Word feature encoding: Synonym and antonym discrimination after generating synonyms or antonyms
نویسندگان
چکیده
منابع مشابه
Word Embedding-based Antonym Detection using Thesauri and Distributional Information
This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, however, cannot capture antonyms since they depend on the distributional hypothesis. Our approach utilizes supervised synonym and antonym information from thesauri, as well as distributional information from large-scale unlabelled...
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We propose a novel vector representation that integrates lexical contrast into distributional vectors and strengthens the most salient features for determining degrees of word similarity. The improved vectors significantly outperform standard models and distinguish antonyms from synonyms with an average precision of 0.66–0.76 across word classes (adjectives, nouns, verbs). Moreover, we integrat...
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For many NLP applications such as Information Extraction and Sentiment Detection, it is of vital importance to distinguish between synonyms and antonyms. While the general assumption is that distributional models are not suitable for this task, we demonstrate that using suitable features, differences in the contexts of synonymous and antonymous German adjective pairs can be identified with a si...
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Distinguishing between antonyms and synonyms is a key task to achieve high performance in NLP systems. While they are notoriously difficult to distinguish by distributional co-occurrence models, pattern-based methods have proven effective to differentiate between the relations. In this paper, we present a novel neural network model AntSynNET that exploits lexico-syntactic patterns from syntacti...
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Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenome...
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ژورنال
عنوان ژورنال: Psychonomic Science
سال: 1972
ISSN: 0033-3131
DOI: 10.3758/bf03332838