A Novel Approach for Semantic Similarity Measurement for High Quality Answer Selection in Question Answering using Deep Learning Methods

Recent years, textual semantic similarity measurements play an important role in Natural Language Processing. The semantic similarity between concepts or terms can be measured by various resources like corpora, ontologies, taxonomies, etc. With the development of deep learning, distributed vector models are constructed for extracting the latent semantic information from corpora. Most of existing models create a single prototype vector to represent the meaning of a word such as CBOW. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. In this work, the authors propose a knowledge-augmented multiple-prototype model by using corpora and ontologies. Based on the distributed word vector learned by the CBOW model, the authors append the concept definition and the relational knowledge vector into the target word vector to enrich the semantic information of the word. Finally, the authors perform the experiments on well-known datasets to verify the efficiency of the authors' approach.

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A Novel Approach to Semantic Similarity Measurement Based on a Weighted Concept Lattice: Exemplifying Geo-Information

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi6110348 ◽

2017 ◽

Vol 6 (11) ◽

pp. 348

Author(s):

Jia Xiao ◽

Zongyi He

Keyword(s):

Semantic Similarity ◽

Concept Lattice ◽

Similarity Measurement ◽

Novel Approach ◽

Semantic Similarity Measurement

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Semantic Similarity Measurement Using Knowledge-Augmented Multiple-prototype Distributed Word Vector

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch041 ◽

2020 ◽

pp. 737-749

Author(s):

Wei Lu ◽

Kailun Shi ◽

Yuanyuan Cai ◽

Xiaoping Che

Keyword(s):

Deep Learning ◽

Semantic Similarity ◽

Language Processing ◽

Semantic Information ◽

Word Meaning ◽

Similarity Measurement ◽

Prototype Model ◽

Concept Definition ◽

Relational Knowledge ◽

Semantic Similarity Measurement

Recent years, textual semantic similarity measurements play an important role in Natural Language Processing. The semantic similarity between concepts or terms can be measured by various resources like corpora, ontologies, taxonomies, etc. With the development of deep learning, distributed vector models are constructed for extracting the latent semantic information from corpora. Most of existing models create a single prototype vector to represent the meaning of a word such as CBOW. However, due to lexical ambiguity, encoding word meaning with a single vector is problematic. In this work, the authors propose a knowledge-augmented multiple-prototype model by using corpora and ontologies. Based on the distributed word vector learned by the CBOW model, the authors append the concept definition and the relational knowledge vector into the target word vector to enrich the semantic information of the word. Finally, the authors perform the experiments on well-known datasets to verify the efficiency of the authors' approach.

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CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures

10.31219/osf.io/wkzrh ◽

2017 ◽

Author(s):

Jorge Martinez-Gil

Keyword(s):

Fuzzy Logic ◽

Semantic Similarity ◽

Similarity Measures ◽

Similarity Measurement ◽

Trade Off ◽

Novel Approach ◽

Fuzzy Aggregation ◽

Semantic Similarity Measurement ◽

Traditional Approaches ◽

Statistical Functions

Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea. There are a lot of semantic similarity measures for addressing this problem. However, the best results have been achieved when aggregating a number of simple similarity measures. This means that after the various similarity values have been calculated, the overall similarity for a pair of text expressions is computed using an aggregation function of these individual semantic similarity values. This aggregation is often computed by means of statistical functions. In this work, we present CoTO (Consensus or Trade-Off) a solution based on fuzzy logic that is able to outperform these traditional approaches.

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