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Two Language Models Using Chinese semantic parsing

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Tsinghua Science and Technology 2006年第5期11卷 582-588页

作者：李明琴王侠王作英 Department of Electronic Engineering Tsinghua University

This paper presents two language models that utilize a Chinese semantic dependency parsing technique for speech recognition. The models are based on a representation of the Chinese semantic structure with dependency relations, A semantic dependency parser was described to automatically tag the semantic class for each word with 90.9% accuracy and parse the sentence semantic dependency structure with 75.8% accuracy. The Chinese semantic parsing technique was applied to structure language models to develop two language models, the semantic dependency model （SDM） and the headword trigram model （HTM）. These language models were evaluated using Chinese speech recognition. The experiments show that both models outperform the word trigram model in terms of the Chinese character recognition error rate.

关键词： language model semantic parsing dependency grammar speech recognition

TSPN: Term-Based semantic Peer-to-Peer Networks

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Wuhan University Journal of Natural Sciences 2012年第1期17卷 31-35页

作者： GAO Guoqiang LI Ruixuan LU Zhengding School of Computer Science and Technology HuazhongUniversity of Science and Technology Wuhan 430074 HubeiChina School of Media and Communication Wuhan TextileUniversity Wuhan 430073 Hubei China

In this paper, we propose Term-based semantic Peerto-Peer Networks （TSPN） to achieve semantic search. For each peer, TSPN builds a full text index of its documents. Through the analysis of resources, TSPN obtains series of terms, and distributes these terms into the network. Thus, TSPN can use query terms to locate appropriate peers to perform semantic search. Moreover, unlike the traditional structured P2P networks, TSPN uses the terms, not the peers, as the logical nodes of DHT. This can withstand the impact of network chum. The experimental results show that TSPN has better performance compared with the existing P2P semantic searching algorithms.

关键词： Term-based semantic Peer-to-Peer Networks (TSPN) peer-to-peer semantic parsing semantic DHT

TSCL-SQL: Two-Stage Curriculum Learning Framework for Text-to-SQL

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Journal of Donghua University(English Edition) 2023年第4期40卷 421-427页

作者：尹枫程路易王秋月王志军杜明徐波 School of Computer Science and Technology Donghua UniversityShanghai 201620China

Text-to-SQL is the task of translating a natural language query into a structured query language. Existing text-to-SQL approaches focus on improving the model’s architecture while ignoring the relationship between queries and table schemas and the differences in difficulty between examples in the dataset. To tackle these challenges, a two-stage curriculum learning framework for text-to-SQL(TSCL-SQL) is proposed in this paper. To exploit the relationship between the queries and the table schemas, a schema identification pre-training task is proposed to make the model choose the correct table schema from a set of candidates for a specific query. To leverage the differences in difficulty between examples, curriculum learning is applied to the text-to-SQL task, accompanied by an automatic curriculum learning solution, including a difficulty scorer and a training scheduler. Experiments show that the framework proposed in this paper is effective.

关键词： text-to-SQL curriculum learning semantic parsing