Results 51 to 60 of about 1,006,138 (295)

Word-class embeddings for multiclass text classification

, 2021
Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and sentiment analysis, to name a few.
Moreo A, Sebastiani F, Esuli A
core   +1 more source

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, Ruifeng Xu, Yulan He, Xuan Wang   +3 more
doaj   +1 more source

Dynamic Word Embeddings

, 2017
In the proceedings of the International Conference on Machine Learning (ICML 2017); 8 pages + references and ...
MANDT STEPHAN MARCEL, BAMLER ROBERT
openaire   +4 more sources

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
openaire   +2 more sources

Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean

Applied Sciences, 2019
To resolve lexical disagreement problems between queries and frequently asked questions (FAQs), we propose a reliable sentence classification model based on an encoder-decoder neural network.
Youngjin Jang, Harksoo Kim
doaj   +1 more source

Comparison of results between four word embeddings.

, 2023
Comparison of results between four word embeddings.
Khadija Akter Lima (17027752), Sidratul Montaha (13200394), Mirjam Jonkman (9765452), Khan Md Hasib (15946547), Asif Karim (4419889), Sami Azam (9765449), Sheak Rashed Haider Noori (10533015)   +6 more
core   +1 more source

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, Leif Azzopardi, Bruza, Peter, Koopman, Bevan, Guido Zuccon, Azzopardi, Leif, Zuccon, Guido, Peter Bruza   +7 more
core   +2 more sources

Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries

Information, 2015
Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts.
Shengyu Liu, Buzhou Tang, Qingcai Chen, Xiaolong Wang   +3 more
doaj   +1 more source

Understanding and Creating Word Embeddings

The Programming Historian
Word embeddings allow you to analyze the usage of different terms in a corpus of texts by capturing information about their contextual usage. Through a primarily theoretical lens, this lesson will teach you how to prepare a corpus and train a word ...
Avery Blankenship, Sarah Connell, Quinn Dombrowski   +2 more
doaj   +1 more source

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, Johannes Reisinger, Christoph Goller, Andreas Fischer 0001   +3 more
openaire   +2 more sources