5 research outputs found

    Indirectly Named Entity Recognition

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    [EN] We define here indirectly named entities, as a term to denote multiword expressions referring to known named entities by means of periphrasis.  While named entity recognition is a classical task in natural language processing, little attention has been paid to indirectly named entities and their treatment. In this paper, we try to address this gap, describing issues related to the detection and understanding of indirectly named entities in texts. We introduce a proof of concept for retrieving both lexicalised and non-lexicalised indirectly named entities in French texts. We also show example cases where this proof of concept is applied, and discuss future perspectives. We have initiated the creation of a first lexicon of 712 indirectly named entity entries that is available for future research.This research has been funded by the FEDER (Fonds europĂ©en de dĂ©veloppement rĂ©gional) and selected by the French-Swiss programme Interreg V. We would like to thank Claire Wuillemin for her preliminary work in the DecRIPT project about the State-of-the-Art in NER and SER in 2020. We would also like to thank for their advice Gilles Falquet, Luka Nerima, Eric Wehrli and Jean-Philippe Goldman at the University of Geneva.Kauffmann, A.; Rey, F.; Atanassova, I.; Gaudinat, A.; Greenfield, P.; Madinier, H.; Cardey, S. (2021). Indirectly Named Entity Recognition. Journal of Computer-Assisted Linguistic Research. 5(1):27-46. https://doi.org/10.4995/jclr.2021.15922OJS274651Abney, Steven. 1987. "The English Noun Phrase in its Sentential Aspect." PhD diss., Massachusetts Institute of Technology.Alsharaf, H., S. Cardey, P. Greenfield, D. Limame, and I. Skouratov. 2003. "Fixedness, the complexity and fragility of the phenomenon: some solutions for natural language processing." In Proceedings of ICL17. Prague, Czech Republic: Matfyzpress.Ananthanarayanan, Rema, Vijil Chenthamarakshan, Prasad M Deshpande, and Raghuram Krishnapuram. 2008. 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    Unsupervised identification of synonymous query intent templates for attribute intents

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    ABSTRACT Among all web search queries there is an important subset of queries containing entity mentions. In these queries, it is observed that users are most interested in requesting some attribute of an entity, such as "Obama age" for the intent of age, which we refer to as the attribute intent. In this work we address the problem of identifying synonymous query intent templates for the attribute intent. For example, "how old is [Person]" and "[Person]'s age" are both synonymous templates for the age intent. Successful identification of the synonymous query intent templates not only can improve the performance of all existing query annotation approaches, but also could benefit applications such as instant answers and intent-based query suggestion. In this work we propose a clustering framework with multiple kernel functions to identify synonymous query intent templates for a set of canonical templates jointly. Furthermore, signals from multiple sources of information are integrated into a kernel function between templates, where the weights of these signals are tuned in an unsupervised manner. We have conducted extensive experiments across multiple domains in FreeBase, and results demonstrate the effectiveness of our clustering framework for finding synonymous query intent templates for attribute intents

    Entity-Oriented Search

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    This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms

    拉脱维亚汉语教学探索 : EXPLORATION OF CHINESE TEACHING IN LATVIA

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    The 10th Anniversary of Confucius Institute at University of Latvia. Exploration of Chinese Teaching in Latvia
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