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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 Hits 1 – 13 of 13 1 Distributional Semantic Models for English verbs and nouns ... Perek, Florent. - : Open Science Framework, 2021 BASE Show details 2 The Role of negative information when learning dense word vectors ; O papel da informação negativa na aprendizagem de vetores palavra densos Salle, Alexandre Tadeu. - 2021 BASE Show details 3 Word Representations Concentrate and This is Good News! Couillet, Romain; Cinar, Yagmur Gizem; Gaussier, Éric... In: CoNLL 2020 - 24th Conference on Computational Natural Language Learning ; https://hal.univ-grenoble-alpes.fr/hal-03356609 ; CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Association for Computational Linguistics (ACL), Nov 2020, Online, France. pp.325-334, ⟨10.18653/v1/2020.conll-1.25⟩ (2020) BASE Show details 4 Can word vectors help corpus linguists? ; Les vecteurs lexicaux peuvent-ils venir en aide aux linguistes de corpus ? Desagulier, Guillaume In: ISSN: 0039-3274 ; Studia Neophilologica ; https://halshs.archives-ouvertes.fr/halshs-01657591 ; Studia Neophilologica, Taylor & Francis (Routledge): SSH Titles, 2019, ⟨10.1080/00393274.2019.1616220⟩ (2019) BASE Show details 5 Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili Casper S. Shikali; Zhou Sijie; Liu Qihe... In: Applied Sciences ; Volume 9 ; Issue 18 (2019) BASE Show details 6 RedMed: Extending drug lexicons for social media applications ... Lavertu, Adam; Altman, Russ. - : Zenodo, 2019 BASE Show details 7 RedMed: Extending drug lexicons for social media applications ... Lavertu, Adam; Altman, Russ. - : Zenodo, 2019 BASE Show details 8 Sparse distributed representations as word embeddings for language understanding Silva, André de Vasconcelos Santos. - 2018 BASE Show details 9 Using EEG to decode semantics during an artificial language learning task Foster, Chris. - 2018 BASE Show details 10 Construction grammars: the empirical challenge ; Les grammaires de constructions à l'épreuve de l'empirie Desagulier, Guillaume. - : HAL CCSD, 2016 In: https://halshs.archives-ouvertes.fr/tel-01657598 ; Linguistique. Université Paris Diderot (Paris 7), 2016 (2016) BASE Show details 11 A Statistical Approach to Retrieving Historical Manuscript Images without Recognition Rath, Toni M.; Lavrenko, Victor; Manmatha, R. In: DTIC (2003) BASE Show details 12 disambiguation Alfredo Maldonado-guerra; Martin Emms In: http://lexicometrica.univ-paris3.fr/jadt/jadt2012/Communications/Maldonado-Guerra+et+al.+-+First-order+and+second-order+context+representations.pdf BASE Show details 13 Use of Semantic Relation Between Words in Text Clustering Bhoopesh Choudhary; Pushpak Bhattacharyya In: http://www.cse.iitb.ac.in/~pb/papers/cluster_unl.pdf Abstract: In traditional document clustering methods, a document is considered a bag of words. The fact that the words may be semantically related- a crucial information for clustering- is not taken into account. The feature vector representing the document is constructed from the frequency count of document terms. To improve results, weights calculated from techniques like Inverse Document Frequency (IDF) and Information Gain (IG) are applied to the frequency count. These weights also are essentially statistical parameters and do not make use of any semantic information. In this paper we describe a new method for generating feature vectors, using the semantic relations between the words in a sentence. The semantic relations are captured by the Universal Networking Language (UNL) which is a recently proposed semantic representation for sentences. UNL expresses a document in the form of a semantic graph, with nodes as disambiguated words and semantic relations between them as arcs. The method described in this paper takes the UNL graph and generates there from feature vectors representing the document. The clustering method applied to the feature vectors is the Kohonen Self Organizing Maps (SOM). This is a neural network based technique which takes the vectors as inputs and forms a document map in which similar documents are mapped to the same or a nearby neurons. Experiments show that if we use the UNL method for feature vector generation, clustering tends to perform better than when the term frequency based method is used. Keyword: 6; 800; Document vectors; Self Organization Maps. Approximate word count; Semantic net/graph; Text clustering; Universal Networking language URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.409.2575 http://www.cse.iitb.ac.in/~pb/papers/cluster_unl.pdf BASE Hide details

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