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 se...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.展开更多
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 qu...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.展开更多
Syntactic and semantic parsing has been investigated for decades,which is one primary topic in the natural language processing community.This article aims for a brief survey on this topic.The parsing community include...Syntactic and semantic parsing has been investigated for decades,which is one primary topic in the natural language processing community.This article aims for a brief survey on this topic.The parsing community includes many tasks,which are difficult to be covered fully.Here we focus on two of the most popular formalizations of parsing:constituent parsing and dependency parsing.Constituent parsing is majorly targeted to syntactic analysis,and dependency parsing can handle both syntactic and semantic analysis.This article briefly reviews the representative models of constituent parsing and dependency parsing,and also dependency graph parsing with rich semantics.Besides,we also review the closely-related topics such as cross-domain,cross-lingual and joint parsing models,parser application as well as corpus development of parsing in the article.展开更多
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 r...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.展开更多
基金Supported by the National Natural Science Foundation of China( 60873225, 60773191, 70771043)National High Technology Research and Development Program of China ( 2007AA01Z403)Wuhan Youth Science and Technology Chenguang Program (200950431171)
文摘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.
基金Fundamental Research Funds for the Central Universities,China (No. 2232023D-19)。
文摘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.
基金the National Natural Science Foundation of China(Grant Nos.61602160 and 61672211)。
文摘Syntactic and semantic parsing has been investigated for decades,which is one primary topic in the natural language processing community.This article aims for a brief survey on this topic.The parsing community includes many tasks,which are difficult to be covered fully.Here we focus on two of the most popular formalizations of parsing:constituent parsing and dependency parsing.Constituent parsing is majorly targeted to syntactic analysis,and dependency parsing can handle both syntactic and semantic analysis.This article briefly reviews the representative models of constituent parsing and dependency parsing,and also dependency graph parsing with rich semantics.Besides,we also review the closely-related topics such as cross-domain,cross-lingual and joint parsing models,parser application as well as corpus development of parsing in the article.
基金Supported by the National High-Tech Research and Development (863) Program of China (No. 2004AA114011-2)
文摘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.
