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Automatic assignment of microgenres to movies using a word embedding-based approach
Streaming services are increasingly leveraging Artificial Intelligence (AI) technologies for improved content cataloging, user experiences in content...
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Impact of word embedding models on text analytics in deep learning environment: a review
The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed...
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Estimating vulnerability metrics with word embedding and multiclass classification methods
Cyber security has an increasing importance since the day when information technologies are an invariable part of modern human life. One of the...
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Word embedding for mixed-emotions analysis
Word embedding is the process of converting words into vectors of real numbers which is of great interest in natural language processing. Recently,...
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Using word embedding to detect keywords in texts modeled as complex networks
Detecting keywords in texts is a task of paramount importance for many text mining applications. Graph-based techniques have been commonly used to...
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A privacy-preserving word embedding text classification model based on privacy boundary constructed by deep belief network
To effectively extract and classify the information from reports or documents and protect the privacy of the extracted results, we propose a privacy...
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Prototype Theory Meets Word Embedding: A Novel Approach for Text Categorization via Granular Computing
The problem of the information representation and interpretation coming from senses by the brain has plagued scientists for decades. The same...
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Impact of preprocessing and word embedding on extreme multi-label patent classification tasks
Patent classification is a necessary step in the efficient processing of patent data and ensuring convenient information access to users. To address...
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SeNSe: embedding alignment via semantic anchors selection
Word embeddings have proven extremely useful across many NLP applications in recent years. Several key linguistic tasks, such as machine translation...
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Extracting White-Box Knowledge from Word Embedding: Modeling as an Optimization Problem
Explainability is crucial to building the confidence of the medical team to adopt natural language processing (NLP) techniques. In the majority of...
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Evaluating Word Embedding Feature Extraction Techniques for Host-Based Intrusion Detection Systems
Research into Intrusion and Anomaly Detectors at the Host level typically pays much attention to extracting attributes from system call traces. These...
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A Study of Word Embedding Models for Machine Translation of North Eastern Languages
Neural Machine Translation (NMT) has experienced significant growth in recent years and is now a well-established field. Despite being the most...
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Text-based emotion recognition using contextual phrase embedding model
In this paper, the proposed approach categories the sentences in the dataset into the various topical documents using the TE-LSTM+SC model. As well...
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Probabilistic topic modeling for short text based on word embedding networks
Uncovering topics in short texts can be an arduous task. The inadequacy of general-purpose topic models for handling short documents may be explained...
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Self-supervised phrase embedding method by fusing internal and external semantic information of phrases
The quality of the phrase embedding is related to the performance of many NLP downstream tasks. Most of the existing phrase embedding methods are...
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Pronunciation-Enhanced Chinese Word Embedding
Chinese word embeddings have recently garnered considerable attention. Chinese characters and their sub-character components, which contain rich...
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Exploring best-matched embedding model and classifier for charging-pile fault diagnosis
The continuous increase of electric vehicles is being facilitating the large-scale distributed charging-pile deployment. It is crucial to guarantee...
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A word embedding-based approach to cross-lingual topic modeling
The cross-lingual topic analysis aims at extracting latent topics from corpora of different languages. Early approaches rely on high-cost...
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Word Sense Disambiguation for Indic Language using Bi-LSTM
In the enormous field of Natural Language Processing (NLP), deciphering the intended significance of a word among a multitude of possibilities is...
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Semantic Similarity of Inverse Morpheme Words Based on Word Embedding
Inverse morpheme words are compound words that have the same morphemes but are arranged in the opposite order. The majority of related works on the...