International Workshop on Parsing Technologies, pages 59–70, Prague and Karlovy Vary, Czech Republic. Association for Computational Linguistics.