Short text or sentence similarity is crucial in various natural language processing activities. Traditional measures for sentence similarity consider word order, semantic features and role annotations of text to derive the similarity. These measures do not suit short texts or sentences with negation. Hence, this paper proposes an approach to determine the semantic similarity of sentences and also presents an algorithm to handle negation. In sentence similarity, word pair similarity plays a significant role. Hence, this paper also discusses the similarity between word pairs. Existing semantic similarity measures do not handle antonyms accurately. Hence, this paper proposes an algorithm to handle antonyms. This paper also presents an antonym dataset with 111-word pairs and corresponding expert ratings. The existing semantic similarity measures are tested on the dataset. The results of the correlation proved that the expert ratings are in order with the correlation obtained from the semantic similarity measures. The sentence similarity is handled by proposing two algorithms. The first algorithm deals with the typical sentences, and the second algorithm deals with contradiction in the sentences. SICK dataset, which has sentences with negation, is considered for handling the sentence similarity. The algorithm helped in improving the results of sentence similarity.

中文翻译：

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使用语义相似度度量的矛盾句与相似度

简短的文本或句子相似性在各种自然语言处理活动中至关重要。传统的句子相似度度量方法是考虑词序，语义特征和文本的角色注释以得出相似度。这些措施不适合简短的文本或带有否定词的句子。因此，本文提出了一种确定句子语义相似度的方法，并提出了一种处理否定的算法。在句子相似度中，词对相似度起着重要作用。因此，本文还讨论了词对之间的相似性。现有的语义相似性度量不能正确处理反义词。因此，本文提出了一种处理反义词的算法。本文还提出了具有111个单词对和相应专家评级的反义数据集。现有的语义相似性度量在数据集上进行测试。相关结果表明，专家评级与从语义相似性度量获得的相关性是一致的。通过提出两种算法来处理句子相似性。第一种算法处理典型的句子，第二种算法处理句子中的矛盾。SICK数据集具有带否定语句的句子，被认为用于处理句子相似度。该算法有助于提高句子相似度的结果。第二种算法处理句子中的矛盾。SICK数据集具有带否定语句的句子，被认为用于处理句子相似度。该算法有助于提高句子相似度的结果。第二种算法处理句子中的矛盾。SICK数据集具有带否定语句的句子，被认为用于处理句子相似度。该算法有助于提高句子相似度的结果。