Results 51 to 60 of about 1,159,266 (295)
, 2015 Code for the ACL-2015 paper "Accurate Linear-Time Chinese Word Segmentation via Embedding ...
JM
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, 2015 <p>preliminary release for the code accompanying the paper "Accurate Linear-Time Chinese Word Segmentation via Embedding Matching" (ACL-2015)</p ...
JM
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Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts
Frontiers in Big Data, 2020 Word embedding has benefited a broad spectrum of text analysis tasks by learning distributed word representations to encode word semantics. Word representations are typically learned by modeling local contexts of words, assuming that words sharing ...
Yu Meng +5 more
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Towards Resolving Word Ambiguity with Word Embeddings
CoRR, 2023 Ambiguity is ubiquitous in natural language. Resolving ambiguous meanings is especially important in information retrieval tasks. While word embeddings carry semantic information, they fail to handle ambiguity well. Transformer models have been shown to handle word ambiguity for complex queries, but they cannot be used to identify ambiguous words, e.g.
Matthias Thurnbauer +3 more
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, 2021 These are several Arabic Word Embedding Models for NLP tasks and it has been described in our paper titled "Leveraging Arabic Sentiment Classification Using an Enhanced CNN-LSTM Approach and Effective Arabic Text ...
Abdulaziz Alayba
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Enhanced TextRank using weighted word embedding for text summarization [PDF]
, 2023 The length of a news article may influence people’s interest to read the article. In this case, text summarization can help to create a shorter representative version of an article to reduce people’s read time.
Pangestu, Nicholas +5 more
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Comparative Analysis of Using Word Embedding in Deep Learning for Text Classification
Jurnal Riset Informatika, 2023 A group of theory-driven computing techniques known as natural language processing (NLP) are used to interpret and represent human discourse automatically.
Mukhamad Rizal Ilham, Arif Dwi Laksito
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Improving Word Embedding Using Variational Dropout
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Pre-trained word embeddings are essential in natural language processing (NLP). In recent years, many post-processing algorithms have been proposed to improve the pre-trained word embeddings.
Zainab Albujasim +3 more
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The activation of embedded words in spoken word recognition [PDF]
Journal of Memory and Language, 2015 The current study investigated how listeners understand English words that have shorter words embedded in them. A series of auditory-auditory priming experiments assessed the activation of six types of embedded words (2 embedded positions × 3 embedded proportions) under different listening conditions.
Xujin, Zhang, Arthur G, Samuel
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Overcoming Poor Word Embeddings with Word Definitions [PDF]
Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics, 2021 Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining. We show that examples that depend critically on a rarer word are more challenging for natural language inference models.
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