Affiliations: [a] Computational Genomics, Centro de Ciencias Genómicas, Universidad Nacional Autónoma de México (UNAM), Morelos, México | [b] Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Autónoma de México (UNAM), Ciudad de México, México | [c] Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
Correspondence:
[*]
Corresponding author. Oscar Lithgow-Serrano. E-mail: olithgow@ccg.unam.mx.
Abstract: The constant increase in the production of scientific literature is making it very difficult for experts to keep up to date with the state-of-the-art knowledge in their fields. The use of Natural Language Processing (NLP) is becoming a necessary aid to tackle this challenge. In the NLP field, the task of measuring semantic similarity between two sentences plays a vital role. It is a cornerstone for tasks like Q&A, Information Retrieval, Automatic Summarization, etc., and it is a crucial element in the ultimate goal of computers being able to decode what is conveyed in human language expression. Measuring Semantic Similarity (SS) in short texts has specific challenges. Because there are fewer words to be compared, the meaning contribution of each word is more relevant, and it is important to take into account the syntax’s contribution to the composed meaning. In addition, the highly specific and specialized vocabulary — Microbial Transcriptional-Regulation—implies the lack of massive training resources. Our approach has been to use an ensemble of similarity metrics including string, distributional, and knowledge-based metric and to combine the results of such analyses. We have trained and tested these methods in a similarity corpus developed in-house. The task has proved very challenging, and the ensemble strategy has proved to be a good approach. Even though there is still much room for improvement in the precision of our methods concerning the human evaluation, we have managed to improve them reaching a strong correlation (ρ = 0.700).
Keywords: Natural Language Processing, Semantic Textual Similarity
