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Distributional Semantic Models for English verbs and nouns ...
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The Role of negative information when learning dense word vectors ; O papel da informação negativa na aprendizagem de vetores palavra densos
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Word Representations Concentrate and This is Good News!
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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)
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Can word vectors help corpus linguists? ; Les vecteurs lexicaux peuvent-ils venir en aide aux linguistes de corpus ?
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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)
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Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
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In: Applied Sciences ; Volume 9 ; Issue 18 (2019)
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RedMed: Extending drug lexicons for social media applications ...
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RedMed: Extending drug lexicons for social media applications ...
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Sparse distributed representations as word embeddings for language understanding
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Using EEG to decode semantics during an artificial language learning task
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Construction grammars: the empirical challenge ; Les grammaires de constructions à l'épreuve de l'empirie
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In: https://halshs.archives-ouvertes.fr/tel-01657598 ; Linguistique. Université Paris Diderot (Paris 7), 2016 (2016)
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A Statistical Approach to Retrieving Historical Manuscript Images without Recognition
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In: DTIC (2003)
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disambiguation
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In: http://lexicometrica.univ-paris3.fr/jadt/jadt2012/Communications/Maldonado-Guerra+et+al.+-+First-order+and+second-order+context+representations.pdf
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Use of Semantic Relation Between Words in Text Clustering
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In: http://www.cse.iitb.ac.in/~pb/papers/cluster_unl.pdf
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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.
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Keyword:
6; 800; Document vectors; Self Organization Maps. Approximate word count; Semantic net/graph; Text clustering; Universal Networking language
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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
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