FAQ Retrieval System Research Articles

Car FAQ Assistant Based on BILSTM-Siamese Network

With the development of artificial intelligence, the automatic question answering technology has been paid more and more attention. Along with the technology’s maturing, the problem is gradually exposed. This technique has several major difficulties. There is semantic recognition. How accurate are the answers to the questions? Because Chinese grammar is relatively complex. There are many different ways of saying the same thing. There is a powerful challenge to semantic recognition. Due to poor semantic recognition, the accuracy of the corresponding questions and answers is low. Aiming at this kind of questions, we build a Q & A library for common use of cars. Firstly, TFIDF method is used to build a basic FAQ retrieval system. Then we use BILSTM-siamese network to construct a semantic similarity model. The accuracy rate on the test set was 99.52%. The final FAQ system: first retrieves 30 most similar questions using the TFIDF model, then uses BILSTM-siamese network matching and returns the answer of the most similar question. The system has a lot to be improved. For example, how to combine this system with speech recognition? How to realize speech recognition will be a great challenge.

A review on the Detection of Missing Content Queries in FAQ Retrieval Systems

When developing an automated FAQ retrieval system, the information supplier constructs question candidates in advance using their own knowledge. Then they answer these question candidates to create question-answer pairs to use in the FAQ retrieval system. However, these question-answer pairs will not always satisfy the users’ information needs. When there is no relevant question–answer pair to a users’ query, such a user may submit various query reformulations browsing over the long results list and may abandon the search before their information need has been satisfied. Such users many never return to use the system again because of the inability of the system to return relevant question-answer pairs to their query. In order to alleviate this, modern automated FAQ retrieval systems use a Missing Content Query (MCQ) detection subsystem to detect those queries that do not have the relevant question–answer pair. In this article we conduct a review of the different approaches proposed in the literature for detecting these MCQs. In particular, we provide a comprehensive review of the different systems that deployed the binary classification approach, the thresholding approach and the hybrid approach in the detection of MCQs. Moreover, we describe the strength and weaknesses of each approach.

Learning regular expressions to template-based FAQ retrieval systems

Template-based approaches have proven to be one of the most efficient and robustest ways of addressing Question Answering problems. Templates embody the expert’s knowledge on the domain and his/her ability to understand and answer questions, but designing these templates may become a complex task since it is usually carried out manually. Although these methods are not automatic, companies may prefer to undertake this solution in order to offer a better service. In this article, we propose a semiautomatic method to reduce the problem of creating templates to that of validate, and possibly modify, a list of proposed templates. In this way, a better trade-off between reliability—the system is still monitored by an expert—and cost is achieved. In addition, updating templates after domain changes becomes easier, human mistakes are reduced, and portability is increased. Our proposal is based on inferring regular expressions that induce the language conveyed by a set of previously collected query reformulations. The main contribution of this work consists of the definition of a suitable optimisation measure that effectively reflects some important aspects of the problem and the theoretical soundness that supports it.

Developing of an ontological interface agent with template-based linguistic processing technique for FAQ services

This paper proposes an ontological Interface agent which works as an assistant between the users and FAQ systems. We integrated several interesting techniques including domain ontology, user modeling, and template-based linguistic processing to effectively tackle the problems associated with traditional FAQ retrieval systems. Specifically, we address the following issues. Firstly, how can an interface agent learn a user’s specialty in order to build a proper user model for him/her? Secondly, how can domain ontology help in establishing user models, analyzing user query, and assisting and guiding interface usage? Finally, how can the intention and focus of a user be correctly extracted? Our work features an template-based linguistic processing technique for developing ontological interface agents; a nature language query mode, along with an improved keyword-based query mode; and an assistance and guidance for human–machine interaction. Our preliminary experimentation demonstrates that user intention and focus of up to eighty percent of the user queries can be correctly understood by the system, and accordingly provides the query solutions with higher user satisfaction.

Cluster-Based FAQ Retrieval Using Latent Term Weights

A high-performance FAQ retrieval system uses query-log clustering to resolve lexical-disagreement problems. The proposed system outperforms traditional information-retrieval systems in FAQ retrieval.

A reliable FAQ retrieval system using a query log classification technique based on latent semantic analysis

To obtain high performances, previous works on FAQ retrieval used high-level knowledge bases or handcrafted rules. However, it is a time and effort consuming job to construct these knowledge bases and rules whenever application domains are changed. To overcome this problem, we propose a high-performance FAQ retrieval system only using users’ query logs as knowledge sources. During indexing time, the proposed system efficiently clusters users’ query logs using classification techniques based on latent semantic analysis. During retrieval time, the proposed system smoothes FAQs using the query log clusters. In the experiment, the proposed system outperformed the conventional information retrieval systems in FAQ retrieval. Based on various experiments, we found that the proposed system could alleviate critical lexical disagreement problems in short document retrieval. In addition, we believe that the proposed system is more practical and reliable than the previous FAQ retrieval systems because it uses only data-driven methods without high-level knowledge sources.

High-performance FAQ retrieval using an automatic clustering method of query logs

To resolve some of lexical disagreement problems between queries and FAQs, we propose a reliable FAQ retrieval system using query log clustering. On indexing time, the proposed system clusters the logs of users' queries into predefined FAQ categories. To increase the precision and the recall rate of clustering, the proposed system adopts a new similarity measure using a machine readable dictionary. On searching time, the proposed system calculates the similarities between users' queries and each cluster in order to smooth FAQs. By virtue of the cluster-based retrieval technique, the proposed system could partially bridge lexical chasms between queries and FAQs. In addition, the proposed system outperforms the traditional information retrieval systems in FAQ retrieval.