Towards optimize-ESA for text semantic similarity: A case study of biomedical text

Explicit Semantic Analysis (ESA) is an approach to measure the semantic relatedness between terms or documents based on similarities to documents of a references corpus usually Wikipedia. ESA usage has received tremendous attention in the field of natural language processing NLP and information retrieval. However, ESA utilizes a huge Wikipedia index matrix in its interpretation by multiplying a large matrix by a term vector to produce a high-dimensional vector. Consequently, the ESA process is too expensive in interpretation and similarity steps. Therefore, the efficiency of ESA will slow down because we lose a lot of time in unnecessary operations. This paper propose enhancements to ESA called optimize-ESA that reduce the dimension at the interpretation stage by computing the semantic similarity in a specific domain. The experimental results show clearly that our method correlates much better with human judgement than the full version ESA approach

Wikipedia-Based Semantic Interpreter Using Approximate Top-k Processing and Its Application

Proper representation of the meaning of texts is crucial for enhancing many data mining and information retrieval tasks, including clustering, computing semantic relatedness between texts, and searching. Representing of texts in the concept-space derived from Wikipedia has received growing attention recently. This concept-based representation is capable of extracting semantic relatedness between texts that cannot be deduced with the bag of words model. A key obstacle, however, for using Wikipedia as a semantic interpreter is that the sheer size of the concepts derived from Wikipedia makes it hard to efficiently map texts into concept-space. In this paper, we develop an efficient and effective algorithm which is able to represent the meaning of a text by using the concepts that best match it. In particular, our approach first computes the approximate top-k Wikipedia concepts that are most relevant to the given text. We then leverage these concepts for representing the meaning of the given text. The experimental results show that the proposed technique provides significant gains in execution time without causing significant reduction in precision. We then explore the effectiveness of the proposed algorithm on a real world problem. In particular, we show that this novel scheme could be leveraged to boost the effectiveness in finding topic boundaries in a news video