Patent retrieval: a literature review <s> Method <s> Prior-art search is an important task in patent retrieval. The success of this task relies upon the selection of relevant search queries. Typically terms for prior-art queries are extracted from the claim fields of query patents. However, due to the complex technical structure of patents, and presence of terms mismatch and vague terms, selecting relevant terms for queries is a difficult task. During evaluating the patents retrievability coverage of prior-art queries generated from query patents, a large bias toward a subset of the collection is experienced. A large number of patents either have a very low retrievability score or can not be discovered via any query. To increase the retrievability of patents, in this paper we expand prior-art queries generated from query patents using query expansion with pseudo relevance feedback. Missing terms from query patents are discovered from feedback patents, and better patents for relevance feedback are identified using a novel approach for checking their similarity with query patents. We specifically focus on how to automatically select better terms from query patents based on their proximity distribution with prior-art queries that are used as features for computing similarity. Our results show, that the coverage of prior-art queries can be increased significantly by incorporating relevant queries terms using query expansion. <s> BIB001 </s> Patent retrieval: a literature review <s> Method <s> Queries in patent prior art search are full patent applications and 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 ambiguous terms in these massive queries. Reducing patent queries by extracting 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 IR 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, hypothesising that removal of these segments can increase the precision of retrieval, while still retaining the useful context to achieve high recall. 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). <s> BIB002 </s> Patent retrieval: a literature review <s> Method <s> Pseudo-relevance feedback (PRF) is an effective approach in Information Retrieval but unfortunately many experiments have shown that PRF is ineffective in patent retrieval. This is because the quality of initial results in the patent retrieval is poor and therefore estimating a relevance model via PRF often hurts the retrieval performance due to off-topic terms. We propose a learning to rank framework for estimating the effectiveness of a patent document in terms of its performance in PRF. Specifically, the knowledge of effective feedback documents on past queries is used to estimate effective feedback documents for new queries. This is achieved by introducing features correlated with feedback document effectiveness. We use patent-specific contents to define such features. We then apply regression to predict document effectiveness given the proposed features. We evaluated the effectiveness of the proposed method on the patent prior art search collection CLEF-IP 2010. Our experimental results show significantly improved retrieval accuracy over a PRF baseline which expands the query using all top-ranked documents. <s> BIB003 </s> Patent retrieval: a literature review <s> Method <s> 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. <s> BIB004
To address the problem of poor PRF results in patent retrieval compared to traditional information retrieval, Bashir and Rauber BIB001 proposed a novel approach for PRF-based query expansion which builds a model that learns to identify better PRF results based on their similarity with the query patent over specific terms. These terms are learned by building a classification model that classifies whether a term would be useful for query expansion or not according to some proximity features between the original query terms and pseudo-relevant terms. The authors, through experiments on a subset of USPTO patents, showed the ability of this model to introduce more relevant query expansion terms and subsequently increasing the retrievability of individual patents. However, the authors did not evaluate this model on any of the available test collections. Moreover, extracting similarity features and computing similarities with PRF results during query execution are computationally expensive and timeconsuming. Along the same efforts, Mahdabi and Crestani BIB003 proposed a framework for identifying effective PRF documents at runtime and then performing query expansion using terms from these relevant documents. The authors first proposed patent-specific features and then used them to build a regression model which calculates a relevancy score of each PRF document. Though results on the CLEF-IP 2010 collection were encouraging, several challenges still exist, for example, the computational complexity of calculating the regression model features at runtime. And PRF parameters tuning (e.g., number of PRF documents to use). Ganguly et al. BIB002 proposed a PRF approach which utilizes a language model for query reduction of long queries composed of full patent applications. The authors argued that naive application of PRF to expand query terms could add noisy terms causing query-topic drift. Moreover, naive removal of terms that has unit term frequency in the query could cause removal of useful terms and thus hurt retrieval effectiveness. Instead, the authors proposed a PRF-based query reduction technique which generates language model similarity scores between query segments (sentences or n-grams) and top ranked results. Segments with top scores are kept and all others are removed. Results on the English subset of the CLEF-IP 2010 collection showed that the proposed approach outperforms the baselines. Parameter tuning is still the main downside of this technique. The performance of the proposed approach was unstable compared to the baselines with different parameter values, specifically the window size, the number of pseudo-relevant documents, and the fraction of terms to retain. Golestan Far et al. BIB004 provided a study on hybrid QRE which aims to automatically approximate the optimalQ by careful selection/expansion of relevant query terms. To motivate the efficacy of QRE on retrieval performance, the authors first designed an experiment where relevance judgments of a query patent Q were assumed to be known in advance. After running Q, using PRF on top-k documents, only terms that are more frequent in retrieved relevant documents (those from relevance judgments) than irrelevant documents are kept and used asQ. Then, querying usingQ achieved a better performance than state of the art on the English subset of CLEF-IP 2010 collection. To approximateQ automatically, the authors proposed four different methods hoping to identify relevant vs. irrelevant terms in Q by: (1) removing terms with high document frequency in the top-100 retrieved documents, (2) removing infrequent terms in Q, (3) using frequent terms in relevant documents assuming the top-5 retrieved documents are relevant, and (4) performing query reduction on Q using IPC definitions as stop-words. All of the four methods failed to perform better than the keywordbased baseline. More interestingly, the authors demonstrated that baseline performance can be doubled if only one relevant document was manually provided by the user. This last observation motivates the need for interactive QRE as a simple and effective method for patent retrieval.