Supervized machine learning: Computer-aided development of a specialized dictionary (original) (raw)

LEXIK. An Integrated System for Specialized Terminology

Lecture Notes in Artificial Intelligence, núm. 10061 (agosto), Springer, 2017

The paper presents LEXIK, an intelligent terminological architecture that is able to efficiently obtain specialized lexical resources for elaborating dictionaries and providing lexical support for different expert tasks. LEXIK is designed as a powerful tool to create a rich knowledge base for lexicography. It will process big amounts of data in a modular system, that combines several applications and techniques for terminology extraction, definition generation, example extraction and term banks, that have been partially developed so far. Such integration is a challenge for the area, which lacks an integrated system for extracting and defining terms from a non-preprocessed corpus.

Towards the induction of terminological decision trees

2010

Abstract A concept learning framework for terminological representations is introduced. It is grounded on a method for inducing logic decision trees as an adaptation of the classic tree induction methods to the Description Logics representations adopted in the Semantic Web context. Differently from the original setting of logical trees based on clausal representations, tree-nodes contain terminological concept descriptions (corresponding to OWL-DL classes) which makes it appealing for the Semantic Web applications.

Inference Engine for Classification of Expert Systems Using Keyword Extraction Technique

Because of the fast-growing demands in automated document dispensation, a steadfast system for automatic identification of keywords entrenched in an electronic document is of immense concern. The paper envisaged an innovative approach for the classification of multiple Expert System (ES) methodologies at a time on the basis of keyword extraction using a commercial text mining tools WordStat and Compare-Suite Pro. These ES methodologies include eleven categories that include; rule-based systems, database methodology ,case-based reasoning, intelligent agent, knowledge-based systems, fuzzy based expert system, object oriented methodology, neural networks, system architecture, systems, modelling, and ontology. The keywords are selected on the basis of frequency analysis and position of most recurring word in context within the article tile, abstract and keywords of respective ES methodology. Based on the extracted keywords, an inference engine has been designed on java software. This software compares the keyword established from the articles of individual ES methodology with all other articles of the remaining methodologies using generation of association rules. The inference engine developed was first calibrated for 100 articles out of 160 and then validated for remaining 60 articles. The validation results shows accuracy of the experimental results up to 85 percent. The paper concludes that the classification of Expert Systems using keyword extraction technique, outperforming a base line, is a more accurate, reliable and optimal with respect to time as compared to other orthodox methods of text mining. At the end it has been concluded that the techniques may further improved by limiting design constraints in the tools adopted in the research for future endeavours.

Principles, procedures and rules in an expert system for information retrieval

Information Processing & Management, 1985

An expert system was developed in the area of information retrieval, with the objective of performing the job of an information specialist who assists users in selecting the right vocabulary terms for a database search. The system is composed of two components: one is the knowledge base, represented as a semantic network, in which the nodes are words, concepts and phrases comprising a cocabulary of the application area, and the links express semantic relationships between those nodes. The second component is the rules, or procedures, which operate upon the knowledge-base, analogous to the decision rules or work patterns of the information specialist. Two major stages comprise the consulting process of the system: During the “search” stage, relevant knowledge in the semantic network is activated, and search and evaluation rules are applied in order to find appropriate vocabulary terms to represent the user's need During the “suggest” stage, those terms are further evaluated, dynamically rank-ordered according to relevancy, and suggested to the user. Explanations to the findings can be provided by the system and backtracking is possible in order to find alternatives in case some suggested term is rejected by the user. This article presents the principle, procedures and rules that are utilized in the expert system.

A New Approach for Semi-Automatic Building and Extending a Multilingual Terminology Thesaurus

International Journal on Artificial Intelligence Tools, 2019

This paper describes a new system for semi-automatically building, extending and managing a terminological thesaurus — a multilingual terminology dictionary enriched with relationships between the terms themselves to form a thesaurus. The system allows to radically enhance the workow of current terminology expert groups, where most of the editing decisions still come from introspection. The presented system supplements the lexicographic process with natural language processing techniques, which are seamlessly integrated to the thesaurus editing environment. The system’s methodology and the resulting thesaurus are closely connected to new domain corpora in the six languages involved. They are used for term usage examples as well as for the automatic extraction of new candidate terms. The terminological thesaurus is now accessible via a web-based application, which (a) presents rich detailed information on each term, (b) visualizes term relations, and (c) displays real-life usage exam...