Automatic Natural Language Parsing
Abstract
This collection of papers on automatic natural language parsing examines research and development in language processing over the past decade. It focuses on current trends toward a phrase structure grammar and deterministic parsing.
Research into man-machine communication has produced systems which allow the user to interrogate the database in natural language [Wallace, 1984]. Natural language systems are particularly useful for interrogating the database, rather than for designing the schema, subschemata, etc. They can thus be considered as alternatives to languages like DML, SQL and QBE, operating as front-end modules to these, or as stand-alone systems which retrieve data directly from the database [Sime & Coombs, 1983].
This chapter reviews the evolution of methods for spoken language understanding (SLU) systems. It provides Meaning Representation Language (MRL) with methods for obtaining meaning representations from natural language. The chapter introduces probabilistic frameworks accounting for knowledge imprecision and limitations of automatic speech recognition systems. It reviews automatic systems for SLU using these methods. Computer epistemology deals with the representation of semantic knowledge in a computer using an appropriate formalism. Furthermore, especially for SLU, signs used for interpretation are extracted from the speech signal with a process that is not perfect. These problems suggested the use of probabilistic models and machine learning methods for automatically characterizing supports for semantic constituents end their relations. As a consequence, methods were proposed for estimating the parameters of generative models and classification methods. The chapter reviews these methods, and reports some evaluations. natural language processing; speech recognition equipment
A parser for natural language is proposed which, (a) concentrates on methodological procedures rather than grammatical details of a natural language; (b) is based on the claim that in order to exploit the full power of computers, models of human language processing should not necessarily be designed to simulate human behavior; and (c) is designed for the advanced technology of the computers of the future. the parser is a component of a language understanding machine which is only represented in a sketch in this article.
Examination regulations of the Neuphilologische Fakultät require that students attend courses regularly. If students do not attend a course meeting on more than two occasions in one semester without proper excuse (e.g., doctor's note), the course instructor has to give them a failing grade. You are expected to come on time. Being late without good reasons will count as not having attended a course meeting. Your grade will be based on two components: The midterm exam (50%), the final exam (50%). In addition, to obtain the grade, you have to hand in 50% of the homework exercises.
Expert Systems have gained considerable momentum in the past few years. Though many systems have been built in various disciplines, the process of Expert System development is not clearly defined. However, it is possible to define a general framework. Expert System building can be classified into the following five steps: (1) problem definition, (2) conceptualization, (3) implementation, (4) user interface and (5) learning. p]In this work, an Expert System for industrial facilities layout planning is developed, from the context of the above five steps. The layout problem is treated as a multi-objective situation and is solved via the Expert System. This paper discusses in depth the development of the Expert System.
Instructional grammar is often used in computer-assisted language learning (CALL) and the grammatical error detection is an important feature. However, it is not an easy task in Japanese language. There is no delimiter separating consecutive words in Japanese sentences. Word segmentation is a process in which proper word boundaries are identified. Before syntactic parsing of a Japanese sentence, word segmentation has to be performed. Traditionally, the word segmentation is often followed by the syntactic parsing. An algorithm in which the Japanese word segmentation and syntactic parsing are combined into one process can increase the overall efficiency.
A major problem in machine translation is the semantic description of lexical units which should be based on a semantic system that is both coherent and operationalized to the greatest possible degree. This is to guarantee consistency between lexical ...
This paper presents a new, language independent model for analysis and generation of word froms based on Finite State Transducers (FSTs). It has been completely implemented on a PC and successfully tested with lexicons and rules covering all of German verb morphology and the most interesting subsets of French and Spanish verbs as well. The linguistic databascs consist of a letter-tree structured lexicon with annotated feature lists and a FST which is constructed from a set of morphophonological rules. These rewriting rules operate on complete words unlike other FST-based systems.
This paper describes salient aspects of the OntoSem lexicon of English, a lexicon whose semantic descriptions can either be grounded in a language-independent ontology, rely on extra-ontological expressive means, or exploit a combination of the two. The variety of descriptive means, as well as the conceptual complexity of semantic description to begin with, necessitates that OntoSem lexicons be compiled primarily manually. However, once a semantic description is created for a lexeme in one language, it can be reused in others, often with little or no modification. Said differently, the challenge in building a semantic lexicon is describing semantics; once the semantics are described, it is relatively straightforward to connect given meanings to the appropriate head words in other languages. In this paper we provide a brief overview of the OntoSem lexicon and processing environment, orient our approach to lexical semantics among others in the field, and describe in more detail what we mean by the largely language-independent lexicon. Finally, we suggest reasons why our resources might be of interest to the larger community.
This paper presents a new method of LR parsing based on the distinction of stack states and non-stack states. Non-stack states are states, which do not need to be pushed into the LR parsing stack, and stack states are states to be pushed into it. By using some properties based on the stack-controlling LR parser defined here, the parsing speed and the size of parsing tables can be remarkably improved and their improvement includes the traditional method eliminating unit productions. By empirical observations for a variety of programming languages, the efficiency is verified. Extending it to the generalized LR parsers for natural language is also discussed.
Diese Einführung hat das Ziel, einen Überblick über zentrale Fragestellungen der maschinellen Sprachverarbeitung und einige exemplarische Ansätze zu ihrer Lösung zu vermitteln. Ausgehend von einer Charakterisierung des Gegenstandsbereichs werden Prinzipien und Methoden der Repräsentation natürlich-sprachlicher Objekte vorgestellt. Im Mittelpunkt steht die Frage der Modellierung menschlichen Sprachhandelns und ihre Umsetzung in Verarbeitungsmechanismen. Gemäß der Einteilung der Sprachwissenschaft in die Lehre von der Ordnung (Phonologie, Morphologie und Syntax), vom Inhalt (Semantik) und vom Gebrauch (Pragmatik und Diskurs) der Sprache werden Sprachverarbeitungssysteme diesen Abstraktionsebenen entsprechend modularisiert. Dabei beschränken wir uns auf die linguistische Analyse; die Behandlung der maschinellen Generierung natürlich-sprachlicher Äußerungen mußte aus Platzgründen entfallen. Die Darstellung der Verarbeitungsebenen wird durch einige Anmerkungen zur Architektur von Sprachverarbeitungssystemen abgeschlossen1.
This paper presents a new method of LR parsing based on the
distinction of stack states and non-stack states. Non-stack states are
states which do not need to be pushed into the LR parsing stack and
stack states are states to be pushed into it. By using some of the
properties based on the stack-controlling LR parser defined, the parsing
speed and the size of parsing tables can be improved, and the
improvement includes the traditional method eliminating unit
productions. By empirical observations for variety of programming
languages, the efficiency is verified. An extension of the method to the
generalized LR parsers for natural language is also discussed
This paper describes an implemented parser-interpreter which is intended as an abstract formal model of part of the process of sentence comprehension. It is illustrated here for Phrase Structure Grammars with a translation into a familiar type of logical form, although the general principles are intended to apply to any grammarital theory sharing certain basic assumptions, which are discussed in the paper. The procedure allows for incremental semantic interpretation as a sentence is parsed, and provides a principled explanation for some familiar observations concerning properties of deeply recursive constructions.
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