Exploration on Effectiveness and Efficiency of Similar Sentence Matching

Autor:	Masaru Kitsuregawa, Miyuki Nakano, Yanhui Gu, Zhenglu Yang
Rok vydání:	2013
Předmět:	Matching (statistics) business.industry Computer science Similarity computation String searching algorithm computer.software_genre Automatic summarization Focus (linguistics) Sentence similarity Artificial intelligence Image extraction business computer Sentence Natural language processing
Zdroj:	Polibits. 47:23-29
ISSN:	2395-8618 1870-9044
DOI:	10.17562/pb-47-3
Popis:	Similar sentence matching is an essential issue for many applications, such as text summarization, image extraction, social media retrieval, question-answer model, and so on. A number of studies have investigated this issue in recent years. Most of such techniques focus on effectiveness issues but only a few focus on efficiency issues. In this paper, we address both effectiveness and efficiency in the sentence similarity matching. For a given sentence collection, we determine how to effectively and efficiently identify the top-k semantically similar sentences to a query. To achieve this goal, we first study several representative sentence similarity measurement strategies, based on which we deliberately choose the optimal ones through cross-validation and dynamically weight tuning. The experimental evaluation demonstrates the effectiveness of our strategy. Moreover, from the efficiency aspect, we introduce several optimization techniques to improve the performance of the similarity computation. The trade-off between the effectiveness and efficiency is further explored by conducting extensive experiments.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::847ef04632d63ab82d646bf81a95c8e5 https://doi.org/10.17562/pb-47-3 Zobrazit plný text záznamu