Neural Collective Entity Linking

Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of those in the entire text. We further introduce an attention scheme to improve the robustness of NCEL to data noise and train the model on Wikipedia hyperlinks to avoid overfitting and domain bias. In experiments, we evaluate NCEL on five publicly available datasets to verify the linking performance as well as generalization ability. We also conduct an extensive analysis of time complexity, the impact of key modules, and qualitative results, which demonstrate the effectiveness and efficiency of our proposed method.Comment: 12 pages, 3 figures, COLING201

WikiM: Metapaths based Wikification of Scientific Abstracts

In order to disseminate the exponential extent of knowledge being produced in the form of scientific publications, it would be best to design mechanisms that connect it with already existing rich repository of concepts -- the Wikipedia. Not only does it make scientific reading simple and easy (by connecting the involved concepts used in the scientific articles to their Wikipedia explanations) but also improves the overall quality of the article. In this paper, we present a novel metapath based method, WikiM, to efficiently wikify scientific abstracts -- a topic that has been rarely investigated in the literature. One of the prime motivations for this work comes from the observation that, wikified abstracts of scientific documents help a reader to decide better, in comparison to the plain abstracts, whether (s)he would be interested to read the full article. We perform mention extraction mostly through traditional tf-idf measures coupled with a set of smart filters. The entity linking heavily leverages on the rich citation and author publication networks. Our observation is that various metapaths defined over these networks can significantly enhance the overall performance of the system. For mention extraction and entity linking, we outperform most of the competing state-of-the-art techniques by a large margin arriving at precision values of 72.42% and 73.8% respectively over a dataset from the ACL Anthology Network. In order to establish the robustness of our scheme, we wikify three other datasets and get precision values of 63.41%-94.03% and 67.67%-73.29% respectively for the mention extraction and the entity linking phase

Entity linking with distributional semantics

[Abstract] Entity Linking (EL) consists in linking name mentions in a given text with their referring entities in external knowledge bases such as DBpedia/Wikipedia. In this paper, we propose an EL approach whose main contribution is to make use of a knowledge base built by means of distributional similarity. More precisely, Wikipedia is transformed into a manageable database structured with similarity relations between entities. Our EL method is focused on a specific task, namely semantic annotation of documents by extracting those relevant terms that are linked to nodes in DBpedia/Wikipedia. The method is currently working for four languages. The Portuguese and English versions have been evaluated and compared against other EL systems, showing competitive range, close to the best systemsMinisterio de Economía y Competitividad; FFI2014-51978-C2-1-

数学情報アクセスのための数式表現の検索と曖昧性解消

学位の種別: 課程博士審査委員会委員 : （主査）東京大学准教授 渋谷 哲朗, 東京大学教授 萩谷 昌己, 東京大学准教授 蓮尾 一郎, 東京大学准教授 鶴岡 慶雅, 東京工業大学准教授 藤井 敦University of Tokyo(東京大学

Concept and entity grounding using indirect supervision

Extracting and disambiguating entities and concepts is a crucial step toward understanding natural language text. In this thesis, we consider the problem of grounding concepts and entities mentioned in text to one or more knowledge bases (KBs). A well-studied scenario of this problem is the one in which documents are given in English and the goal is to identify concept and entity mentions, and find the corresponding entries the mentions refer to in Wikipedia. We extend this problem in two directions: First, we study identifying and grounding entities written in any language to the English Wikipedia. Second, we investigate using multiple KBs which do not contain rich textual and structural information Wikipedia does. These more involved settings pose a few additional challenges beyond those addressed in the standard English Wikification problem. Key among them is that no supervision is available to facilitate training machine learning models. The first extension, cross-lingual Wikification, introduces problems such as recognizing multilingual named entities mentioned in text, translating non-English names into English, and computing word similarity across languages. Since it is impossible to acquire manually annotated examples for all languages, building models for all languages in Wikipedia requires exploring indirect or incidental supervision signals which already exist in Wikipedia. For the second setting, we need to deal with the fact that most KBs do not contain the rich information Wikipedia has; consequently, the main supervision signal used to train Wikification rankers does not exist anymore. In this thesis, we show that supervision signals can be obtained by carefully examining the redundancy and relations between multiple KBs. By developing algorithms and models which harvest these incidental signals, we can achieve better performance on these tasks

Linking named entities to Wikipedia

Natural language is fraught with problems of ambiguity, including name reference. A name in text can refer to multiple entities just as an entity can be known by different names. This thesis examines how a mention in text can be linked to an external knowledge base (KB), in our case, Wikipedia. The named entity linking (NEL) task requires systems to identify the KB entry, or Wikipedia article, that a mention refers to; or, if the KB does not contain the correct entry, return NIL. Entity linking systems can be complex and we present a framework for analysing their different components, which we use to analyse three seminal systems which are evaluated on a common dataset and we show the importance of precise search for linking. The Text Analysis Conference (TAC) is a major venue for NEL research. We report on our submissions to the entity linking shared task in 2010, 2011 and 2012. The information required to disambiguate entities is often found in the text, close to the mention. We explore apposition, a common way for authors to provide information about entities. We model syntactic and semantic restrictions with a joint model that achieves state-of-the-art apposition extraction performance. We generalise from apposition to examine local descriptions specified close to the mention. We add local description to our state-of-the-art linker by using patterns to extract the descriptions and matching against this restricted context. Not only does this make for a more precise match, we are also able to model failure to match. Local descriptions help disambiguate entities, further improving our state-of-the-art linker. The work in this thesis seeks to link textual entity mentions to knowledge bases. Linking is important for any task where external world knowledge is used and resolving ambiguity is fundamental to advancing research into these problems

Robust Entity Linking in Heterogeneous Domains

Entity Linking is the task of mapping terms in arbitrary documents to entities in a knowledge base by identifying the correct semantic meaning. It is applied in the extraction of structured data in RDF (Resource Description Framework) from textual documents, but equally so in facilitating artificial intelligence applications, such as Semantic Search, Reasoning and Question and Answering. Most existing Entity Linking systems were optimized for specific domains (e.g., general domain, biomedical domain), knowledge base types (e.g., DBpedia, Wikipedia), or document structures (e.g., tables) and types (e.g., news articles, tweets). This led to very specialized systems that lack robustness and are only applicable for very specific tasks. In this regard, this work focuses on the research and development of a robust Entity Linking system in terms of domains, knowledge base types, and document structures and types. To create a robust Entity Linking system, we first analyze the following three crucial components of an Entity Linking algorithm in terms of robustness criteria: (i) the underlying knowledge base, (ii) the entity relatedness measure, and (iii) the textual context matching technique. Based on the analyzed components, our scientific contributions are three-fold. First, we show that a federated approach leveraging knowledge from various knowledge base types can significantly improve robustness in Entity Linking systems. Second, we propose a new state-of-the-art, robust entity relatedness measure for topical coherence computation based on semantic entity embeddings. Third, we present the neural-network-based approach Doc2Vec as a textual context matching technique for robust Entity Linking. Based on our previous findings and outcomes, our main contribution in this work is DoSeR (Disambiguation of Semantic Resources). DoSeR is a robust, knowledge-base-agnostic Entity Linking framework that extracts relevant entity information from multiple knowledge bases in a fully automatic way. The integrated algorithm represents a collective, graph-based approach that utilizes semantic entity and document embeddings for entity relatedness and textual context matching computation. Our evaluation shows, that DoSeR achieves state-of-the-art results over a wide range of different document structures (e.g., tables), document types (e.g., news documents) and domains (e.g., general domain, biomedical domain). In this context, DoSeR outperforms all other (publicly available) Entity Linking algorithms on most data sets

LINKING ENTITIES TO A KNOWLEDGE BASE

Ph.DDOCTOR OF PHILOSOPH