Experiments on Extracting Knowledge from a Machine-Readable Dictionary of Synonym Differences (original) (raw)
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Building a lexical knowledge-base of near-synonym differences
Proceedings of the Workshop on WordNet and Other …, 2001
In machine translation and natural language generation, making a poor choice from a set of near-synonyms can be imprecise or awkward, or convey unwanted implications. Our goal is to automatically derive a lexical knowledge-base from a dictionary of near-synonym discriminations. We do this by classifying sentences according to the classes of distinctions they express, on the basis of words selected by a decision-list algorithm. Improvements on previous results are due in part to the addition of a coreference module.
Building and using a lexical knowledge base of near-synonym differences
Computational Linguistics, 2006
Choosing the wrong word in a machine translation or natural language generation system can convey unwanted connotations, implications, or attitudes. The choice between near-synonyms such as error, mistake, slip, and blunder -words that share the same core meaning, but differ in their nuances -can be made only if knowledge about their differences is available.
Towards a Better Learning of Near-Synonyms
Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion, 2017
Language learners are confused by near-synonyms and often look for answers from the Web. However, there is little to aid them in sorting through the overwhelming load of information that is offered. In this paper, we propose a new research problem: suggesting example sentences for learning word distinctions. We focus on near-synonyms as the first step. Two kinds of one-class classifiers, the GMM and BiLSTM models, are used to solve fill-in-the-blank (FITB) questions and further to select example sentences which best differentiate groups of near-synonyms. Experiments are conducted on both an open benchmark and a private dataset for the FITB task. Experiments show that the proposed approach yields an accuracy of 73.05% and 83.59% respectively, comparable to state-of-the-art multi-class classifiers. Learner study further shows the results of the example sentence suggestion by the learning effectiveness and demonstrates the proposed model indeed is more effective in learning near-synonyms compared to the resource-based models.
Automatic Extraction of Synonymy Information: An Extended Abstract
Proceedings of the …, 2006
Automatic Extraction of Synonymy Information: -Extended Abstract-A Kumaran, Ranbeer Makin, Vijay Pattisapu and Shaik Emran Sharif Multilingual Systems Research, Microsoft Research India Bangalore, India Gary Kacmarcik and Lucy Vanderwende Natural Language ...
TEST Engineering & Management, 2020
Choosing the wrong word may convey unintended connotations, meanings or attitudes in a machine translation or natural language generation system. Identifying near synonyms like near, closer, almost and close bywords that share the same core meaning but differ in their nuances-can be made only if knowledge about their differences is available. Identifying such synonym of a word/entity in the given context is a critical and trending concept in Natural Language Processing (NLP) which has immense application in various fields like word sense disambiguation, text summarization, document retrieval etc. There are wide variety of technique and methodologies have been proposed for identification of synonyms in a given context by utilizing various dataset or corpus. Identifying synonym in a given context has become more trending topic in a research field of NLP. In this paper we try to discuss various technique and works that has been used to solve automatic synonyms retrieval problem.
Evaluating the Quality of Automatically Extracted Synonymy Information
Foundations of Ontologies …
Automatic extraction of semantic information, if successful, offers to lan-guages with little or poor resources, the prospects of creating ontological resources inexpensively, thus providing support for common-sense reason-ing applications in those languages. In this paper we explore ...
Usage notes as the basis for a representation of near-synonymy for lexical choice
Proceedings of 9th annual conference of the …, 1993
The task of choosing between lexical near-equivalents in text generation requires the kind of knowledge of ne di erences between words that is typi ed by the usage notes of dictionaries and books of synonym discrimination. These usage notes follow a fairly standard pattern, and a study of their form and content shows the kinds of di erentiae adduced in the discrimination of nearsynonyms. For appropriate lexical choice in text generation and machine translation systems, it is necessary to develop the concept of formal`computational usage notes', which w ould be part of the lexical entries in a conceptual knowledge base. The construction of a set of`computational usage notes' adequate for text generation is a major lexicographic task of the future.
Near-synonym choice in an intelligent thesaurus
2007
An intelligent thesaurus assists a writer with alternative choices of words and orders them by their suitability in the writing context. In this paper we focus on methods for automatically choosing near-synonyms by their semantic coherence with the context. Our statistical method uses the Web as a corpus to compute mutual information scores. Evaluation experiments show that this method performs better than a previous method on the same task. We also propose and evaluate two more methods, one that uses anti-collocations, and one that uses supervised learning. To asses the difficulty of the task, we present results obtained by human judges.
Extracting Synonyms from Dictionary Definitions
Recent Advances in Natural Language Processing, 2009
Automatic extraction of synonyms and/or semantically related words has various applications in Natural Language Processing (NLP). There are currently two mainstream extraction paradigms, namely, lexicon-based and distributional approaches. The former usually suffers from low coverage, while the latter is only able to capture general relatedness rather than strict synonymy. In this paper, two rule-based extraction methods are applied to definitions from a machine-readable dictionary. Extracted synonyms are evaluated in two ...