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首页> 外文期刊>IEEE computational intelligence magazine >word2set: WordNet-Based Word Representation Rivaling Neural Word Embedding for Lexical Similarity and Sentiment Analysis
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word2set: WordNet-Based Word Representation Rivaling Neural Word Embedding for Lexical Similarity and Sentiment Analysis

机译：Word2Set：基于Wordnet的字表示竞争神经词嵌入词汇相似性和情感分析

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摘要

Measuring lexical similarity using WordNet has a long tradition. In the last decade, it has been challenged by distributional methods, and more recently by neural word embedding. In recent years, several larger lexical similarity benchmarks have been introduced, on which word embedding has achieved state-of-the-art results. The success of such methods has eclipsed the use of WordNet for predicting human judgments of lexical similarity. We propose a new set cardinality-based method for measuring lexical similarity, which exploits the WordNet graph, obtaining a word representation, which we called word2set, based on related neighboring words. We show that the features extracted from set cardinalities computed using this word representation, when fed into a support vector regression classifier trained on a dataset of common synonyms and antonyms, produce results competitive with those of word-embedding approaches. On the task of predicting the lexical sentiment polarity, our WordNet set-based representation significantly outperforms the classical measures and achieves the performance of neural embeddings. Although word embedding is still the best approach for these tasks, our method significantly reduces the gap between the results shown by knowledge-based approaches and by distributional representations, without requiring a large training corpus. It is also more effective for less-frequent words.
机译：使用Wordnet测量词汇相似性具有悠久的传统。在过去的十年中，它受到分布方法的挑战，最近是神经词嵌入的。近年来，已经介绍了几个较大的词汇相似基准，其中嵌入的单词嵌入已经实现了最先进的结果。此类方法的成功使Wordnet使用Wordnet来预测词汇判断的词汇相似性。我们提出了一种新的基于集基主的方法，用于测量词汇相似性，该方法利用Wordnet图形，获取基于相关的相邻单词的Word2Set的单词表示。我们展示从使用此单词表示计算的集基数中提取的功能，当馈入在公共同义词和反义词的数据集上培训的支持向量回归分类器时，产生与嵌入方法的结果竞争的结果。关于预测词汇情绪极性的任务，我们的Wordnet集合的表示显着优于经典措施并实现了神经嵌入的性能。虽然Word嵌入仍然是这些任务的最佳方法，但我们的方法显着降低了基于知识的方法和分布表示所示的结果之间的差距，而无需大型培训语料库。它对较少频繁的单词来说也更有效。

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