620 research outputs found

    Mining Entity Synonyms with Efficient Neural Set Generation

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    Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.Comment: AAAI 2019 camera-ready versio

    From Terminology Extraction to Terminology Validation: An Approach Adapted to Log Files

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    International audienceLog files generated by computational systems contain relevant and essential information. In some application areas like the design of integrated circuits, log files generated by design tools contain information which can be used in management information systems to evaluate the final products. However, the complexity of such textual data raises some challenges concerning the extraction of information from log files. Log files are usually multi-source, multi-format, and have a heterogeneous and evolving structure. Moreover, they usually do not respect natural language grammar and structures even though they are written in English. Classical methods of information extraction such as terminology extraction methods are particularly irrelevant to this context. In this paper, we introduce our approach Exterlog to extract terminology from log files. We detail how it deals with the specific features of such textual data. The performance is emphasized by favoring the most relevant terms of the domain based on a scoring function which uses a Web and context based measure. The experiments show that Exterlog is a well-adapted approach for terminology extraction from log files

    From Terminology Extraction to Terminology Validation: An Approach Adapted to Log Files

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    Abstract: Log files generated by computational systems contain relevant and essential information. In some application areas like the design of integrated circuits, log files generated by design tools contain information which can be used in management information systems to evaluate the final products. However, the complexity of such textual data raises some challenges concerning the extraction of information from log files. Log files are usually multi-source, multi-format, and have a heterogeneous and evolving structure. Moreover, they usually do not respect natural language grammar and structures even though they are written in English. Classical methods of information extraction such as terminology extraction methods are particularly irrelevant to this context. In this paper, we introduce our approach Exterlog to extract terminology from log files. We detail how it deals with the specific features of such textual data. The performance is emphasized by favoring the most relevant terms of the domain based on a scoring function which uses a Web and context based measure. The experiments show that Exterlog is a well-adapted approach for terminology extraction from log files

    Gathering Information on the Web by Consistent Entity Augmentation

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    Users usually want to gather information about what they are interested in, which could be achieved by entity augmentation using a vast amount of web tables. Existing techniques assume that web tables are entity-attribute binary tables. As for tables having multiple columns to be augmented, they will be split into several entity-attribute binary relations, which would cause semantic fragmentation. Furthermore, the result table consolidated by binary relations will suffer from entity inconsistency and low precision. The objective of our research is to return a consistent result table for entity augmentation when given a set of entities and attribute names. In this paper we propose a web information gathering framework based on consistent entity augmentation. To ensure high consistency and precision of the result table we propose that answer tables for building result table should have consistent matching relationships with each other. Instead of splitting tables into pieces we regard web tables as nodes and consistent matching relationships as edges to make a consistent clique and expand it until its coverage for augmentation query reaches certain threshold gamma. It is proved in this paper that a consistent result table could be built by considering tables in consistent clique to be answer tables. We tested our method on four real-life datasets, compared it with different answer table selection methods and state-of-the-art entity augmentation technique based on table fragmentation as well. The results of a comprehensive set of experiments indicate that our entity augmentation framework is more effective than the existing method in getting consistent entity augmentation results with high accuracy and reliability

    Aggregated search: a new information retrieval paradigm

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    International audienceTraditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, ...) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation and question answering. Then we focus on more recent trends namely cross vertical aggregated search and relational aggregated search which are already present in current Web search

    From Frequency to Meaning: Vector Space Models of Semantics

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    Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field

    Proceedings of the 9th Dutch-Belgian Information Retrieval Workshop

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