Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

An API-based search system for one click access to information

This paper proposes a prototype One Click access system, based on previous work in the field and the related 1CLICK-2@NTCIR10 task. The proposed solution integrates methods from into a three tier algorithm: query categorization, information extraction and output generation and offers suggestions on how each of these can be implemented. Finally, a thorough user-based evaluation concludes that such an information retrieval system outperforms the textual preview collected from Google search results, based on a paired sign test. Based on validation results suggestions for future improvements are proposed

Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

The evaluation and assessment of conversational interfaces is a complex task since such software products are challenging to validate through traditional testing approaches. We conducted a systematic Multivocal Literature Review (MLR), on five different literature sources, to provide a view on quality attributes, evaluation frameworks, and evaluation datasets proposed to provide aid to the researchers and practitioners of the field. We came up with a final pool of 118 contributions, including grey (35) and white literature (83). We categorized 123 different quality attributes and metrics under ten different categories and four macro-categories: Relational, Conversational, User-Centered and Quantitative attributes. While Relational and Conversational attributes are most commonly explored by the scientific literature, we testified a predominance of User-Centered Attributes in industrial literature. We also identified five different academic frameworks/tools to automatically compute sets of metrics, and 28 datasets (subdivided into seven different categories based on the type of data contained) that can produce conversations for the evaluation of conversational interfaces. Our analysis of literature highlights that a high number of qualitative and quantitative attributes are available in the literature to evaluate the performance of conversational interfaces. Our categorization can serve as a valid entry point for researchers and practitioners to select the proper functional and non-functional aspects to be evaluated for their products

Combining granularity-based topic-dependent and topic-independent evidences for opinion detection

Fouille des opinion, une sous-discipline dans la recherche d'information (IR) et la linguistique computationnelle, fait référence aux techniques de calcul pour l'extraction, la classification, la compréhension et l'évaluation des opinions exprimées par diverses sources de nouvelles en ligne, social commentaires des médias, et tout autre contenu généré par l'utilisateur. Il est également connu par de nombreux autres termes comme trouver l'opinion, la détection d'opinion, l'analyse des sentiments, la classification sentiment, de détection de polarité, etc. Définition dans le contexte plus spécifique et plus simple, fouille des opinion est la tâche de récupération des opinions contre son besoin aussi exprimé par l'utilisateur sous la forme d'une requête. Il y a de nombreux problèmes et défis liés à l'activité fouille des opinion. Dans cette thèse, nous nous concentrons sur quelques problèmes d'analyse d'opinion. L'un des défis majeurs de fouille des opinion est de trouver des opinions concernant spécifiquement le sujet donné (requête). Un document peut contenir des informations sur de nombreux sujets à la fois et il est possible qu'elle contienne opiniâtre texte sur chacun des sujet ou sur seulement quelques-uns. Par conséquent, il devient très important de choisir les segments du document pertinentes à sujet avec leurs opinions correspondantes. Nous abordons ce problème sur deux niveaux de granularité, des phrases et des passages. Dans notre première approche de niveau de phrase, nous utilisons des relations sémantiques de WordNet pour trouver cette association entre sujet et opinion. Dans notre deuxième approche pour le niveau de passage, nous utilisons plus robuste modèle de RI i.e. la language modèle de se concentrer sur ce problème. L'idée de base derrière les deux contributions pour l'association d'opinion-sujet est que si un document contient plus segments textuels (phrases ou passages) opiniâtre et pertinentes à sujet, il est plus opiniâtre qu'un document avec moins segments textuels opiniâtre et pertinentes. La plupart des approches d'apprentissage-machine basée à fouille des opinion sont dépendants du domaine i.e. leurs performances varient d'un domaine à d'autre. D'autre part, une approche indépendant de domaine ou un sujet est plus généralisée et peut maintenir son efficacité dans différents domaines. Cependant, les approches indépendant de domaine souffrent de mauvaises performances en général. C'est un grand défi dans le domaine de fouille des opinion à développer une approche qui est plus efficace et généralisé. Nos contributions de cette thèse incluent le développement d'une approche qui utilise de simples fonctions heuristiques pour trouver des documents opiniâtre. Fouille des opinion basée entité devient très populaire parmi les chercheurs de la communauté IR. Il vise à identifier les entités pertinentes pour un sujet donné et d'en extraire les opinions qui leur sont associées à partir d'un ensemble de documents textuels. Toutefois, l'identification et la détermination de la pertinence des entités est déjà une tâche difficile. Nous proposons un système qui prend en compte à la fois l'information de l'article de nouvelles en cours ainsi que des articles antérieurs pertinents afin de détecter les entités les plus importantes dans les nouvelles actuelles. En plus de cela, nous présentons également notre cadre d'analyse d'opinion et tâches relieés. Ce cadre est basée sur les évidences contents et les évidences sociales de la blogosphère pour les tâches de trouver des opinions, de prévision et d'avis de classement multidimensionnel. Cette contribution d'prématurée pose les bases pour nos travaux futurs. L'évaluation de nos méthodes comprennent l'utilisation de TREC 2006 Blog collection et de TREC Novelty track 2004 collection. La plupart des évaluations ont été réalisées dans le cadre de TREC Blog track.Opinion mining is a sub-discipline within Information Retrieval (IR) and Computational Linguistics. It refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online sources like news articles, social media comments, and other user-generated content. It is also known by many other terms like opinion finding, opinion detection, sentiment analysis, sentiment classification, polarity detection, etc. Defining in more specific and simpler context, opinion mining is the task of retrieving opinions on an issue as expressed by the user in the form of a query. There are many problems and challenges associated with the field of opinion mining. In this thesis, we focus on some major problems of opinion mining

Text mining techniques for patent analysis.

Abstract Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus-and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a realworld patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches

A user-centred approach to information retrieval

A user model is a fundamental component in user-centred information retrieval systems. It enables personalization of a user's search experience. The development of such a model involves three phases: collecting information about each user, representing such information, and integrating the model into a retrieval application. Progress in this area is typically met with privacy and scalability challenges that hinder the ability to synthesize collective knowledge from each user's search behaviour. In this thesis, I propose a framework that addresses each of these three phases. The proposed framework is based on social role theory from the social science literature and at the centre of this theory is the concept of a social position. A social position is a label for a group of users with similar behavioural patterns. Examples of such positions are traveller, patient, movie fan, and computer scientist. In this thesis, a social position acts as a label for users who are expected to have similar interests. The proposed framework does not require real users' data; rather it uses the web as a resource to model users. The proposed framework offers a data-driven and modular design for each of the three phases of building a user model. First, I present an approach to identify social positions from natural language sentences. I formulate this task as a binary classification task and develop a method to enumerate candidate social positions. The proposed classifier achieves an accuracy score of 85.8%, which indicates that social positions can be identified with good accuracy. Through an inter-annotator agreement study, I further show a reasonable level of agreement between users when identifying social positions. Second, I introduce a novel topic modelling-based approach to represent each social position as a multinomial distribution over words. This approach estimates a topic from a document collection for each position. To construct such a collection for a particular position, I propose a seeding algorithm that extracts a set of terms relevant to the social position. Coherence-based evaluation shows that the proposed approach learns significantly more coherent representations when compared with a relevance modelling baseline. Third, I present a diversification approach based on the proposed framework. Diversification algorithms aim to return a result list for a search query that would potentially satisfy users with diverse information needs. I propose to identify social positions that are relevant to a search query. These positions act as an implicit representation of the many possible interpretations of the search query. Then, relevant positions are provided to a diversification technique that proportionally diversifies results based on each social position's importance. I evaluate my approach using four test collections provided by the diversity task of the Text REtrieval Conference (TREC) web tracks for 2009, 2010, 2011, and 2012. Results demonstrate that my proposed diversification approach is effective and provides statistically significant improvements over various implicit diversification approaches. Fourth, I introduce a session-based search system under the framework of learning to rank. Such a system aims to improve the retrieval performance for a search query using previous user interactions during the search session. I present a method to match a search session to its most relevant social positions based on the session's interaction data. I then suggest identifying related sessions from query logs that are likely to be issued by users with similar information needs. Novel learning features are then estimated from the session's social positions, related sessions, and interaction data. I evaluate the proposed system using four test collections from the TREC session track. This approach achieves state-of-the-art results compared with effective session-based search systems. I demonstrate that such a strong performance is mainly attributed to features that are derived from social positions' data