Simple vs. Sophisticated Approaches for Patent Prior-Art Search

Magdy, Walid; Lopez, Patrice; Jones, Gareth

doi:10.1007/978-3-642-20161-5_80

Cited by 24 publications

(12 citation statements)

References 2 publications

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“…Both the methods are compared against a baseline of using a simple frequency cut-off filter to remove unit frequency terms from patent queries, shown to work well in previous work [13].…”

Section: Query Segmentationmentioning

confidence: 94%

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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“…Both the methods are compared against a baseline of using a simple frequency cut-off filter to remove unit frequency terms from patent queries, shown to work well in previous work [13].…”

Section: Query Segmentationmentioning

confidence: 94%

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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“…Magdy et al [9] and Bouadjenek et al [2] studied different query expansion and reduction techniques for patent search on CLEF-IP 2010, and reported little improvement with automatic methods. Magdy et al [10] further compare the best two systems in CLEF-IP 2010.…”

Section: Related Workmentioning

confidence: 99%

On Term Selection Techniques for Patent Prior Art Search

Far

Sanner

Bouadjenek

et al. 2015

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval

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In this paper, we investigate the influence of term selection on retrieval performance on the CLEF-IP prior art test collection, using the Description section of the patent query with Language Model (LM) and BM25 scoring functions. We find that an oracular relevance feedback system that extracts terms from the judged relevant documents far outperforms the baseline and performs twice as well on MAP as the best competitor in CLEF-IP 2010. We find a very clear term selection value threshold for use when choosing terms. We also noticed that most of the useful feedback terms are actually present in the original query and hypothesized that the baseline system could be substantially improved by removing negative query terms. We tried four simple automated approaches to identify negative terms for query reduction but we were unable to notably improve on the baseline performance with any of them. However, we show that a simple, minimal interactive relevance feedback approach where terms are selected from only the first retrieved relevant document outperforms the best result from CLEF-IP 2010 suggesting the promise of interactive methods for term selection in patent prior art search.

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“…Each topic is a full patent application which can be tens of pages in length. For our experiments we used a simple state-ofthe-art query formulation technique presented in [12]. The applied search query was constructed from terms in the description section of the patent topic by filtering out terms that appeared only once, and including term bigrams appearing in the title and abstract sections of the query patent more than once.…”

Section: Testing Standard Query Expansion Techniques With Patent Retrmentioning

confidence: 99%

“…The Indri 1 toolkit was used for the retrieval process. It has been shown that citation extraction techniques for the prior-art patent search task improve the results significantly [13,12]. However, in our experiments, we do not apply any of these extraction techniques, since our focus is the retrieval algorithm itself.…”

Section: Testing Standard Query Expansion Techniques With Patent Retrmentioning

confidence: 99%

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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Simple vs. Sophisticated Approaches for Patent Prior-Art Search

Cited by 24 publications

References 2 publications

Utilizing sub-topical structure of documents for information retrieval

Utilizing sub-topical structure of documents for information retrieval

On Term Selection Techniques for Patent Prior Art Search

A study on query expansion methods for patent retrieval

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