(Almost) Automatic Semantic Feature Extraction From Technical Text

(ALMOST) AUTOMATIC SEMANTIC FEATURE EXTRACTION FROM TECHNICAL TEXT Rajeev Agarwal* rajeevOcs, msstaSe, edu Department of Computer Science Mississippi State University Mississippi State, MS 39762. ABSTRACT Acquisition of semantic information is necessary for proper understanding of natural language text. Such information is often domain-speclfic in nature and must be acquized from the domain. This causes a problem whenever a natural fan- guage processing (NLP) system is moved from one domain to another. The portability of an NLP system can be improved if these semantic features can be acquired with ];mited hu- man intervention. This paper proposes an approach towards (almost) automatic semantic feature extraction. I. INTRODUCTION Acquisition of semantic information is necessary for proper understanding of natural language text. Such information is often domaln-specific in nature and must be acquized from the domain. When an NLP system is moved from one do- main to another, usually a substantial amount of time has to be spent in tailoring the system to the new domain. Most of this time is spent on acquiring the semantic features specific to that domain. It is important to automate the process of acquisition of semantic information as much as possible, and facilitate whatever human intervention is absolutely neces- sary. Portability of NLP systems has been of concern to re- searchers for some time [8, 5, 11, 9]. This paper proposes an approach to obtain the domaln-dependent semantic features of any given domain in a domain-independent manner. The next section will describe an existing NLP system (KUDZU) which has been developed at Mississippi State Uni- versity. Section 3 will then present the motimstion behind the automatic acquisition of the semantic features of a domain, and a brief outline of the methodology proposed to do it. Sec- tion 4 will describe the dlf[ereut steps in this methodology in detail Section 5 will focus on the app]icatlons of the seman- tic features. Section 6 compares the proposed approach to R;ml]ar research efforts. The last section presents some final comments. 2. THE EXISTING KUDZU SYSTEM The research described in this paper is part of a larger on- going project called the KUDZU (Knowledge Under Devel- opment from Zero Understanding) project. This project is aimed at exploring the automation of extraction of infor- mation from technical texts. The KUDZU system has two primary components -- an NLP component, and a Knowl- edge Analysis (KA) component. This section desoribes this system in order to facilitate understanding of the approach described in this paper. The NLP component consists of a tagger, a semi-purser, a prepositional phrase attachment specialist, a conjunct iden- tifier for coordinate conjunctions, and a restructuzer. The tagger is an u-gram based program that currently generates syntactic/semantic tags for the words in the corpus. The syntactic portion of the tag is mandatory and the seman- tic portion depends upon whether the word has any spe- cial domain-specific classification or not. Currently only nouns, gerunds, and adjectives are assigned semantic tags. For example, in the domain of veterinary medicine, adog" would be assigned …

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