8,792 research outputs found

    Enriching Knowledge Bases with Counting Quantifiers

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    Information extraction traditionally focuses on extracting relations between identifiable entities, such as . Yet, texts often also contain Counting information, stating that a subject is in a specific relation with a number of objects, without mentioning the objects themselves, for example, "California is divided into 58 counties". Such counting quantifiers can help in a variety of tasks such as query answering or knowledge base curation, but are neglected by prior work. This paper develops the first full-fledged system for extracting counting information from text, called CINEX. We employ distant supervision using fact counts from a knowledge base as training seeds, and develop novel techniques for dealing with several challenges: (i) non-maximal training seeds due to the incompleteness of knowledge bases, (ii) sparse and skewed observations in text sources, and (iii) high diversity of linguistic patterns. Experiments with five human-evaluated relations show that CINEX can achieve 60% average precision for extracting counting information. In a large-scale experiment, we demonstrate the potential for knowledge base enrichment by applying CINEX to 2,474 frequent relations in Wikidata. CINEX can assert the existence of 2.5M facts for 110 distinct relations, which is 28% more than the existing Wikidata facts for these relations.Comment: 16 pages, The 17th International Semantic Web Conference (ISWC 2018

    Boosting terminology extraction through crosslingual resources

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    Terminology Extraction is an important Natural Language Processing task with multiple applications in many areas. The task has been approached from different points of view using different techniques. Language and domain independent systems have been proposed as well. Our contribution in this paper focuses on the improvements on Terminology Extraction using crosslingual resources and specifically the Wikipedia and on the use of a variant of PageRank for scoring the candidate terms. // La extracción de terminología es una tarea de procesamiento de la lengua sumamente importante y aplicable en numerosas áreas. La tarea se ha abordado desde múltiples perspectivas y utilizando técnicas diversas. También se han propuesto sistemas independientes de la lengua y del dominio. La contribución de este artículo se centra en las mejoras que los sistemas de extracción de terminología pueden lograr utilizando recursos translingües, y concretamente la Wikipedia y en el uso de una variante de PageRank para valorar los candidatos a términoPeer ReviewedPostprint (published version

    Cardinal Virtues: Extracting Relation Cardinalities from Text

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    Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award. However, some facts are never fully mentioned, and no IE method has perfect recall. Thus, it is beneficial to also tap contents about the cardinalities of these relations, for example, how many awards someone has won. We introduce this novel problem of extracting cardinalities and discusses the specific challenges that set it apart from standard IE. We present a distant supervision method using conditional random fields. A preliminary evaluation results in precision between 3% and 55%, depending on the difficulty of relations.Comment: 5 pages, ACL 2017 (short paper

    Extracting semantic entities and events from sports tweets

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    Large volumes of user-generated content on practically every major issue and event are being created on the microblogging site Twitter. This content can be combined and processed to detect events, entities and popular moods to feed various knowledge-intensive practical applications. On the downside, these content items are very noisy and highly informal, making it difficult to extract sense out of the stream. In this paper, we exploit various approaches to detect the named entities and significant micro-events from users’ tweets during a live sports event. Here we describe how combining linguistic features with background knowledge and the use of Twitter-specific features can achieve high, precise detection results (f-measure = 87%) in different datasets. A study was conducted on tweets from cricket matches in the ICC World Cup in order to augment the event-related non-textual media with collective intelligence

    Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon

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    This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for different languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which affects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The different steps of the procedure (mapping, disambiguation, extraction, NE identification and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the system’s accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented
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