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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 24 1 Dynamic Extension of ASR Lexicon Using Wikipedia Data Abdullah, Badr; Illina, Irina; Fohr, Dominique In: IEEE Workshop on Spoken and Language Technology (SLT) ; https://hal.archives-ouvertes.fr/hal-01874495 ; IEEE Workshop on Spoken and Language Technology (SLT), Dec 2018, Athènes, Greece (2018) BASE Show details 2 Categorization of B2B Service Offers: Lessons learnt from the Silex Use case Dhouib, Molka,; Faron Zucker, Catherine; Tettamanzi, Andrea In: 4ème conférence sur les Applications Pratiques de l'Intelligence Artificielle APIA2018 ; https://hal.archives-ouvertes.fr/hal-01830905 ; 4ème conférence sur les Applications Pratiques de l'Intelligence Artificielle APIA2018, Jul 2018, Nancy, France (2018) BASE Show details 3 Facing the facts of fake: a distributional semantics and corpus annotation approach Cappelle, Bert; Denis, Pascal; Keller, Mikaela In: ISSN: 2197-2796 ; Yearbook of the German Cognitive Linguistics Association ; https://hal.archives-ouvertes.fr/hal-01959609 ; Yearbook of the German Cognitive Linguistics Association, De Gruyter, 2018, 6 (9-42) (2018) BASE Show details 4 Building and evaluating resources for sentiment analysis in the Greek language Tsakalidis, Adam; PAPADOPOULOS, Symeon; Voskaki, Rania... In: ISSN: 1574-020X ; EISSN: 1574-0218 ; Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-03382985 ; Language Resources and Evaluation, Springer Verlag, 2018, 52 (4), pp.1021-1044. ⟨10.1007/s10579-018-9420-4⟩ (2018) BASE Show details 5 Exploration par apprentissage de discussions de personnes en détresse psychologique Kessler, Remy; Béchet, Nicolas; Ledegen, Gudrun... In: 29es Journées Francophones d'Ingénierie des Connaissances, IC 2018 ; https://hal.archives-ouvertes.fr/hal-01839561 ; 29es Journées Francophones d'Ingénierie des Connaissances, IC 2018, Jul 2018, Nancy, France. pp.95-102 ; http://pfia2018.loria.fr/ (2018) BASE Show details 6 Unsupervised Creation of Normalisation Dictionaries for Micro-Blogs in Arabic, French and English HTAIT, Amal; Fournier, Sébastien; Bellot, Patrice In: 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2018) ; https://hal.archives-ouvertes.fr/hal-01795348 ; 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2018), Mar 2018, Hanoi, Vietnam (2018) BASE Show details 7 Unsupervised Creation of Normalization Dictionaries for Micro-Blogs in Arabic, French and English HTAIT, Amal; Fournier, Sébastien; Bellot, Patrice In: ISSN: 1405-5546 ; EISSN: 2007-9737 ; Computación y sistemas ; https://hal.archives-ouvertes.fr/hal-01958675 ; Computación y sistemas, Instituto Politécnico Nacional IPN Centro de Investigación en Computación, 2018, 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2018), 22 (3), pp.729-737. ⟨10.13053/CyS-22-3-3034⟩ ; https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/3034/2514 (2018) BASE Show details 8 Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers Cruz, Christophe; Nguyen Van, Cyril; Gautier, Laurent In: ISSN: 2199-188X ; Open Journal of Web Technologies ; https://halshs.archives-ouvertes.fr/halshs-01872273 ; Open Journal of Web Technologies, RonPub, 2018, Special Issue: Proceedings of the International Workshop on Web Data Processing & Reasoning (WDPAR 2018) in conjunction with the 41st German Conference on Artificial Intelligence, 5 (1), pp.23-30 ; https://www.ronpub.com/ojwt/OJWT_2018v5i1n04_Cruz.html (2018) BASE Show details 9 Semantic Analysis using Wikipedia Graph Structure Sajadi, Armin. - 2018 BASE Show details 10 Word Embeddings for Domain Specific Semantic Relatedness Tilbury, Kyle. - 2018 BASE Show details 11 A Framework to Understand Emoji Meaning: Similarity and Sense Disambiguation of Emoji using EmojiNet Wijeratne, Sanjaya In: http://rave.ohiolink.edu/etdc/view?acc_num=wright1547506375922938 (2018) BASE Show details 12 A Semi-supervised Corpus Annotation for Saudi Sentiment Analysis Using Twitter Alqarafi, Abdulrahman; Adeel, Ahsan; Hawalah, Ahmed. - : Springer, 2018 BASE Show details 13 An Empirical Study of Word Embedding Dimensionality Reduction ... Ji, Yichao. - : Zenodo, 2018 BASE Show details 14 An Empirical Study of Word Embedding Dimensionality Reduction ... Ji, Yichao. - : Zenodo, 2018 BASE Show details 15 Sparse distributed representations as word embeddings for language understanding Silva, André de Vasconcelos Santos. - 2018 Abstract: Word embeddings are vector representations of words that capture semantic and syntactic similarities between them. Similar words tend to have closer vector representations in a N dimensional space considering, for instance, Euclidean distance between the points associated with the word vector representations in a continuous vector space. This property, makes word embeddings valuable in several Natural Language Processing tasks, from word analogy and similarity evaluation to the more complex text categorization, summarization or translation tasks. Typically state of the art word embeddings are dense vector representations, with low dimensionality varying from tens to hundreds of floating number dimensions, usually obtained from unsupervised learning on considerable amounts of text data by training and optimizing an objective function of a neural network. This work presents a methodology to derive word embeddings as binary sparse vectors, or word vector representations with high dimensionality, sparse representation and binary features (e.g. composed only by ones and zeros). The proposed methodology tries to overcome some disadvantages associated with state of the art approaches, namely the size of corpus needed for training the model, while presenting comparable evaluations in several Natural Language Processing tasks. Results show that high dimensionality sparse binary vectors representations, obtained from a very limited amount of training data, achieve comparable performances in similarity and categorization intrinsic tasks, whereas in analogy tasks good results are obtained only for nouns categories. Our embeddings outperformed eight state of the art word embeddings in word similarity tasks, and two word embeddings in categorization tasks. ; A designação word embeddings refere-se a representações vetoriais das palavras que capturam as similaridades semânticas e sintáticas entre estas. Palavras similares tendem a ser representadas por vetores próximos num espaço N dimensional considerando, por exemplo, a distância Euclidiana entre os pontos associados a estas representações vetoriais num espaço vetorial contínuo. Esta propriedade, torna as word embeddings importantes em várias tarefas de Processamento Natural da Língua, desde avaliações de analogia e similaridade entre palavras, às mais complexas tarefas de categorização, sumarização e tradução automática de texto. Tipicamente, as word embeddings são constituídas por vetores densos, de dimensionalidade reduzida. São obtidas a partir de aprendizagem não supervisionada, recorrendo a consideráveis quantidades de dados, através da otimização de uma função objetivo de uma rede neuronal. Este trabalho propõe uma metodologia para obter word embeddings constituídas por vetores binários esparsos, ou seja, representações vetoriais das palavras simultaneamente binárias (e.g. compostas apenas por zeros e uns), esparsas e com elevada dimensionalidade. A metodologia proposta tenta superar algumas desvantagens associadas às metodologias do estado da arte, nomeadamente o elevado volume de dados necessário para treinar os modelos, e simultaneamente apresentar resultados comparáveis em várias tarefas de Processamento Natural da Língua. Os resultados deste trabalho mostram que estas representações, obtidas a partir de uma quantidade limitada de dados de treino, obtêm performances consideráveis em tarefas de similaridade e categorização de palavras. Por outro lado, em tarefas de analogia de palavras apenas se obtém resultados consideráveis para a categoria gramatical dos substantivos. As word embeddings obtidas com a metodologia proposta, e comparando com o estado da arte, superaram a performance de oito word embeddings em tarefas de similaridade, e de duas word embeddings em tarefas de categorização de palavras. Keyword: Análise vetorial; Binary sparse vectors; Distributional semantic model; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica; Eletrónica e Informática; Neural networks; Redes neuronais; Text clustering; Word embedding URL: http://hdl.handle.net/10071/18245 BASE Hide details 16 The Effect of Data Quantity on Dialog System Input Classification Models ; Datamängdens effekt på modeller för avsiktsklassificering i chattkonversationer Lipecki, Johan; Lundén, Viggo. - : KTH, Hälsoinformatik och logistik, 2018 BASE Show details 17 Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition Mohammed N. A. Ali; Guanzheng Tan; Aamir Hussain In: Future Internet ; Volume 10 ; Issue 12 (2018) BASE Show details 18 An Integrated Graph Model for Document Summarization Kang Yang; Kamal Al-Sabahi; Yanmin Xiang... In: Information ; Volume 9 ; Issue 9 (2018) BASE Show details 19 Combining Word Embedding and Knowledge-Based Topic Modeling for Entity Summarization Pouriyeh, Seyedamin; Allahyari, Mehdi; Kochut, Krys... In: Computer Science Faculty Publications (2018) BASE Show details 20 Παράσταση γλωσσολογικού συναισθηματικού περιεχομένου με χρήση υπολογιστικής νοημοσύνης ... Μαγιώνας, Οδυσσέας Σ.. - : Aristotle University of Thessaloniki, 2018 BASE Show details Page: 1 2

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