Results 51 to 60 of about 19,032 (297)
Benchmark for Evaluation of Danish Clinical Word Embeddings
Northern European Journal of Language Technology, 2023 In natural language processing, benchmarks are used to track progress and identify useful models. Currently, no benchmark for Danish clinical word embeddings exists.
Martin Sundahl Laursen +4 more
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Morphological Skip-Gram: Replacing FastText characters n-gram with morphological knowledge
Inteligencia Artificial, 2021 Natural language processing systems have attracted much interest of the industry. This branch of study is composed of some applications such as machine translation, sentiment analysis, named entity recognition, question and answer, and others.
Thiago Dias Bispo +3 more
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Integrating and evaluating neural word embeddings in information retrieval [PDF]
, 2015 Recent advances in neural language models have contributed new methods for learning distributed vector representations of words (also called word embeddings). Two such methods are the continuous bag-of-words model and the skipgram model.
Bevan Koopman +7 more
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Analysis of Italian Word Embeddings [PDF]
, 2017 In this work we analyze the performances of two of the most used word embeddings algorithms, skip-gram and continuous bag of words on Italian language. These algorithms have many hyper-parameter that have to be carefully tuned in order to obtain accurate word representation in vectorial space. We provide an extensive analysis and an evaluation, showing
Tripodi, Rocco, Pira, Stefano Li
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Word embeddings reveal how fundamental sentiments structure natural language
, 2023 Central to affect control theory are culturally shared meanings of concepts. That these sentiments overlap among members of a culture presumably reflects their roots in the language use that members observe. Yet the degree to which the affective meaning
Jeremy Freese, Austin van Loon
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When Word Embeddings Become Endangered [PDF]
, 2021 Big languages such as English and Finnish have many natural language processing (NLP) resources and models, but this is not the case for low-resourced and endangered languages as such resources are so scarce despite the great advantages they would ...
Alnajjar, Khalid, Khalid Alnajjar
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A Gloss Composition and Context Clustering Based Distributed Word Sense Representation Model
Entropy, 2015 In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word ...
Tao Chen +3 more
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Efficient estimation of Hindi WSD with distributed word representation in vector space
Journal of King Saud University: Computer and Information Sciences, 2022 Word Sense Disambiguation (WSD) is significant for improving the accuracy of the interpretation of a Natural language text. Various supervised learning-based models and knowledge-based models have been developed in the literature for WSD of the language ...
Archana Kumari, D.K. Lobiyal
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When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting [PDF]
Journal of Universal Computer Science, 2022 In 2018, Mikolov et al. introduced the positional language model, which has characteristics of attention-based neural machine translation models and which achieved state-of-the-art performance on the intrinsic word analogy task.
Vít Novotný +4 more
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Word Embeddings in Sentiment Analysis [PDF]
, 2018 In the late years sentiment analysis and its applications have reached growing popularity. Concerning this field of research, in the very late years machine learning and word representation learning derived from distributional semantics field (i.e. word embeddings) have proven to be very successful in performing sentiment analysis tasks.
Petrolito R, Dell'Orletta F
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