A Chinese Question Answering System for Single-Relation Factoid Questions

Lai, Yongqing; Jia, Yanyan; Lin, Yang; Feng, Yansong; Zhao, Dongyan

doi:10.1007/978-3-319-73618-1_11

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…At present, the question answering method of knowledge base in Chinese domain is mainly improved based on information retrieval and vector modeling. Lai et al [8] used convolutional neural network to identify semantic features in questions and determined the results through the matching degree of answers and questions; Dai et al [9] proposed a method, which first carries out named entity recognition, then carries out attribute mapping through two-way LSTM [10] based on attention mechanism, and finally selects the answer from the knowledge base based on the results of the first two steps; Chen et al [11] proposed a relationship extraction method integrating artificial rules to improve the accuracy of relationship recognition.…”

Section: Related Workmentioning

confidence: 99%

The Construction of College English Smart Classroom Based on Artificial Intelligence and Big Data

Sun

Sun³

2022

Wireless Communications and Mobile Computing

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In today’s era, the English level of college students is very important. Different English classes can cultivate different English abilities. Smarter classroom is a concept put forward in the new century. This paper proposes the construction of a smarter classroom for college English with artificial intelligence and big data and proposes a deep neural network text semantic matching original model (OM), which uses different lexical information to match different English digital information. Combined with the K-means clustering method, different lexical semantic information is matched. After the comparison of experiments, the combination of the research theory and the algorithm in this paper is effective and has good use value.

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Section: Related Workmentioning

confidence: 99%

The Construction of College English Smart Classroom Based on Artificial Intelligence and Big Data

Sun

Sun³

2022

Wireless Communications and Mobile Computing

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“…Finally, the answer is constructed according to the inferred candidate entities. Lai et al [18] constructed a question answering system that can automatically find the commitment entities and predicates of single relationship problems. After the feature-based entity link component and the word vector-based candidate predicate generation component, a deep convolution neural network is used to reorder the entity predicate pairs, and all intermediate scores are used to select the final prediction answer.…”

Section: Related Workmentioning

confidence: 99%

Towards Knowledge-Based Tourism Chinese Question Answering System

Luo

Huang

et al. 2022

Mathematics

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With the rapid development of the tourism industry, various travel websites are emerging. The tourism question answering system explores a large amount of information from these travel websites to answer tourism questions, which is critical for providing a competitive travel experience. In this paper, we propose a framework that automatically constructs a tourism knowledge graph from a series of travel websites with regard to tourist attractions in Zhejiang province, China. Backed by this domain-specific knowledge base, we developed a tourism question answering system that also incorporates the underlying knowledge from a large-scale language model such as BERT. Experiments on real-world datasets demonstrate that the proposed method outperforms the baseline on various metrics. We also show the effectiveness of each of the question answering components in detail, including the query intent recognition and the answer generation.

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“…to the QA system supported by a Chinese knowledge graph, this system will firstly use natural language processing technologies to convert the problem to mining what is the attribute value of attribute "倡议国 (initiative country)" for the entity "一带一路 (OBOR)", then obtain the attribute value "中国 (China)" in the given knowledge graph and finally return the answer to users. Currently, many researchers are working on the techniques of Knowledge Base Question Answering (KBQA) [29,30] and a lot of QA platforms have been developed in China, and they have already introduced in knowledge graphs to ensure a better user experience, such as robot "Xiaodu" from Baidu Company, robot "Xiaomi" from Alibaba Company, intelligent voice assistant "Siri" from Apple Company and Microsoft's robot "Cortana".…”

Section: Question Answeringmentioning

confidence: 99%

A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

et al. 2018

Sustainability

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With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the "One Belt One Road (OBOR)" initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.Sustainability 2018, 10, 3245 The Development of Knowledge GraphKnowledge graph is essentially originated from semantic network [3]. Semantic network was proposed in late 1950s and early 1960s, and it is a graph-based data structure to store knowledge, where the graph can be directed or undirected. It is quite convenient to utilize semantic network to represent and store natural language sentences, which can be further applied in machine translation, question answering and natural language understanding. In 1970s, lots of works began to study the relationship between semantic network and first-order predicate. For example, Simmons et al. [4] presented an algorithm to convert semantic network to predicate logic, while Schubert [5] proposed a method leveraging semantic network to represent the conjunctions and quantifiers in first-order predicate logic. In 1980s, the mainstream of artificial intelligence research had become knowledge engineering and expert system, especially rule-based expert system. During this period, the theory of semantic network became more mature and lots of research worked on reasoning based on semantic network [6]. More importantly, the research on semantic network started to turn to knowledge representation and reasoning with strict logical semantics. From late 1980s to 1990s, the research on semantic network focused on modeling the relationship between concepts. Based on this, terminology logic and description logic were proposed. The representative wor...

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A Chinese Question Answering System for Single-Relation Factoid Questions

Cited by 11 publications

References 16 publications

The Construction of College English Smart Classroom Based on Artificial Intelligence and Big Data

The Construction of College English Smart Classroom Based on Artificial Intelligence and Big Data

Towards Knowledge-Based Tourism Chinese Question Answering System

A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

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