Interactive query expansion for professional search applications

Knowledge workers (such as healthcare information professionals, patent agents and recruitment professionals) undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expert knowledge to formulate accurate search strategies. Interactive features such as query expansion can play a key role in supporting these tasks. However, generating query suggestions within a professional search context requires that consideration be given to the specialist, structured nature of the search strategies they employ. In this paper, we investigate a variety of query expansion methods applied to a collection of Boolean search strategies used in a variety of real-world professional search tasks. The results demonstrate the utility of context-free distributional language models and the value of using linguistic cues to optimise the balance between precision and recall.

Download Full-text

Combining Language Models with NLP and Interactive Query Expansion

Focused Retrieval and Evaluation - Lecture Notes in Computer Science ◽

10.1007/978-3-642-14556-8_14 ◽

2010 ◽

pp. 122-132 ◽

Cited By ~ 2

Author(s):

Eric SanJuan ◽

Fidelia Ibekwe-SanJuan

Keyword(s):

Query Expansion ◽

Language Models ◽

Interactive Query

Download Full-text

Enhancing Web Search through Query Expansion

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch116 ◽

2011 ◽

pp. 752-757 ◽

Cited By ~ 2

Author(s):

Daniel Crabtree

Keyword(s):

Information Retrieval ◽

Search Engines ◽

Query Expansion ◽

Web Search ◽

User Involvement ◽

Semantic Knowledge ◽

Web Pages ◽

Search Performance ◽

Interactive Query ◽

Web Search Engines

Web search engines help users find relevant web pages by returning a result set containing the pages that best match the user’s query. When the identified pages have low relevance, the query must be refined to capture the search goal more effectively. However, finding appropriate refinement terms is difficult and time consuming for users, so researchers developed query expansion approaches to identify refinement terms automatically. There are two broad approaches to query expansion, automatic query expansion (AQE) and interactive query expansion (IQE) (Ruthven et al., 2003). AQE has no user involvement, which is simpler for the user, but limits its performance. IQE has user involvement, which is more complex for the user, but means it can tackle more problems such as ambiguous queries. Searches fail by finding too many irrelevant pages (low precision) or by finding too few relevant pages (low recall). AQE has a long history in the field of information retrieval, where the focus has been on improving recall (Velez et al., 1997). Unfortunately, AQE often decreased precision as the terms used to expand a query often changed the query’s meaning (Croft and Harper (1979) identified this effect and named it query drift). The problem is that users typically consider just the first few results (Jansen et al., 2005), which makes precision vital to web search performance. In contrast, IQE has historically balanced precision and recall, leading to an earlier uptake within web search. However, like AQE, the precision of IQE approaches needs improvement. Most recently, approaches have started to improve precision by incorporating semantic knowledge.

Download Full-text

Interactive Query Expansion in a Meta-search Engine

Next Generation Information Technologies and Systems - Lecture Notes in Computer Science ◽

10.1007/3-540-48521-x_5 ◽

1999 ◽

pp. 50-57

Author(s):

Claudia Oliveira ◽

Luis Gustavo ◽

Varges Resende ◽

Roberto Lehmann

Keyword(s):

Search Engine ◽

Query Expansion ◽

Interactive Query ◽

Meta Search Engine ◽

Meta Search

Download Full-text

Pushdown Automata in Statistical Machine Translation

Computational Linguistics ◽

10.1162/coli_a_00197 ◽

2014 ◽

Vol 40 (3) ◽

pp. 687-723 ◽

Cited By ~ 3

Author(s):

Cyril Allauzen ◽

Bill Byrne ◽

Adrià de Gispert ◽

Gonzalo Iglesias ◽

Michael Riley

Keyword(s):

Machine Translation ◽

Large Scale ◽

Complexity Analysis ◽

Statistical Machine Translation ◽

Language Model ◽

General Purpose ◽

Language Models ◽

Experimental Conditions ◽

Context Free ◽

Pushdown Automata

This article describes the use of pushdown automata (PDA) in the context of statistical machine translation and alignment under a synchronous context-free grammar. We use PDAs to compactly represent the space of candidate translations generated by the grammar when applied to an input sentence. General-purpose PDA algorithms for replacement, composition, shortest path, and expansion are presented. We describe HiPDT, a hierarchical phrase-based decoder using the PDA representation and these algorithms. We contrast the complexity of this decoder with a decoder based on a finite state automata representation, showing that PDAs provide a more suitable framework to achieve exact decoding for larger synchronous context-free grammars and smaller language models. We assess this experimentally on a large-scale Chinese-to-English alignment and translation task. In translation, we propose a two-pass decoding strategy involving a weaker language model in the first-pass to address the results of PDA complexity analysis. We study in depth the experimental conditions and tradeoffs in which HiPDT can achieve state-of-the-art performance for large-scale SMT.

Download Full-text