Patent retrieval: a literature review <s> Keyword-based methods <s> We systematically investigate a new approach to estimating the parameters of language models for information retrieval, called parsimonious language models. Parsimonious language models explicitly address the relation between levels of language models that are typically used for smoothing. As such, they need fewer (non-zero) parameters to describe the data. We apply parsimonious models at three stages of the retrieval process: 1) at indexing time; 2) at search time; 3) at feedback time. Experimental results show that we are able to build models that are significantly smaller than standard models, but that still perform at least as well as the standard approaches. <s> BIB001 </s> Patent retrieval: a literature review <s> Keyword-based methods <s> This paper describes our patent retrieval system participated in the NTCIR-5 Patent Retrieval Task, Document Retrieval Subtask. The main scope of our method is the appropriate query expansion to improve recall. We extracted query terms from the topic claim, and expanded query terms extracted from sentences explained in the patent document including the topic claim. The explanation sentences were extracted by the method based on pattern matching and by the method based on the longest common subsequence length. <s> BIB002 </s> Patent retrieval: a literature review <s> Keyword-based methods <s> Leveraging information from relevance assessments has been proposed as an effective means for improving retrieval. We introduce a novel language modeling method which uses information from each assessed document and their aggregate. While most previous approaches focus either on features of the entire set or on features of the individual relevant documents, our model exploits features of both the documents and the set as a whole. When evaluated, we show that our model is able to significantly improve over state-of-art feedback methods. <s> BIB003 </s> Patent retrieval: a literature review <s> Keyword-based methods <s> Searching for prior-art patents is an essential step for the patent examiner to validate or invalidate a patent application. In this paper, we consider the whole patent as the query, which reduces the burden on the user, and also makes many more potential search features available. We explore how to automatically transform the query patent into an effective search query, especially focusing on the effect of different patent fields. Experiments show that the background summary of a patent is the most useful source of terms for generating a query, even though most previous work used the patent claims. <s> BIB004 </s> Patent retrieval: a literature review <s> Keyword-based methods <s> In this paper we describe our participation in the 2010 CLEF-IP Prior Art Retrieval task where we examined the impact of information in dierent sections of patent documents, namely the title, abstract, claims, description and IPC-R sections, on the retrieval and re-ranking of patent documents. Using a standard bag-of-words approach in Lemur we found that the IPC-R sections are the most informative for patent retrieval. We then performed a re-ranking of the retrieved documents using a Logistic Regression Model, trained on the retrieved documents in the training set. We found indications that the information contained in the text sections of the patent document can contribute to a better ranking of the retrieved documents. The ocial results have shown that among the nine groups that participated in the Prior Art Retrieval task we achieved the eigth rank in terms of both Mean Average Precision (MAP) and Recall. <s> BIB005 </s> Patent retrieval: a literature review <s> Keyword-based methods <s> In this paper, we report our approach to retrieve patent documents based on the prior art. We use the standard Information Retrieval (IR) techniques which contain indexing and retrieval processes. We use various combinations of document fields for the query formulation. Based on the evaluation summary, we achieve the best result for the combinations of invention-title, description and claims fields in terms of precision and recall. <s> BIB006 </s> Patent retrieval: a literature review <s> Keyword-based methods <s> We present our experiments and results for the DCU CNGL participation in the CLEF-IP 2010 Candidate Patent Search Task. Our work applied standard information retrieval (IR) techniques to patent search. In addition, a very simple citation extraction method was applied to improve the results. This was our second consecutive participation in the CLEF-IP tasks. Our experiments in 2009 showed that many sophisticated approach to IR do not improve the retrieval effectiveness for this task. For this reason of we decided to apply only simple methods in 2010. These were demonstrated to be highly competitive with other participants. DCU submitted three runs for the Prior Art Candidate Search Task, two of these runs achieved the second and third ranks among the 25 runs submitted by nine different participants. Our best run achieved MAP of 0.203, recall of 0.618, and PRES of 0.523. <s> BIB007 </s> Patent retrieval: a literature review <s> Keyword-based methods <s> Prior-art search is a critical step in the examination procedure of a patent application. This study explores automatic query generation from patent documents to facilitate the time-consuming and labor-intensive search for relevant patents. It is essential for this task to identify discriminative terms in different fields of a query patent, which enables us to distinguish relevant patents from non-relevant patents. To this end we investigate the distribution of terms occurring in different fields of the query patent and compare the distributions with the rest of the collection using language modeling estimation techniques. We experiment with term weighting based on the Kullback-Leibler divergence between the query patent and the collection and also with parsimonious language model estimation. Both of these techniques promote words that are common in the query patent and are rare in the collection. We also incorporate the classification assigned to patent documents into our model, to exploit available human judgements in the form of a hierarchical classification. Experimental results show that the retrieval using the generated queries is effective, particularly in terms of recall, while patent description is shown to be the most useful source for extracting query terms. <s> BIB008 </s> Patent retrieval: a literature review <s> Keyword-based methods <s> Patents are used by legal entities to legally protect their inventions and represent a multi-billion dollar industry of licensing and litigation. In 2014, 326,033 patent applications were approved in the US alone -- a number that has doubled in the past 15 years and which makes prior art search a daunting, but necessary task in the patent application process. In this work, we seek to investigate the efficacy of prior art search strategies from the perspective of the inventor who wishes to assess the patentability of their ideas prior to writing a full application. While much of the literature inspired by the evaluation framework of the CLEF-IP competition has aimed to assist patent examiners in assessing prior art for complete patent applications, less of this work has focused on patent search with queries representing partial applications. In the (partial) patent search setting, a query is often much longer than in other standard IR tasks, e.g., the description section may contain hundreds or even thousands of words. While the length of such queries may suggest query reduction strategies to remove irrelevant terms, intentional obfuscation and general language used in patents suggests that it may help to expand queries with additionally relevant terms. To assess the trade-offs among all of these pre-application prior art search strategies, we comparatively evaluate a variety of partial application search and query reformulation methods. Among numerous findings, querying with a full description, perhaps in conjunction with generic (non-patent specific) query reduction methods, is recommended for best performance. However, we also find that querying with an abstract represents the best trade-off in terms of writing effort vs. retrieval efficacy (i.e., querying with the description sections only lead to marginal improvements) and that for such relatively short queries, generic query expansion methods help. <s> BIB009 </s> Patent retrieval: a literature review <s> Keyword-based methods <s> Patent retrieval is a long query task whose aim is to retrieve all documents related to patent applications. However, current approaches face with the term mismatch problem, leading to low retrieval performance. To deal with this issue, we propose a novel automatic query construction approach based on semantic concept importance for effective patent retrieval. In this approach, natural language processing techniques are firstly adopted to analyze patent long query inputs. Then, candidate query concepts are generated according to the concept features. Further, a concept importance-based query construction algorithm is presented to select the representative query concepts. Experimental results on the standard patent dataset demonstrate that our proposed approach can significantly outperform other state-of-art methods. <s> BIB010
This set of techniques retrieves relevant documents by looking for exact matches between search query term(s) and the target data. keyword search operates under the closed vocabulary assumption where vocabulary is derived solely from terms that appear in the target search data. Table 3 shows some keyword-based methods along with their performance results on benchmark datasets. Keyword-based techniques differ in: (1) which elements of the target data are indexed, (2) which query terms are selected/removed, (3) the relative weights of such terms, and (4) the match scoring function. Query reduction (QR): the rationale behind QR approaches is intuitive as patents are very long documents with several sections. Querying with the whole document would be impractical and inefficient. Some query reduction methods are position-based; they select relevant terms based on their position in the patent document BIB005  BIB008  BIB006 . For example, Verberne and D'hondt  used only terms from the claims section on the CLEF-IP 2009 collection. However, the results were moderate in terms in MAP compared to other runs on the same collection. Magdy et al.  experimented using text from different sections of the topic patent on the CLEF-IP 2009 collection. The authors used various combinations of sections including: (1) short sections such as title, abstract, first line of the description, first sentence of the claims, and (2) lengthy sections such as the description and the claims. The authors assigned different weights to each section manually. Their best scores were achieved using a combination of all short sections and post-filtering retrieved documents keeping only those that share the same IPC classification code with the topic patent. The main challenge with such approach is how to assign the respective weight of each section automatically. Moreover, IPC filtering wouldn't be possible when only partial patent application is available for prior art search. Mahdabi et al. BIB008 proposed a position-based query reduction method which selects relevant query terms by building two query language models using various sections of the topic patent: (1) a variant of the weighted log-likelihood model BIB003 , and (2) a model based on the parsimonious language model BIB001 . Their experiments showed that queries constructed from terms in the description section using weighted log-likelihood give better results than other sections which agree with the previous results BIB009 BIB007 BIB004 . The main advantage of this approach is that respective weights of query terms are derived automatically from the query model. However, some challenges still exist regarding tuning the model parameters such as the smoothing parameter which was set heuristically. Query expansion (QE): pattern-based QE was proposed in many studies BIB002  BIB010 . Wang and Lin BIB010 proposed patterns in the form of syntactic rules in order to extract query terms as weighted concepts. Konishi BIB002 proposed a pattern-based query expansion method for the patent invalidity search task on the NTCIR-5 collection. In this task, the initial query of the topic patent was the terms in the claims section. However, rather than using only raw claims terms which are often abstract, Konishi BIB002 , using pattern matching, identifies other specific terms in the description and uses them as expansion terms. First, components of the invention are extracted from the topic claim using handcrafted patterns. Secondly, explanation sentences describing components of the invention are extracted from the description using handcrafted patterns. Thirdly, terms from first and second steps are used as the new query. The results showed that this query expansion approach works better than using terms extracted from the claims section only. The main drawback of this method is its dependency on manually coded patterns to identify potential terms. Meanwhile, it demonstrates the potential of using entities and their relations as retrieval features motivating the need for deeper and more generic linguistic analysis of patent texts.