Leveraging Grammatical Roles for Measuring Semantic Similarity Between Texts

Abstract

Semantic similarity between texts can be defined based on their meaning. Assessing the textual similarity is a prerequisite in almost all applications in the field of language processing and information retrieval. However, the diversity in the sentence structure makes it formidable to estimate the similarity. Some sentences pairs are lexicographically similar but semantically dissimilar. That is why the trivial lexical overlapping is not enough for measuring the similarity. To attain the semanticity of sentences, the context of the words and the structure of the sentence should be considered. In this paper, we propose a new method for capturing the semantic similarity between sentences based on their grammatical roles through word semantics. First, the sentences are divided grammatically into different parts where each part is considered as a grammatical role. Then multiple new measures are introduced to estimate the role-based similarity exploiting word semantics considering the sentence structure. The proposed similarity measures focus on inter-role and intra-role similarity between the sentence-pair. The word-level semantic information is extracted from a pre-trained word-embedding model. The performance of the proposed method was verified by conducting a wide range of experiments on the SemEval STS dataset. The experimental results indicated the effectiveness of the proposed method in terms of different standard evaluation metrics and outperformed some known related works.

Open Domain Response Generation Guided by Retrieved Conversations

“…The key is in developing matching algorithms that can measure textual relevance between two utterances [12]. Nowadays, retrieval models typically use semantic retrieval rather than keyword retrieval, thanks to the advent of semantic matching [13]. For hybrid or largescale models, the latter is faster and more efficient [14].…”

Revisiting the challenges and surveys in text similarity matching and detection methods

“…The presence of a corpus in text similarity research is a consequence, as presented in the previous issue. There are several free and publicly available online corpus in English, like WordNet [9], Microsoft [16], Reuters [13], Standford [17], and PAN [11]. The problem in this group includes a corpus development for specific needs [18]- [20] or corpus for non-English languages [3], [21].…”

“…Those characters do not contribute to measuring the similarity. Hence, this step removes all unnecessary characters with still maintains the structure of original texts [9], [17], [35]. 7) Characters replacement.…”