Pseudo relevance feedback method based on taylor expansion of retrieval function in NTCIR-3 patent retrieval task

Kishida, Kazuaki

doi:10.3115/1119303.1119307

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…These results are much worse than those reported in previous research [5,4,15]. This is because the previous work evaluated a patent invalidity search task, where the query is a patent claim (one or few sentences).…”

Section: Psuedo Relevance Feedbackcontrasting

confidence: 54%

“…Some of the initial trials for utilizing pseudo relevance feedback (PRF) for QE in patent search are described in [5]. PRF is a standard techniques used to enrich a search query with additional terms from the top ranked documents from an initial retrieval run under the assumption that these documents are relevant [14].…”

Section: Query Expansion For Patent Retrievalmentioning

confidence: 99%

“…Several studies have sought to improve retrieval effectiveness for patent search tasks through QE with relevance feedback techniques [5,4,15,6]. Unfortunately, none of these studies succeeded in achieving a stable significant improvement in retrieval effectiveness.…”

Section: Introductionmentioning

confidence: 99%

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A study on query expansion methods for patent retrieval

Magdy

Jones

2011

Proceedings of the 4th Workshop on Patent Information Retrieval

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Patent retrieval is a recall-oriented search task where the objective is to find all possible relevant documents. Queries in patent retrieval are typically very long since they take the form of a patent claim or even a full patent application in the case of priorart patent search. Nevertheless, there is generally a significant mismatch between the query and the relevant documents, often leading to low retrieval effectiveness. Some previous work has tried to address this mismatch through the application of query expansion (QE) techniques which have generally showed effectiveness for many other retrieval tasks. However, results of QE on patent search have been found to be very disappointing. We present a review of previous investigations of QE in patent retrieval, and explore some of these techniques on a prior-art patent search task. In addition, a novel method for QE using automatically generated synonyms set is presented. While previous QE techniques fail to improve over baseline retrieval, our new approach show statistically better retrieval precision over the baseline, although not for recall. In addition, it proves to be significantly more efficient than existing techniques. An extensive analysis to the results is presented which seeks to better understand situations where these QE techniques succeed or fail.

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Section: Psuedo Relevance Feedbackcontrasting

confidence: 54%

Section: Query Expansion For Patent Retrievalmentioning

confidence: 99%

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A study on query expansion methods for patent retrieval

Magdy

Jones

2011

Proceedings of the 4th Workshop on Patent Information Retrieval

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“…Extracting sub-topics from a full patent description has the effect of making the information need expressed in each sub-query more focused, and the final merging step of interleaving documents retrieved for each sub-query has the effect of addressing each aspect of the claimed invention. PRF for patent prior art search tasks shows a degradation in MAP [9,21,12] primarily because the massive queries lack a specific focus towards a relevance criterion, initial retrieval precision is low, and added terms tend to make the query more ambiguous. However, PRF on the focused query segments expressing a precise information need is expected to benefit retrieval effectiveness.…”

Section: Retrieval With Sub-queriesmentioning

confidence: 99%

Utilizing sub-topical structure of documents for information retrieval

Ganguly

Leveling

Jones

2011

Proceedings of the 4th Workshop on Workshop for Ph.D. Students in Information &Amp; Knowledge Management

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Text segmentation in natural language processing typically refers to the process of decomposing a document into constituent subtopics. Our work centers on the application of text segmentation techniques within information retrieval (IR) tasks. For example, for scoring a document by combining the retrieval scores of its constituent segments, exploiting the proximity of query terms in documents for ad-hoc search, and for question answering (QA), where retrieved passages from multiple documents are aggregated and presented as a single document to a searcher. Feedback in ad hoc IR task is shown to benefit from the use of extracted sentences instead of terms from the pseudo relevant documents for query expansion. Retrieval effectiveness for patent prior art search task is enhanced by applying text segmentation to the patent queries. Another aspect of our work involves augmenting text segmentation techniques to produce segments which are more readable with less unresolved anaphora. This is particularly useful for QA and snippet generation tasks where the objective is to aggregate relevant and novel information from multiple documents satisfying user information need on one hand, and ensuring that the automatically generated content presented to the user is easily readable without reference to the original source document.

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“…However, query expansion is associated with the risk of additional terms contributing to a drift in the original information need followed by a degradation of retrieval effectiveness in the feedback step [13]. Unfortunately all existing work on PRF coupled with query expansion for patent prior art search tasks report a degradation in MAP [5,12,8].…”

Section: Related Workmentioning

confidence: 99%

Patent query reduction using pseudo relevance feedback

Ganguly

Leveling

Magdy

et al. 2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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Queries in patent prior art search, being full patent applications, are very much longer than standard ad hoc search and web search topics. Standard information retrieval (IR) techniques are not entirely effective for patent prior art search because of the presence of ambiguous terms in these massive queries. Reducing patent queries by extracting small numbers of key terms has been shown to be ineffective mainly because it is not clear what the focus of the query is. An optimal query reduction algorithm must thus seek to retain the useful terms for retrieval favouring recall of relevant patents, but remove terms which impair retrieval effectiveness. We propose a new query reduction technique decomposing a patent application into constituent text segments and computing the Language Modeling (LM) similarities by calculating the probability of generating each segment from the top ranked documents. We reduce a patent query by removing the least similar segments from the query, hypothesizing that removal of segments most dissimilar to the pseudo-relevant documents can increase the precision of retrieval by removing nonuseful context, while still retaining the useful context to achieve high recall as well. Experiments on the patent prior art search collection CLEF-IP 2010, show that the proposed method outperforms standard pseudo relevance feedback (PRF) and a naive method of query reduction based on removal of unit frequency terms (UFTs).

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Pseudo relevance feedback method based on taylor expansion of retrieval function in NTCIR-3 patent retrieval task

Cited by 8 publications

References 11 publications

A study on query expansion methods for patent retrieval

A study on query expansion methods for patent retrieval

Utilizing sub-topical structure of documents for information retrieval

Patent query reduction using pseudo relevance feedback

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