A quasi-synchronous dependence model for information retrieval

Park, Jae Hyun; Croft, W. Bruce; Smith, David A.

doi:10.1145/2063576.2063585

Cited by 25 publications

(12 citation statements)

References 26 publications

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“…Gao et al [13] propose a method of extracting dependencies using a linkage grammar. Park et al [20] use quasi-synchronous parsing to generate syntactic parse trees, from which dependencies are extracted. Maxwell and Croft [17] have recently shown retrieval performance can be improved through the use of dependency parsing techniques in the extraction of non-adjacent subsets of dependent terms.…”

Section: Background and Related Work 21 Identification Of Dependent mentioning

confidence: 99%

A Comparison of Retrieval Models using Term Dependencies

Huston

Croft

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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A number of retrieval models incorporating term dependencies have recently been introduced. Most of these modify existing "bag-ofwords" retrieval models by including features based on the proximity of pairs of terms (or bi-terms). Although these term dependency models have been shown to be significantly more effective than the bag-of-words models, there have been no previous systematic comparisons between the different approaches that have been proposed. In this paper, we compare the effectiveness of recent bi-term dependency models over a range of TREC collections, for both short (title) and long (description) queries. To ensure the reproducibility of our study, all experiments are performed on widely available TREC collections, and all tuned retrieval model parameters are made public. These comparisons show that the weighted sequential dependence model is at least as effective as, and often significantly better than, any other model across this range of collections and queries. We observe that dependency features are much more valuable in improving the performance of longer queries than for shorter queries. We then examine the effectiveness of dependence models that incorporate proximity features involving more than two terms. The results show that these features can improve effectiveness, but not consistently, over the available data sets.

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Section: Background and Related Work 21 Identification Of Dependent mentioning

confidence: 99%

A Comparison of Retrieval Models using Term Dependencies

Huston

Croft

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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“…Another direction that is relevant to our work is the attempt to improve information retrieval using NLP techniques [35,36,1,18,6,22,26,30,31]. Out of this large body of works, we present those that employ the syntactic analysis of queries and documents that are most relevant to our work.…”

Section: Syntactic Analysis For Irmentioning

confidence: 99%

“…Term weights were determined by taking into account grammatical relationships between the query terms, in addition to traditional statistical based term features. Park et al [31] align the syntactic parsed trees of the query and the content via matches between different types of syntactic relations in the document and the query.…”

Section: Syntactic Analysis For Irmentioning

confidence: 99%

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Improving Term Weighting for Community Question Answering Search Using Syntactic Analysis

Carmel¹,

Mejer²,

Pinter³

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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Query term weighting is a fundamental task in information retrieval and most popular term weighting schemes are primarily based on statistical analysis of term occurrences within the document collection. In this work we study how term weighting may benefit from syntactic analysis of the corpus. Focusing on Community-based Question Answering (CQA) sites, we take into account the syntactic function of the terms within CQA texts as an important factor affecting their relative importance for retrieval. We analyze a large log of web queries that landed on Yahoo Answers site, showing a strong deviation between the tendencies of different document words to appear in a landing (click-through) query given their syntactic function. To this end, we propose a novel term weighting method that makes use of the syntactic information available for each query term occurrence in the document, on top of term occurrence statistics. The relative importance of each feature is learned via a learning to rank algorithm that utilizes a click-through query log. We examine the new weighting scheme using manual evaluation based on editorial data and using automatic evaluation over the query log. Our experimental results show consistent improvement in retrieval when syntactic information is taken into account.

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“…There are a variety of models that more selectively extract key concepts and other linguistic features that contain 2 or more terms from queries [Bendersky and Croft 2008;Maxwell and Croft 2013;Park et al 2011;Xue and Croft 2010]. The efficiency of each of these models could benefit from direct access to the n-gram statistics provided by the sketch index.…”

Section: Retrievalmentioning

confidence: 99%

Indexing Word Sequences for Ranked Retrieval

Huston

Culpepper

Croft

2014

ACM Trans. Inf. Syst.

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Formulating and processing phrases and other term dependencies to improve query effectiveness is an important problem in information retrieval. However, accessing word-sequence statistics using inverted indexes requires unreasonable processing time or substantial space overhead. Establishing a balance between these competing space and time trade-offs can dramatically improve system performance.In this paper, we present and analyze a new index structure designed to improve query efficiency in dependency retrieval models. By adapting a class of ( , δ)-approximation algorithms originally proposed for sketch summarization in networking applications, we show how to accurately estimate statistics important in term dependency models with low, probabilistically bounded error rates. The space requirements for the vocabulary of the index is only logarithmically linked to the size of the vocabulary.Empirically, we show that the sketch index can reduce the space requirements of the vocabulary component of an index of n-grams consisting of between 1 and 4 words extracted from the GOV2 collection to less than 0.01% of the space requirements of the vocabulary of a full index. We also show that larger n-gram queries can be processed considerably more efficiently than in current alternatives, such as positional and next-word indexes.

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A quasi-synchronous dependence model for information retrieval

Cited by 25 publications

References 26 publications

A Comparison of Retrieval Models using Term Dependencies

A Comparison of Retrieval Models using Term Dependencies

Improving Term Weighting for Community Question Answering Search Using Syntactic Analysis

Indexing Word Sequences for Ranked Retrieval

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