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PatentQ&A: Proposal of Patent Q&A Neural Search System Using Transformer Model
Yoonmin Lee, Taewook Hwang, Sangkeun Jung, Hyein Seo, Yoonhyung Roh
http://doi.org/10.5626/JOK.2023.50.4.306
Recent neural network search has enabled semantic search beyond search based on statistical methods, and finds accurate search results even with typos. This paper proposes a neural network-based patentQ&A search system that provides the closest answer to the user"s question intention when a general public without patent expertise searches for patent information using general terms. A patent dataset was constructed using patent customer consultation data posted on the Korean Intellectual Property Office website. Patent-KoBERT (Triplet) and Patent-KoBERT (CrossEntropy) were fine-tuned as patent datasets were used to extract similar questions to questions entered by the user and re-rank them. As a result of the experiment, values of Mean Reciprocal Rank (MRR) and Mean Average Precision (MAP) were 0.96, confirming that answers most similar to the intention of the user input were well selected.
Semantic Similarity-based Intent Analysis using Pre-trained Transformer for Natural Language Understanding
Sangkeun Jung, Hyein Seo, Hyunji Kim, Taewook Hwang
http://doi.org/10.5626/JOK.2020.47.8.748
Natural language understanding (NLU) is a central technique applied to developing robot, smart messenger, and natural interface. In this study, we propose a novel similarity-based intent analysis method instead of the typical classification methods for intent analysis problems in the NLU. To accomplish this, the neural network-based text and semantic frame readers are introduced to learn semantic vectors using pairwise text-semantic frame instances. The text to vector and the semantic frame to vector projection methods using the pre-trained transformer are proposed. Then, we propose a method of attaching the intention tag of the nearest training sentence to the query sentence by measuring the semantic vector distances in the vector space. Four experiments on the natural language learning suggest that the proposed method demonstrates superior performance compared to the existing intention analysis techniques. These four experiments use natural language corpora in Korean and English. The two experiments in Korean are weather and navigation language corpora, and the two English-based experiments involve air travel information systems and voice platform language corpora.
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