Automatic Query Reformulation Research Articles

How the Quality of Maintenance Tasks is Affected by Criteria for Selecting Engineers for Collaboration

In industry, software projects might span over decades, with many engineers joining or leaving the company over time. In these circumstances, no single engineer has all of the knowledge when maintenance tasks such as Traceability Link Recovery (TLR), Bug Localization (BL), and Feature Location (FL) are performed. Thus, collaboration has the potential to boost the quality of maintenance tasks since the solution advanced by one engineer might be enhanced with contributions from other engineers. However, assembling a team of software engineers to collaborate may not be as intuitive as we might think. In the context of a worldwide industrial supplier of railway solutions, this work evaluates how the quality of TLR, BL, and FL is affected by the criteria for selecting engineers for collaboration. The criteria for collaboration are based on engineers’ profile information to select the set of search queries that are involved in the maintenance task. Collaboration is achieved by applying automatic query reformulation, and the location relies on an evolutionary algorithm. Our work uncovers how software engineers who might be seen as not being relevant in the collaboration can lead to significantly better results. A focus group confirmed the relevance of the findings.

Automatic query reformulation for code search using crowdsourced knowledge

Traditional code search engines (e.g., Krugle) often do not perform well with natural language queries. They mostly apply keyword matching between query and source code. Hence, they need carefully designed queries containing references to relevant APIs for the code search. Unfortunately, preparing an effective search query is not only challenging but also time-consuming for the developers according to existing studies. In this article, we propose a novel query reformulation technique–RACK–that suggests a list of relevant API classes for a natural language query intended for code search. Our technique offers such suggestions by exploiting keyword-API associations from the questions and answers of Stack Overflow (i.e., crowdsourced knowledge). We first motivate our idea using an exploratory study with 19 standard Java API packages and 344K Java related posts from Stack Overflow. Experiments using 175 code search queries randomly chosen from three Java tutorial sites show that our technique recommends correct API classes within the Top-10 results for 83% of the queries, with 46% mean average precision and 54% recall, which are 66%, 79% and 87% higher respectively than that of the state-of-the-art. Reformulations using our suggested API classes improve 64% of the natural language queries and their overall accuracy improves by 19%. Comparisons with three state-of-the-art techniques demonstrate that RACK outperforms them in the query reformulation by a statistically significant margin. Investigation using three web/code search engines shows that our technique can significantly improve their results in the context of code search.

MUSEMBLE: A novel music retrieval system with automatic voice query transcription and reformulation

So far, many researches have been done to develop efficient music retrieval systems, and query-by-humming has been considered as one of the most intuitive and effective query methods for music retrieval. For the voice humming to be a reliable query source, elaborate signal processing and acoustic similarity measurement schemes are necessary. On the other hand, recently, there has been an increased interest in query reformulation using relevance feedback with evolutionary techniques such as genetic algorithm for multimedia information retrieval. However, these techniques have not been exploited widely in the field of music retrieval. In this paper, we develop a novel music retrieval system called MUSEMBLE (MUSic enEMBLE) based on two distinct features: (i) A sung or hummed query is automatically transcribed into a sequence of pitch and duration pairs with improved accuracy for music representation. More specifically, we developed two new and unique techniques called WAE (windowed average energy) and dynamic ADF (amplitude-based difference function) onsets for more accurate note segmentation and onset/offset detection in acoustic signal, respectively. The former improved energy-based approaches such as AE by defining small but coherent windows with local and global threshold values. On the other hand, the latter improved the AF (amplitude function) that calculates the summation of the absolute values of signal differences for the clustering energy contour. (ii) A user query is reformulated using user relevance feedback with a genetic algorithm to improve retrieval performance. Even though we have especially focused on humming queries in this paper, MUSEMBLE provides versatile query and browsing interfaces for various kinds of users. We have carried out extensive experiments on the prototype system to evaluate the performance of our voice query transcription and genetic algorithm-based relevance feedback schemes. We demonstrate that our proposed method improves the retrieval accuracy up to 20–40% compared with other popular RF methods. We also show that both WAE and Dynamic ADF methods improve the transcription accuracy up to 95%.

A syntactically-based query reformulation technique for information retrieval

Whereas in language words of high frequency are generally associated with low content [Bookstein, A., & Swanson, D. (1974). Probabilistic models for automatic indexing. Journal of the American Society of Information Science, 25(5), 312–318; Damerau, F. J. (1965). An experiment in automatic indexing. American Documentation, 16, 283–289; Harter, S. P. (1974). A probabilistic approach to automatic keyword indexing. PhD thesis, University of Chicago; Sparck-Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28, 11–21; Yu, C., & Salton, G. (1976). Precision weighting – an effective automatic indexing method. Journal of the Association for Computer Machinery (ACM), 23(1), 76–88], shallow syntactic fragments of high frequency generally correspond to lexical fragments of high content [Lioma, C., & Ounis, I. (2006). Examining the content load of part of speech blocks for information retrieval. In Proceedings of the international committee on computational linguistics and the association for computational linguistics (COLING/ACL 2006), Sydney, Australia]. We implement this finding to Information Retrieval, as follows. We present a novel automatic query reformulation technique, which is based on shallow syntactic evidence induced from various language samples, and used to enhance the performance of an Information Retrieval system. Firstly, we draw shallow syntactic evidence from language samples of varying size, and compare the effect of language sample size upon retrieval performance, when using our syntactically-based query reformulation (SQR) technique. Secondly, we compare SQR to a state-of-the-art probabilistic pseudo-relevance feedback technique. Additionally, we combine both techniques and evaluate their compatibility. We evaluate our proposed technique across two standard Text REtrieval Conference (TREC) English test collections, and three statistically different weighting models. Experimental results suggest that SQR markedly enhances retrieval performance, and is at least comparable to pseudo-relevance feedback. Notably, the combination of SQR and pseudo-relevance feedback further enhances retrieval performance considerably. These collective experimental results confirm the tenet that high frequency shallow syntactic fragments correspond to content-bearing lexical fragments.

Search improvement via automatic query reformulation

Abstract : Users of online retrieval systems experience many difficulties, particularly with search tactics. User studies have indicated that searchers use vocabulary incorrectly and do not take full advantage of iteration to improve their queries. To address these problems, an expert system for online search assistance was developed. This prototype augments the searching capabilities of novice users by providing automatic query reformulation to improve the search results, and automatic ranking of the retrieved passages to speed the identification of relevant information. Users' search performance using the expert system was compared with their search performance on their own, and their search performance using an online thesaurus. The following conclusions were reached: (1) The expert system significantly reduced the number of queries necessary to find relevant passages compared with the user searching alone or with the thesaurus. (2) The expert system produced marginally significant improvements in precision compared with the user searching on their own. There was no significant difference in the recall achieved by the three system configurations. (3) Overall, the expert system ranked relevant passages above irrelevant passages.

Integrated information retrieval in a knowledge worker support system

This paper describes the design of the information retrieval facilities of an integrated information system called EUROMATH. EUROMATH is an example of a Knowledge Worker Support System: it has been designed specifically to support mathematicians in their research work. EUROMATH is required to provide uniform retrieval facilities for searching in a user's personal data, in a shared database of structured documents and in public, bibliographic databases. The design of information retrieval facilities that satisfy these and other requirements posed several interesting design issues regarding the integration of various retrieval techniques. As well as a uniform query language, designed to be highly usable by the target user group, the retrieval facilities provide expert intermediary functions, i.e. sophisticated support for the retrieval of bibliographic data. This support is achieved using a model of the user, a model of the user's information need and a set of search strategies based on those used by human intermediaries. The expert intermediary facilities include extensive help facilities, automatic query reformulation and browsing of a variety of sources of query terms.