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Knowledge graph and corpus driven segmentation and answer inference for telegraphic entity-seeking queries

Knowledge graph and corpus driven segmentation and answer in...

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2014 Conference on Empirical methods in natural language processing, EMNLP 2014

作者： Joshi, Mandar Sawant, Uma Chakrabarti, Soumen IBM Research United States Yahoo Labs IIT Bombay India IIT Bombay India

ISBN: (纸本)9781937284961

Much recent work focuses on formal interpretation of natural question utterances, with the goal of executing the resulting structured queries on knowledge graphs (KGs) such as Freebase. Here we address two limitations of this approach when applied to open-domain, entity-orientedWeb queries. first,Web queries are rarely wellformed questions. They are "telegraphic", with missing verbs, prepositions, clauses, case and phrase clues. Second, the KG is always incomplete, unable to directly answer many queries. We propose a novel technique to segment a telegraphic query and assign a coarse-grained purpose to each segment: A base entity e1, a relation type r, a target entity type t2, and contextual words s. The query seeks entity e2 Ε t2 where r(e1, e2) holds, further evidenced by schema-agnostic words s. Query segmentation is integrated with the KG and an unstructured corpus where mentions of entities have been linked to the KG. We do not trust the best or any specific query segmentation. Instead, evidence in favor of candidate e2s are aggregated across several segmentations. Extensive experiments on the ClueWeb corpus and parts of Freebase as our KG, using over a thousand telegraphic queries adapted from TREC, INEX, and Web- Questions, show the efficacy of our approach. For one benchmark, MAP improves from 0.2-0.29 (competitive baselines) to 0.42 (our system). NDCG@10 improves from 0.29-0.36 to 0.54. © 2014 Association for Computational Linguistics.

关键词： Knowledge graph

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Yet Another Ranking Function for Automatic Multiword Term Extraction

Yet Another Ranking Function for Automatic Multiword Term Ex...

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9th International Conference on natural language processing (NLP)

作者： Lossio-Ventura, Juan Antonio Jonquet, Clement Roche, Mathieu Teisseire, Maguelonne Univ Montpellier 2 LIRMM CNRS Montpellier France

ISBN: (纸本)9783319108889;9783319108872

Term extraction is an essential task in domain knowledge acquisition. We propose two new measures to extract multiword terms from a domain-specific text. The first measure is both linguistic and statistical based. The second measure is graph-based, allowing assessment of the importance of a multiword term of a domain. Existing measures often solve some problems related (but not completely) to term extraction, e.g., noise, silence, low frequency, large-corpora, complexity of the multiword term extraction process. Instead, we focus on managing the entire set of problems, e.g., detecting rare terms and overcoming the low frequency issue. We show that the two proposed measures outperform precision results previously reported for automatic multiword extraction by comparing them with the state-of-the-art reference measures.

关键词： graphic methods

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NTUNLP approaches to recognizing and disambiguating entities in long and short text at the ERD Challenge 2014

NTUNLP approaches to recognizing and disambiguating entities...

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1st ACM International workshop on Entity Recognition and Disambiguation, ERD 2014 - Co-located with SIGIR 2014

作者： Chiu, Yen-Pin Shih, Yong-Siang Lee, Yang-Yin Shao, Chih-Chieh Cai, Ming-Lun Wei, Sheng-Lun Chen, Hsin-Hsi Natural Language Processing Laboratory Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9781450330237

This paper presents the NTUNLP systems in the long track and the short track of the Entity Recognition and Disambiguation Challenge 2014. We first create a dictionary that contains the possible surface forms of Freebase Ids, then scan the given text from left to right with the longest match strategy to detect the mentions, and eliminate the unwanted surface forms based on a stop word list. methods to link to the most relevant entities and select the best candidate are proposed for these two tracks, respectively. The outside resources such as DBpedia Spotlight and TAGME are integrated to our basic NTUNLP systems. Various experimental setups are presented and discussed with the development set. In the formal run, one NTUNLP system wins the first prize in the short track and another NTUNLP system gets the fourth place in the long track. Copyright 2014 ACM.

关键词： Character recognition

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Named entity recognition from tweets 16

Named entity recognition from tweets

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16th workshops on Learning, Knowledge, Adaptation, LWA 2014: Knowledge Discovery, Data Mining and Machine Learning, KDML 2014, Information Retrieval, IR 2014 and Knowledge Management, FGWM 2014

作者： Bandyopadhyay, Ayan Roy, Dwaipayan Mitra, Mandar Saha, Sanjoy Kumar Indian Statistical Institute India Jadavpur University India

Entries in microblogging sites are very short. For example, a 'tweet' (a post or status update on the popular microblogging site Twitter) can contain at most 140 characters. To comply with this restriction, users frequently use abbreviations to express their thoughts, thus producing sentences that are often poorly structured or ungrammatical. As a result, it becomes a challenge to come up with methods for automatically identifying named entities (names of persons, organizations, locations etc.). In this study, we use a four-step approach to automatic named entity recognition from microposts. first, we do some preprocessing of the micropost (e.g. replace abbreviations with actual words). Then we use an off-the-shelf part-of-speech tagger to tag the nouns. Next, we use the Google Search API to retrieve sentences containing the tagged nouns. Finally, we run a standard Named Entity Recognizer (NER) on the retrieved sentences. The tagged nouns are returned along with the tags assigned by the NER. This simple approach, using readily available components, yields promising results on standard benchmark data. Copyright © 2014 by the paper's authors.

关键词： natural language processing systems

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JobimText visualizer: A graph-based Approach to Contextualizing Distributional Similarity 8

JobimText visualizer: A Graph-based Approach to Contextualiz...

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8th workshop on graph-based methods for natural language processing, Textgraphs 2013, at the Conference on Empirical methods in natural language processing, EMNLP 2013

作者： Gliozzo, Alfio Biemann, Chris Riedl, Martin Coppola, Bonaventura Glass, Michael R. Hatem, Matthew IBM T.J. Watson Research Yorktown HeightsNY10598 United States FG Language Technology CS Dept. TU Darmstadt Darmstadt64289 Germany

ISBN: (纸本)9781937284978

We introduce an interactive visualization component for the JoBimText project. JoBimText is an open source platform for large-scale distributional semantics based on graph representations. first we describe the underlying technology for computing a distributional thesaurus on words using bipartite graphs of words and context features, and contextualizing the list of semantically similar words towards a given sentential context using graph-based ranking. Then we demonstrate the capabilities of this contextualized text expansion technology in an interactive visualization. The visualization can be used as a semantic parser providing contextualized expansions of words in text as well as disambiguation to word senses induced by graph clustering, and is provided as an open source tool. © 2013 Association for Computational Linguistics

关键词： Visualization

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graph-based unsupervised learning of word similarities using heterogeneous feature types 8

Graph-based unsupervised learning of word similarities using...

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8th workshop on graph-based methods for natural language processing, Textgraphs 2013, at the Conference on Empirical methods in natural language processing, EMNLP 2013

作者： Saluja, Avneesh Navrátil, Jiří Carnegie Mellon University United States IBM Research United States

ISBN: (纸本)9781937284978

In this work, we propose a graph-based approach to computing similarities between words in an unsupervised manner, and take advantage of heterogeneous feature types in the process. The approach is based on the creation of two separate graphs, one for words and one for features of different types (alignment-based, orthographic, etc.). The graphs are connected through edges that link nodes in the feature graph to nodes in the word graph, the edge weights representing the importance of a particular feature for a particular word. High quality graphs are learned during training, and the proposed method outperforms experimental baselines. © 2013 Association for Computational Linguistics

关键词： graphic methods

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graph-structures matching for review relevance identification 8

Graph-structures matching for review relevance identificatio...

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8th workshop on graph-based methods for natural language processing, Textgraphs 2013, at the Conference on Empirical methods in natural language processing, EMNLP 2013

作者： Ramachandran, Lakshmi Gehringer, Edward F. North Carolina State University United States

ISBN: (纸本)9781937284978

Review quality is determined by identifying the relevance of a review to a submission (the article or paper the review was written for). We identify relevance in terms of the semantic and syntactic similarities between two texts. We use a word order graph, whose vertices, edges and double edges help determine structure-based match across texts. We use WordNet to determine semantic relatedness. Ours is a lexico-semantic approach, which predicts relevance with an accuracy of 66% and f-measure of 0.67. © 2013 Association for Computational Linguistics

关键词： Semantics

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A graph-based approach to skill extraction from text 8

A graph-based approach to skill extraction from text

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8th workshop on graph-based methods for natural language processing, Textgraphs 2013, at the Conference on Empirical methods in natural language processing, EMNLP 2013

作者： Kivimäki, Ilkka Panchenko, Alexander Dessy, Adrien Verdegem, Dries Francq, Pascal Fairon, Cédrick Bersini, Hugues Saerens, Marco ICTEAM Université Catholique de Louvain Belgium CENTAL Université Catholique de Louvain Belgium IRIDIA Université Libre de Bruxelles Belgium Digital Society Laboratory LLC Russia

ISBN: (纸本)9781937284978

This paper presents a system that performs skill extraction from text documents. It outputs a list of professional skills that are relevant to a given input text. We argue that the system can be practical for hiring and management of personnel in an organization. We make use of the texts and the hyperlink graph of Wikipedia, as well as a list of professional skills obtained from the LinkedIn social network. The system is based on first computing similarities between an input document and the texts of Wikipedia pages and then using a biased, hub-avoiding version of the Spreading Activation algorithm on the Wikipedia graph in order to associate the input document with skills. © 2013 Association for Computational Linguistics

关键词： Extraction

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Merging word senses 8

Merging word senses

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8th workshop on graph-based methods for natural language processing, Textgraphs 2013, at the Conference on Empirical methods in natural language processing, EMNLP 2013

作者： Bhagwani, Sumit Satapathy, Shrutiranjan Karnick, Harish Computer Science and Engineering IIT Kanpur Kanpur208016 India

ISBN: (纸本)9781937284978

WordNet, a widely used sense inventory for Word Sense Disambiguation(WSD), is often too fine-grained for many natural language applications because of its narrow sense distinctions. We present a semi-supervised approach to learn similarity between WordNet synsets using a graph based recursive similarity definition. We seed our framework with sense similarities of all the word-sense pairs, learnt using supervision on human-labelled sense clusterings. Finally we discuss our method to derive coarse sense inventories at arbitrary granularities and show that the coarse-grained sense inventory obtained significantly boosts the disambiguation of nouns on standard test sets. © 2013 Association for Computational Linguistics

关键词： graphic methods

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From global to local similarities: A graph-based Contextualization Method using Distributional Thesauri 8

From global to local similarities: A Graph-Based Contextuali...

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8th workshop on graph-based methods for natural language processing, Textgraphs 2013, at the Conference on Empirical methods in natural language processing, EMNLP 2013

作者： Biemann, Chris Riedl, Martin Computer Science Department Technische Universität Darmstadt Hochschulstrasse 10 DarmstadtD-64289 Germany

ISBN: (纸本)9781937284978

After recasting the computation of a distributional thesaurus in a graph-based framework for term similarity, we introduce a new contextualization method that generates, for each term occurrence in a text, a ranked list of terms that are semantically similar and compatible with the given context. The framework is instantiated by the definition of term and context, which we derive from dependency parses in this work. Evaluating our approach on a standard data set for lexical substitution, we show substantial improvements over a strong non-contextualized baseline across all parts of speech. In contrast to comparable approaches, our framework defines an unsupervised generative method for similarity in context and does not rely on the existence of lexical resources as a source for candidate expansions. © 2013 Association for Computational Linguistics

关键词： Thesauri

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