[PDF] Top 20 Classification of Micro-Texts Using Sub-Word Embeddings
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Classification of Micro-Texts Using Sub-Word Embeddings
... choose the most suitable classifier using the ex- tracted features proposed in the previous section. To this end, we employed the same small dataset used in Phan and Zincir-Heywood (2018). Table 1 shows the ... See full document
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Domain Adapted Word Embeddings for Improved Sentiment Classification
... quality word embeddings that capture domain specific semantics and are suitable for tasks on the specific ...(DA) embeddings are obtained by com- bining generic embeddings and Domain Specific ... See full document
6
Searching for the X Factor: Exploring Corpus Subjectivity for Word Embeddings
... both word embeddings, there- fore, some of the words are included in both ...a word carries sentiment connotation using sentiment lex- icon compiled by Hu and Liu (2004), including 6789 words ... See full document
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Robust Word Vectors: Context Informed Embeddings for Noisy Texts
... with sub- word units (morphemes or ...on word vec- tor ...level embeddings trained with a CNN can store the information about semantic and grammati- cal features of ...model using ... See full document
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Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification
... A growing number of people get used to give their opinions on social network websites, forums, video sharing websites, blogs and e-commerce websites, leading to a most rising research fields caused by the important ... See full document
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Can Character Embeddings Improve Cause of Death Classification for Verbal Autopsy Narratives?
... handwritten texts to digital format creates many ...purely word-based models, especially ones trained on other corpora, are likely to miss key informa- ...the word- only ... See full document
6
Word Embeddings for Multi-label Document Classification
... shorter texts and in single-label classifi- cation ...of word embeddings for rep- resentation of longer texts in multi-label classifi- cation ...The embeddings are used in three ... See full document
7
Evaluating Word Embeddings in Multi label Classification Using Fine Grained Name Typing
... aka word em- bedding, is an important element of many natu- ral language processing ...of word embeddings is assessed using differ- ent ...evaluate word embeddings on different ... See full document
6
Domain Adapted Word Embeddings for Improved Sentiment Classification
... DA embeddings are used to initial- ize a state-of-the-art sentence encoding algorithm, ...sentence embeddings are then classified using a logistic regression ...DA embeddings. DA ... See full document
9
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings
... Sentiment classification has been used in analyzing user-generated contents for understanding users’ intent and opinions in social ...model using TF-IDF, and probabilistic model using Naïve Bayes, ... See full document
10
UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Merging
... Pretrained word embeddings improve the per- formance of both the lemmatizer and the tagger by a substantial ...the embeddings we trained on CoNLL 2017 UD Shared Task plain texts, we also ... See full document
9
Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of Sanskrit Texts
... trees using both the neighbour-joining and the UPGMA methods for all the ma- trices described above and compare them with the trees manually created by our ...of using word em- beddings were closest ... See full document
14
When Specialization Helps: Using Pooled Contextualized Embeddings to Detect Chemical and Biomedical Entities in Spanish
... While most NLP research is conducted on En- glish datasets, there are a considerable number of non-English biomedically relevant texts writ- ten in other languages, e.g. clinical texts. In order to advance ... See full document
5
Reconstruction of Word Embeddings from Sub Word Parameters
... pre-trained word embeddings as the teacher and sub-lexical embeddings as the ...for classification and framed as matching the probabilities of the student to the probabilities of the ... See full document
6
Delta training: Simple Semi Supervised Text Classification using Pretrained Word Embeddings
... Pretrained Word Embeddings. Pretrained word embeddings are based on the distributed representation hypothesis that a word can be represented as an n-dimensional vector (Mikolov et ... See full document
6
The Role of Protected Class Word Lists in Bias Identification of Contextualized Word Representations
... debias word embeddings, an appropriate word list representing the bias in question needs to be used to define the ...der word list might be a set of pronouns which are specific to a particular ... See full document
7
Merging Verb Senses of Hindi WordNet using Word Embeddings
... for word vectors generation: Continuous SkipGram and Continu- ous Bag of Words ...rent word based on surrounding context, whereas Continuous SkipGram model tries to maximize classification of ... See full document
9
Deconfounded Lexicon Induction for Interpretable Social Science
... We also compare the proposed algorithms: Deep Residualization using word frequencies DR+BOW and embeddings DR+ATTN, and Adversarial Selection using word frequencies A+BOW and embeddings [r] ... See full document
11
A Contrastive Evaluation of Word Sense Disambiguation Systems for Finnish
... This evaluation may be limited by a number of issues. Multiple issues stem from the use of EuroSense. Due to the way it is automatically induced, it contains errors, making its use problematic, especially its use as a ... See full document
13
Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings
... iteratively using the set of labeled data to obtain probabilistically-weighted class labels for each unlabeled document and then training a clas- sifier on the complete corpus based on those anno- ...ence ... See full document
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