1. 1-9 of 9
    1. Information, Vol. 8, Pages 64: Identifying High Quality Document–Summary Pairs through Text Matching

      Information, Vol. 8, Pages 64: Identifying High Quality Document–Summary Pairs through Text Matching

      Information 2017 , 8 (2), 64; doi:10.3390/info8020064 (registering DOI) Identifying High Quality Document–Summary Pairs through Text Matching Intelligence Computing Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China * Author to whom correspondence should be addressed.

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    2. Nutrients, Vol. 9, Pages 457: An Innovative Method for Monitoring Food Quality and the Healthfulness of Consumers’ Grocery Purchases

      Nutrients, Vol. 9, Pages 457: An Innovative Method for Monitoring Food Quality and the Healthfulness of Consumers’ Grocery Purchases

      Nutrients 2017 , 9 (5), 457; doi: 10.3390/nu9050457 An Innovative Method for Monitoring Food Quality and the Healthfulness of Consumers’ Grocery Purchases Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT 84108, USA * Author to whom correspondence should be addressed.

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    3. Algorithms, Vol. 10, Pages 42: RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation

      Algorithms, Vol. 10, Pages 42: RGloVe: An Improved Approach of Global Vectors for Distributional Entity Relation Representation

      No Abstract Most of the previous works on relation extraction between named entities are often limited to extracting the pre-defined types; which are inefficient for massive unlabeled text data. Recently; with the appearance of various distributional word representations; unsupervised methods for many natural language processing (NLP) tasks have been widely researched.

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    4. Algorithms, Vol. 10, Pages 34: A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek

      Algorithms, Vol. 10, Pages 34: A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek

      Artificial Intelligence Laboratory, University of the Aegean, 2 Palama Street, 83200 Samos, Greece * Author to whom correspondence should be addressed. Academic Editors: Katia Lida Kermanidis, Christos Makris, Phivos Mylonas and Spyros Sioutas Received: 11 December 2016 / Accepted: 24 February 2017 / Published: 6 March 2017 (This article belongs to the Special Issue Humanistic Data Processing ) Download PDF [630 KB, uploaded 6 March 2017] No Abstract Sentiment analysis has played a primary role in text classification. It is an undoubted fact that some years ago, textual information was spreading in manageable rates; however, nowadays, such information has overcome even the most ambiguous ...

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    5. Algorithms, Vol. 10, Pages 33: Large Scale Implementations for Twitter Sentiment Classification

      Algorithms, Vol. 10, Pages 33: Large Scale Implementations for Twitter Sentiment Classification

      Algorithms 2017 , 10 (1), 33; doi:10.3390/a10010033 (registering DOI) Large Scale Implementations for Twitter Sentiment Classification Computer Engineering and Informatics Department, University of Patras, Patras 26504, Greece 2 Department of Informatics, Ionian University, Corfu 49100, Greece 3 Department of Cultural Heritage Management and New Technologies, University of Patras, Agrinio 30100, Greece 4 Computer & Informatics Engineering Department, Technological Educational Institute of Western Greece, Patras 26334, Greece * Author to whom correspondence should be addressed. Academic Editor: Bruno Carpentieri Received: 8 December 2016 / Revised: 28 February 2017 / Accepted: 1 March 2017 / Published: 4 March 2017 (This article belongs to the Special ...

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    6. Information, Vol. 8, Pages 13: Dependency Parsing with Transformed Feature

      Information, Vol. 8, Pages 13: Dependency Parsing with Transformed Feature

      Information 2017 , 8 (1), 13; doi:10.3390/info8010013 (registering DOI) Dependency Parsing with Transformed Feature School of Astronautics, Beihang University, Beijing 100191, China Academic Editor: Günter Neumann Received: 2 November 2016 / Revised: 15 December 2016 / Accepted: 16 January 2017 / Published: 21 January 2017 (This article belongs to the Section Artificial Intelligence ) No Abstract Dependency parsing is an important subtask of natural language processing.

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    7. IJGI, Vol. 5, Pages 221: Spatiotemporal Information Extraction from a Historic Expedition Gazetteer

      IJGI, Vol. 5, Pages 221: Spatiotemporal Information Extraction from a Historic Expedition Gazetteer

      ISPRS Int. J. Geo-Inf. 2016 , 5 (12), 221; doi:10.3390/ijgi5120221 (registering DOI) Spatiotemporal Information Extraction from a Historic Expedition Gazetteer Department of Computer Science, University of Cape Town, Cape Town 7700, South Africa 2 Department of Geo-Information Processing (GIP), Faculty of Geo-information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands 3 Regional Integrity Management Systems, ROSEN Europe B.V., 7575 EJ Oldenzaal, The Netherlands Editor: Wolfgang Kainz No Abstract Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or ...

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    8. IJMS, Vol. 17, Pages 1313: Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

      IJMS, Vol. 17, Pages 1313: Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

      1 Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain 2 Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña 15006, Spain * Author to whom correspondence should be addressed.

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      Mentions: Spain Big Data
    9. Entropy, Vol. 18, Pages 204: Distant Supervision for Relation Extraction with Ranking-Based Methods

      Entropy, Vol. 18, Pages 204: Distant Supervision for Relation Extraction with Ranking-Based Methods

      and Yang Qin Intelligence Computing Research Center, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China † This paper is an extended version of our paper published in the 22nd International Conference on Neural Information Processing, Istanbul, Turkey, 9–12 November 2015. * Authors to whom correspondence should be addressed.

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      Mentions: China Turkey Istanbul
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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD