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ConceptNet 5.5: An Open Multilingual Graph of General Knowledge Open
Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be u…
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ConceptNet 5.5: An Open Multilingual Graph of General Knowledge Open
Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be u…
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Word2Vec Open
My last column ended with some comments about Kuhn and word2vec. Word2vec has racked up plenty of citations because it satisifies both of Kuhn’s conditions for emerging trends: (1) a few initial (promising, if not convincing) successes tha…
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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition Open
Inspired by natural\nlanguage processing techniques, we here introduce\nMol2vec, which is an unsupervised machine learning approach to learn\nvector representations of molecular substructures. Like the Word2vec\nmodels, where vectors of cl…
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Network Embedding as Matrix Factorization Open
Since the invention of word2vec, the skip-gram model has significantly\nadvanced the research of network embedding, such as the recent emergence of the\nDeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of\nthe a…
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Using Convolutional Neural Networks to Classify Hate-Speech Open
The paper introduces a deep learning-based Twitter hate-speech text classification system. The classifier assigns each tweet to one of four predefined categories: racism, sexism, both (racism and sexism) and non-hate-speech. Four Convoluti…
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Deep learning in clinical natural language processing: a methodical review Open
Objective This article methodically reviews the literature on deep learning (DL) for natural language processing (NLP) in the clinical domain, providing quantitative analysis to answer 3 research questions concerning methods, scope, and co…
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Two/Too Simple Adaptations of Word2Vec for Syntax Problems Open
Wang Ling, Chris Dyer, Alan W. Black, Isabel Trancoso. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.
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Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism Open
There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. …
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A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification Open
Sarcasm identification on text documents is one of the most challenging tasks in natural language processing (NLP), has become an essential research direction, due to its prevalence on social media data. The purpose of our research is to p…
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A Latent Variable Model Approach to PMI-based Word Embeddings Open
Semantic word embeddings represent the meaning of a word via a vector, and are created by diverse methods. Many use nonlinear operations on co-occurrence statistics, and have hand-tuned hyperparameters and reweighting methods. This paper p…
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A CNN-BiLSTM Model for Document-Level Sentiment Analysis Open
Document-level sentiment analysis is a challenging task given the large size of the text, which leads to an abundance of words and opinions, at times contradictory, in the same document. This analysis is particularly useful in analyzing pr…
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WELFake: Word Embedding Over Linguistic Features for Fake News Detection Open
Social media is a popular medium for the dissemination of real-time news all over the world. Easy and quick information proliferation is one of the reasons for its popularity. An extensive number of users with different age groups, gender,…
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Two-Stage Topic Extraction Model for Bibliometric Data Analysis Based on Word Embeddings and Clustering Open
Topic extraction is an essential task in bibliometric data analysis, data mining and knowledge discovery, which seeks to identify significant topics from text collections. The conventional topic extraction schemes require human interventio…
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Drug drug interaction extraction from biomedical literature using syntax convolutional neural network Open
Motivation: Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is …
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Word2Vec Model Analysis for Semantic Similarities in English Words Open
This paper examines the calculation of the similarity between words in English using word representation techniques. Word2Vec is a model used in this paper to represent words into vector form. The model in this study was formed using the 3…
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A Novel Hybrid Deep Learning Model for Sentiment Classification Open
A massive use of social media platforms such as Twitter and Facebook by omnifarious organizations has increased the critical individual feedback on the situation, events, products, and services. However, sentiment classification plays an i…
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Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships Open
Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectr…
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A Theoretical Analysis of Contrastive Unsupervised Representation Learning Open
Recent empirical works have successfully used unlabeled data to learn feature representations that are broadly useful in downstream classification tasks. Several of these methods are reminiscent of the well-known word2vec embedding algorit…
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Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning Open
The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals’ emotions empowers sentiment analysis. However, sentiment analysis beco…
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Geo-Teaser Open
Point-of-interest (POI) recommendation is an important application for location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. Previous studies show that mode…
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Refining Word Embeddings for Sentiment Analysis Open
Word embeddings that can capture semantic and syntactic information from contexts have been extensively used for various natural language processing tasks. However, existing methods for learning context-based word embeddings typically fail…
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A BERT Framework to Sentiment Analysis of Tweets Open
Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the …
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Word2vec convolutional neural networks for classification of news articles and tweets Open
Big web data from sources including online news and Twitter are good resources for investigating deep learning. However, collected news articles and tweets almost certainly contain data unnecessary for learning, and this disturbs accurate …
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Tweets Classification on the Base of Sentiments for US Airline Companies Open
The use of data from social networks such as Twitter has been increased during the last few years to improve political campaigns, quality of products and services, sentiment analysis, etc. Tweets classification based on user sentiments is …
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Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change Open
Understanding how words change their meanings over time is key to models of language and cultural evolution, but historical data on meaning is scarce, making theories hard to develop and test. Word embeddings show promise as a diachronic t…
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Comparative study of word embedding methods in topic segmentation Open
The vector representations of words are very useful in different natural language processing tasks in order to capture the semantic meaning of words. In this context, the three known methods are: LSA, Word2Vec and GloVe. In this paper, the…
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Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations Open
Being a prevalent form of social communications on the Internet, billions of short texts are generated everyday. Discovering knowledge from them has gained a lot of interest from both industry and academia. The short texts have a limited c…
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Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis Open
Blockchain has become one of the core technologies in Industry 4.0. To help decision-makers establish action plans based on blockchain, it is an urgent task to analyze trends in blockchain technology. However, most of existing studies on b…
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Sentiment Analysis Using Word2vec And Long Short-Term Memory (LSTM) For Indonesian Hotel Reviews Open
Generally, Online Travel Agent (OTA) has a review element where clients can give reviews of the facilities they have used. Availability of a huge volume of reviews makes it troublesome for service executives to know the percent of reviews …