Linking Textual Resources to Support Information Discovery

A vast amount of information is today stored in the form of textual documents, many of which are available online. These documents come from different sources and are of different types. They include newspaper articles, books, corporate reports, encyclopedia entries and research papers. At a semantic level, these documents contain knowledge, which was created by explicitly connecting information and expressing it in the form of a natural language. However, a significant amount of knowledge is not explicitly stated in a single document, yet can be derived or discovered by researching, i.e. accessing, comparing, contrasting and analysing, information from multiple documents. Carrying out this work using traditional search interfaces is tedious due to information overload and the difficulty of formulating queries that would help us to discover information we are not aware of. In order to support this exploratory process, we need to be able to effectively navigate between related pieces of information across documents. While information can be connected using manually curated cross-document links, this approach not only does not scale, but cannot systematically assist us in the discovery of sometimes non-obvious (hidden) relationships. Consequently, there is a need for automatic approaches to link discovery. This work studies how people link content, investigates the properties of different link types, presents new methods for automatic link discovery and designs a system in which link discovery is applied on a collection of millions of documents to improve access to public knowledge

Joint Discourse-aware Concept Disambiguation and Clustering

This thesis addresses the tasks of concept disambiguation and clustering. Concept disambiguation is the task of linking common nouns and proper names in a text – henceforth called mentions – to their corresponding concepts in a predefined inventory. Concept clustering is the task of clustering mentions, so that all mentions in one cluster denote the same concept. In this thesis, we investigate concept disambiguation and clustering from a discourse perspective and propose a discourse-aware approach for joint concept disambiguation and clustering in the framework of Markov logic. The contributions of this thesis are fourfold: Joint Concept Disambiguation and Clustering. In previous approaches, concept disambiguation and concept clustering have been considered as two separate tasks (Schütze, 1998; Ji & Grishman, 2011). We analyze the relationship between concept disambiguation and concept clustering and argue that these two tasks can mutually support each other. We propose the – to our knowledge – first joint approach for concept disambiguation and clustering. Discourse-Aware Concept Disambiguation. One of the determining factors for concept disambiguation and clustering is the context definition. Most previous approaches use the same context definition for all mentions (Milne & Witten, 2008b; Kulkarni et al., 2009; Ratinov et al., 2011, inter alia). We approach the question which context is relevant to disambiguate a mention from a discourse perspective and state that different mentions require different notions of contexts. We state that the context that is relevant to disambiguate a mention depends on its embedding into discourse. However, how a mention is embedded into discourse depends on its denoted concept. Hence, the identification of the denoted concept and the relevant concept mutually depend on each other. We propose a binwise approach with three different context definitions and model the selection of the context definition and the disambiguation jointly. Modeling Interdependencies with Markov Logic. To model the interdependencies between concept disambiguation and concept clustering as well as the interdependencies between the context definition and the disambiguation, we use Markov logic (Domingos & Lowd, 2009). Markov logic combines first order logic with probabilities and allows us to concisely formalize these interdependencies. We investigate how we can balance between linguistic appropriateness and time efficiency and propose a hybrid approach that combines joint inference with aggregation techniques. Concept Disambiguation and Clustering beyond English: Multi- and Cross-linguality. Given the vast amount of texts written in different languages, the capability to extend an approach to cope with other languages than English is essential. We thus analyze how our approach copes with other languages than English and show that our approach largely scales across languages, even without retraining. Our approach is evaluated on multiple data sets originating from different sources (e.g. news, web) and across multiple languages. As an inventory, we use Wikipedia. We compare our approach to other approaches and show that it achieves state-of-the-art results. Furthermore, we show that joint concept disambiguating and clustering as well as joint context selection and disambiguation leads to significant improvements ceteris paribus

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

Enhancing knowledge acquisition systems with user generated and crowdsourced resources

This thesis is on leveraging knowledge acquisition systems with collaborative data and crowdsourcing work from internet. We propose two strategies and apply them for building effective entity linking and question answering (QA) systems. The first strategy is on integrating an information extraction system with online collaborative knowledge bases, such as Wikipedia and Freebase. We construct a Cross-Lingual Entity Linking (CLEL) system to connect Chinese entities, such as people and locations, with corresponding English pages in Wikipedia. The main focus is to break the language barrier between Chinese entities and the English KB, and to resolve the synonymy and polysemy of Chinese entities. To address those problems, we create a cross-lingual taxonomy and a Chinese knowledge base (KB). We investigate two methods of connecting the query representation with the KB representation. Based on our CLEL system participating in TAC KBP 2011 evaluation, we finally propose a simple and effective generative model, which achieved much better performance. The second strategy is on creating annotation for QA systems with the help of crowd- sourcing. Crowdsourcing is to distribute a task via internet and recruit a lot of people to complete it simultaneously. Various annotated data are required to train the data-driven statistical machine learning algorithms for underlying components in our QA system. This thesis demonstrates how to convert the annotation task into crowdsourcing micro-tasks, investigate different statistical methods for enhancing the quality of crowdsourced anno- tation, and finally use enhanced annotation to train learning to rank models for passage ranking algorithms for QA.Gegenstand dieser Arbeit ist das Nutzbarmachen sowohl von Systemen zur Wissener- fassung als auch von kollaborativ erstellten Daten und Arbeit aus dem Internet. Es werden zwei Strategien vorgeschlagen, welche für die Erstellung effektiver Entity Linking (Disambiguierung von Entitätennamen) und Frage-Antwort Systeme eingesetzt werden. Die erste Strategie ist, ein Informationsextraktions-System mit kollaborativ erstellten Online- Datenbanken zu integrieren. Wir entwickeln ein Cross-Linguales Entity Linking-System (CLEL), um chinesische Entitäten, wie etwa Personen und Orte, mit den entsprechenden Wikipediaseiten zu verknüpfen. Das Hauptaugenmerk ist es, die Sprachbarriere zwischen chinesischen Entitäten und englischer Datenbank zu durchbrechen, und Synonymie und Polysemie der chinesis- chen Entitäten aufzulösen. Um diese Probleme anzugehen, erstellen wir eine cross linguale Taxonomie und eine chinesische Datenbank. Wir untersuchen zwei Methoden, die Repräsentation der Anfrage und die Repräsentation der Datenbank zu verbinden. Schließlich stellen wir ein einfaches und effektives generatives Modell vor, das auf unserem System für die Teilnahme an der TAC KBP 2011 Evaluation basiert und eine erheblich bessere Performanz erreichte. Die zweite Strategie ist, Annotationen für Frage-Antwort-Systeme mit Hilfe von "Crowd- sourcing" zu erstellen. "Crowdsourcing" bedeutet, eine Aufgabe via Internet an eine große Menge an angeworbene Menschen zu verteilen, die diese simultan erledigen. Verschiedene annotierte Daten sind notwendig, um die datengetriebenen statistischen Lernalgorithmen zu trainieren, die unserem Frage-Antwort System zugrunde liegen. Wir zeigen, wie die Annotationsaufgabe in Mikro-Aufgaben für das Crowdsourcing umgewan- delt werden kann, wir untersuchen verschiedene statistische Methoden, um die Qualität der Annotation aus dem Crowdsourcing zu erweitern, und schließlich nutzen wir die erwei- erte Annotation, um Modelle zum Lernen von Ranglisten von Textabschnitten zu trainieren

Mixed-Language Arabic- English Information Retrieval

Includes abstract.Includes bibliographical references.This thesis attempts to address the problem of mixed querying in CLIR. It proposes mixed-language (language-aware) approaches in which mixed queries are used to retrieve most relevant documents, regardless of their languages. To achieve this goal, however, it is essential firstly to suppress the impact of most problems that are caused by the mixed-language feature in both queries and documents and which result in biasing the final ranked list. Therefore, a cross-lingual re-weighting model was developed. In this cross-lingual model, term frequency, document frequency and document length components in mixed queries are estimated and adjusted, regardless of languages, while at the same time the model considers the unique mixed-language features in queries and documents, such as co-occurring terms in two different languages. Furthermore, in mixed queries, non-technical terms (mostly those in non-English language) would likely overweight and skew the impact of those technical terms (mostly those in English) due to high document frequencies (and thus low weights) of the latter terms in their corresponding collection (mostly the English collection). Such phenomenon is caused by the dominance of the English language in scientific domains. Accordingly, this thesis also proposes reasonable re-weighted Inverse Document Frequency (IDF) so as to moderate the effect of overweighted terms in mixed queries

Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion

The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13636-015-0063-8Spoken term detection (STD) aims at retrieving data from a speech repository given a textual representation of the search term. Nowadays, it is receiving much interest due to the large volume of multimedia information. STD differs from automatic speech recognition (ASR) in that ASR is interested in all the terms/words that appear in the speech data, whereas STD focuses on a selected list of search terms that must be detected within the speech data. This paper presents the systems submitted to the STD ALBAYZIN 2014 evaluation, held as a part of the ALBAYZIN 2014 evaluation campaign within the context of the IberSPEECH 2014 conference. This is the first STD evaluation that deals with Spanish language. The evaluation consists of retrieving the speech files that contain the search terms, indicating their start and end times within the appropriate speech file, along with a score value that reflects the confidence given to the detection of the search term. The evaluation is conducted on a Spanish spontaneous speech database, which comprises a set of talks from workshops and amounts to about 7 h of speech. We present the database, the evaluation metrics, the systems submitted to the evaluation, the results, and a detailed discussion. Four different research groups took part in the evaluation. Evaluation results show reasonable performance for moderate out-of-vocabulary term rate. This paper compares the systems submitted to the evaluation and makes a deep analysis based on some search term properties (term length, in-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and in-language/foreign terms).This work has been partly supported by project CMC-V2 (TEC2012-37585-C02-01) from the Spanish Ministry of Economy and Competitiveness. This research was also funded by the European Regional Development Fund, the Galician Regional Government (GRC2014/024, “Consolidation of Research Units: AtlantTIC Project” CN2012/160)

Getting More out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics.

This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three examples of GATE-based systems operating in the life sciences and in medicine. First, in genome-wide association studies which have contributed to discovery of a head and neck cancer mutation association. Second, medical records analysis which has significantly increased the statistical power of treatment/ outcome models in the UK’s largest psychiatric patient cohort. Third, richer constructs in drug-related searching. We also explore the ways in which the GATE family supports the various stages of the lifecycle present in our examples. We conclude that the deployment of text mining for document abstraction or rich search and navigation is best thought of as a process, and that with the right computational tools and data collection strategies this process can be made defined and repeatable. The GATE research programme is now 20 years old and has grown from its roots as a specialist development tool for text processing to become a rather comprehensive ecosystem, bringing together software developers, language engineers and research staff from diverse fields. GATE now has a strong claim to cover a uniquely wide range of the lifecycle of text analysis systems. It forms a focal point for the integration and reuse of advances that have been made by many people (the majority outside of the authors’ own group) who work in text processing for biomedicine and other areas. GATE is available online ,1. under GNU open source licences and runs on all major operating systems. Support is available from an active user and developer community and also on a commercial basis

An Integrated Framework for Patent Analysis and Mining

Patent documents are important intellectual resources of protecting interests of individuals, organizations and companies. These patent documents have great research values, beneficial to the industry, business, law, and policy-making communities. Patent mining aims at assisting patent analysts in investigating, processing, and analyzing patent documents, which has attracted increasing interest in academia and industry. However, despite recent advances in patent mining, several critical issues in current patent mining systems have not been well explored in previous studies. These issues include: 1) the query retrieval problem that assists patent analysts finding all relevant patent documents for a given patent application; 2) the patent documents comparative summarization problem that facilitates patent analysts in quickly reviewing any given patent documents pairs; and 3) the key patent documents discovery problem that helps patent analysts to quickly grasp the linkage between different technologies in order to better understand the technical trend from a collection of patent documents. This dissertation follows the stream of research that covers the aforementioned issues of existing patent analysis and mining systems. In this work, we delve into three interleaved aspects of patent mining techniques, including (1) PatSearch, a framework of automatically generating the search query from a given patent application and retrieving relevant patents to user; (2) PatCom, a framework for investigating the relationship in terms of commonality and difference between patent documents pairs, and (3) PatDom, a framework for integrating multiple types of patent information to identify important patents from a large volume of patent documents. In summary, the increasing amount and textual complexity of patent repository lead to a series of challenges that are not well addressed in the current generation systems. My work proposed reasonable solutions to these challenges and provided insights on how to address these challenges using a simple yet effective integrated patent mining framework