A Semantic Distance Of Natural Language Queries Based On Question Answer Pairs
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A Semantic Distance of Natural Language Queries Based on Question-answer Pairs
Many Natural Language Processing (NLP) techniques have been applied in the field of Question Answering (QA) for understanding natural language queries. Practical QA systems classify a natural language query into vertical domains, and determine whether it is similar to a question with known or latent answers. Current mobile personal assistant applications process queries, recognized from voice input or translated from cross-lingual queries. Theoretically speaking, all these problems rely on an intuitive notion of semantic distance. However, it is neither definable nor computable. Many studies attempt to approximate such a semantic distance in heuristic ways, for instance, distances based on synonym dictionaries. In this paper, we propose a unified algorithm to approximate the semantic distance by a well-defined information distance theory. The algorithm depends on a pre-constructed data structure - semantic clusters, which is built from 35 million question-answer pairs automatically. From the semantic measurement of questions, we implement two practical NLP systems, including a question classifier and a translation corrector. Then a series of comparison experiments have been conducted on both implementations. Experimental results demonstrate that our distance based approach produces fewer errors in classification, compared with other academic works. Also, our translation correction system achieves significant improvements on the Google translation results.
Natural Language Processing and Chinese Computing
This three-volume set constitutes the refereed proceedings of the 12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023, held in Foshan, China, during October 12–15, 2023. The ____ regular papers included in these proceedings were carefully reviewed and selected from 478 submissions. They were organized in topical sections as follows: dialogue systems; fundamentals of NLP; information extraction and knowledge graph; machine learning for NLP; machine translation and multilinguality; multimodality and explainability; NLP applications and text mining; question answering; large language models; summarization and generation; student workshop; and evaluation workshop.
Natural Language Processing and Chinese Computing
This two volume set of LNAI 11108 and LNAI 11109 constitutes the refereed proceedings of the 7th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2018, held in Hohhot, China, in August 2018. The 55 full papers and 31 short papers presented were carefully reviewed and selected from 308 submissions. The papers of the first volume are organized in the following topics: conversational Bot/QA/IR; knowledge graph/IE; machine learning for NLP; machine translation; and NLP applications. The papers of the second volume are organized as follows: NLP for social network; NLP fundamentals; text mining; and short papers.