Patent query reduction using pseudo relevance feedback

We describe and evaluate an implemented system for general-knowledge question answering. The system combines techniques for standard ad-hoc information retrieval (IR), query-dependent text summarization, and shallow syntactic and semantic sentence analysis. In a series of experiments we examine the role of each statistical and linguistic knowledge source in the question-answering system. In contrast to previous results, we find first that statistical knowledge of word co-occurrences as computed by IR vector space methods can be used to quickly and accurately locate the relevant documents for each question. The use of query-dependent text summarization techniques, however, provides only small increases in performance and severely limits recall levels when inaccurate. Nevertheless, it is the text summarization component that allows subsequent linguistic filters to focus on relevant passages. We find that even very weak linguistic knowledge can offer substantial improvements over purely IRbased techniques for question answering, especially when smoothly integrated with statistical preferences computed by the IR subsystems.

A semantic framework for enhancing pseudo-relevance feedback with soft negative sampling and contrastive learning

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.

The Web has become a large repository of documents (or pages) written in many different languages. In this context, traditional information retrieval (IR) techniques cannot be used whenever the user query and the documents being retrieved are in different languages. To address this problem, new cross‐language information retrieval (CLIR) techniques have been proposed. In this work, we describe a method for cross‐language retrieval of medical information. This method combines query terms and related medical concepts obtained automatically through a categorization procedure. The medical concepts are used to create a linguistic abstraction that allows retrieval of information in a language‐independent way, minimizing linguistic problems such as polysemy. To evaluate our method, we carried out experiments using the OHSUMED test collection, whose documents are written in English, with queries expressed in Portuguese, Spanish, and French. The results indicate that our cross‐language retrieval method is as effective as a standard vector space model algorithm operating on queries and documents in the same language. Further, our results are better than previous results in the literature.

Query-document vocabulary mismatch, the lack of query expressiveness for user needs and the phenomenon of short queries are the main issues associated with information retrieval systems. Query Expansion (QE) is one of the well-known alternative for overcoming these problems. It mainly involves finding synonyms or related words for the query terms. There are several approaches in the query expansion field such as statistical and semantic approaches; they focus on expanding the individual query terms rather than the entire query during the expansion process. An other category of approaches deals with the whole query by using a neural approach based on Pseudo Relevance feedback (PRF) documents. In this work, we carried out an ablation study to measure the impact of the classical and semantic (word embedding, order, context) based query expansion on the retrieval performance. The experiments conducted on the Arabic EveTAR dataset reveal that our hybrid proposed approach combining classical (PRF) and transformer (AraBERT) is competitive with the state-of-the-art methods. In fact, the obtained result in terms of the Mean Average Precision (MAP) is up to 0.72.

Along with the exponential growth of text data on the Web, particularly of the user-generated content, comes an increasing need for hierarchically organizing documents, retrieving documents accurately, and discovering evolutionary trends of various popular topics from the data. However, all of these are challenging due to the diversity, heterogeneity, noisiness and time-sensitivity of Web 2.0 data. Motivated by this, we tackle the challenges at a fundamental level, by proposing a novel topic modeling method with ontological guidance. It may be used to discover topic language models formalizing various terms relevant to given topics using the Web data. The topic model takes into account both the ontological relationships amongst the topics defined in a topic taxonomy and also word co-occurrence patterns in the data to automatically identify the portions in the data relevant to the topics. Then, it estimates language models for these topics from these relevant portions. At an application level, we use the topic model to propose novel approaches for three different tasks, namely hierarchical text classification without labeled data, information retrieval with pseudo-relevance feedback, and discovering topic evolutionary trends. Our classification experiment on the IPTC (International Press and Telecommunications Council) taxonomy, containing more than 1100 topics, shows that our approach achieves a performance of 67% in terms of the hierarchical version of the F-1 measure, without using any labeled data. Our retrieval experiments on five benchmark datasets show that compared to baseline retrieval (without pseudo-relevance feed-back), our approach improves on average 39% in terms of mean average precision. Finally, for the last task, using blog data, our approach discovers meaningful insights on how the crowd responds to various news topics such as the language used to discuss each topic, how this language drifts over time, and when the crowd’s focus on a topic increases, reaches a peak, and declines.

ABSTRACTThis article introduces a novel technological distance measure between companies based on their patent portfolios. We describe the technological position or characteristics of companies by applying the framework of language modelling technique in information retrieval on their patent portfolios. The proposed novel approach is an asymmetric measure and better captures the technological characteristic of a company and hence is a more accurate distance measure.

Vocabulary mismatch in Information retrieval can be solved by Query Expansion (QE) techniques. Relevance feedback is a prominent solution to improve recall of retrieval system. Sometimes user may be reluctant and novice in providing feedback to improve the retrieval performance. Pseudo Relevance Feedback (PRF) automates the process. PRF treats top ranked resultant items are relevant and uses them to expand the query, which is not always correct. PRF by local analysis does not give guarantee to feedback positive terms to the system. Use of global analysis to capture the positive feedback is a regular practice in information retrieval process. This paper addresses the limitations of local analysis and global analysis alone by a novel approach that integrates both PRF and WordNet to select good expansion terms. The proposed solution filters the expansion terms and optimizes the expanded query. The proposed work is carried out on a huge Telugu text corpus collected from Wikipedia and other Telugu daily news portals.

This paper describes an overview of the findings of the track named ‘Causality-driven Ad hoc Information Retrieval’ (abbv. CAIR) at the Forum for Information Retrieval Evaluation (FIRE) 2020. The purpose of the track was to investigate how effectively can search systems retrieve documents that are causally related to a specified query event. Different from standard information retrieval (IR), the criteria of relevance in this search scenario is stricter in the sense that the retrieved documents at the top ranks should provide information on the potentially relevant causes that might have caused a given query event, e.g. retrieve documents on political situations that might have led to ‘Brexit’. We released a dataset comprised of a set of 25 queries split into train and test sets. We received submissions from two participating groups. The two main observations from the best performing runs from the two participating groups are that longer queries showed a general trend to yield more causally relevant documents towards top ranks as seen from the results obtained from the first participating group, whereas it turned out that sequence-based text representation for semantically matching the documents with queries did not yield effective retrieval results, thus leaving the scope to develop supervised or semi-supervised methods to address causality-based retrieval.

Standard Information Retrieval (IR) metrics are not well suited for new paradigms like XML or Web IR in which retrievable information units are document elements and/or sets of related documents. Part of the problem stems from the classical hypotheses on the user models: They do not take into account the structural or logical context of document elements or the possibility of navigation between units. This article proposes an explicit and formal user model that encompasses a large variety of user behaviors. Based on this model, we extend the probabilistic precision-recall metric to deal with the new IR paradigms.

The Web enables to have access to silo-ed information describing news articles, often offering a multitude of viewpoints that, once combined, can provide a broader picture of the story being reported on the news. In this paper, we propose an approach that automatically extracts representative features of a news item, namely named entities, from textual content attached to a video item (subtitles) and from a set of documents from the Web collected using entity expansion techniques. Approaches relying on entity expansion generally try to collect and process the important facts behinds a particular news item, but they are often too dependent on frequency-based functions and information retrieval techniques thus neglecting the multi-dimensional relationships that are established among the entities. We propose a concentric-based approach that enables to represent the context of a news item, by harmonizing into a single model the representative entities, which can be extracted using information retrieval and natural language processing techniques (Core), and other entities that get prominent according to different dimensions such as informativeness, semantic connectivity, or popularity (Crust). We compare our approach with a baseline by analyzing the compactness of the generated summary on an existing gold standard available on the Web. Results of the experiments show that our approach converges faster to the ideal compact news snapshot with an improvement of 36.9% over the baseline.

Keyphrase extraction is a task with many applications in information retrieval, text mining, and natural language processing. In this paper, a keyphrase extraction approach based on the naive Bayesian classifier is proposed. To determine whether a phrase is a keyphrase, the following features of a phrase in a given document are adopted: its term frequency, whether to appear in the title, abstract and headings (subheadings), and its frequency appearing in the paragraphs of the given document. The approach is evaluated by the standard information retrieval metrics of precision and recall. Experiment results show that this approach is very practical: it can achieve high precision and recall; especially the recall it can achieve is over 80 percent.

Companies often develop and maintain a collection of product variants that share some common features but also support different, customer-specific features. To reengineering such legacy product variants for systematic reuse, one must identify features and their implementing code units (e.g. functions, files) in different product variants. Information retrieval (IR) techniques may be applied for that purpose. In this paper, we discuss problems that hinder direct application of IR techniques to a collection of product variants. To counter these problems, we present an approach to support effective feature location in product variants. The novelty of our approach is that we exploit commonalities and differences of product variants by software differencing and FCA techniques so that IR technique can achieve satisfactory results for feature location in product variants. We have implemented our approach and conducted evaluation with a collection of nine Linux kernel product variants. Our evaluation shows that our approach always significantly outperforms a direct application of IR technique in the subject product variants.

This chapter provides a perspective on a problem-based learning (PBL) approach to teaching information retrieval (IR) theory, techniques, and practice at under- and postgraduate levels. The educational philosophy of PBL is outlined, and its pertinence to addressing the challenges of teaching IR in terms of both the diverse nature of the subject and also of the students is discussed. It is argued that teaching IR as a solution to a problem (access to information) and as a problem (how can we organize information in IR systems so it can be accessed) provides an engaging and challenging way to teach it to a range of students. It also draws connections between IR theory and techniques and relates IR to other disciplines through showing how similar problems are faced in other subjects. Some specific examples of teaching exercises based on PBL are provided with a discussion and reflection on their effectiveness. Finally assessment and feedback from students are discussed.

Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.