A deep learning analysis on question classification task using Word2vec representations

Yılmaz, Şeyhmus; Toklu, Sinan

doi:10.1007/s00521-020-04725-w

Cited by 82 publications

(33 citation statements)

References 38 publications

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“…It is easy to see that the proposed method obtains better or comparable performance. e methods that utilize word embedding/sentence embedding techniques include glove [28], word2vec [29], RNN [30], LSTM [31], and iRNN [32]. Glove denotes global vectors, which is is a word representation tool based on global word frequency statistics.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Exploiting Syntactic and Semantic Information for Textual Similarity Estimation

Luo

Shan

Zhang

et al. 2021

Mathematical Problems in Engineering

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The textual similarity task, which measures the similarity between two text pieces, has recently received much attention in the natural language processing (NLP) domain. However, due to the vagueness and diversity of language expression, only considering semantic or syntactic features, respectively, may cause the loss of critical textual knowledge. This paper proposes a new type of structure tree for sentence representation, which exploits both syntactic (structural) and semantic information known as the weight vector dependency tree (WVD-tree). WVD-tree comprises structure trees with syntactic information along with word vectors representing semantic information of the sentences. Further, Gaussian attention weight is proposed for better capturing important semantic features of sentences. Meanwhile, we design an enhanced tree kernel to calculate the common parts between two structures for similarity judgment. Finally, WVD-tree is tested on widely used semantic textual similarity tasks. The experimental results prove that WVD-tree can effectively improve the accuracy of sentence similarity judgments.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Exploiting Syntactic and Semantic Information for Textual Similarity Estimation

Luo

Shan

Zhang

et al. 2021

Mathematical Problems in Engineering

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“…These methods have been used for measuring semantic similarity [52] between texts and topic modelling [53]. They have been used in conjunction with deep neural networks for language modelling tasks such as topic modeling and semantic analysis [54]. We note that we use word2vec embedding in our framework (Figure 1) for the LSTM language model.…”

Section: Word Embeddingmentioning

confidence: 99%

Biden vs Trump: Modeling US General Elections Using BERT Language Model

Chandra¹,

Saini

2021

IEEE Access

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Social media has played a crucial role in shaping the worldview during election campaigns. It has been used as a medium for political campaigns and a tool for organizing protests; some of which have been peaceful, while others have led to riots. Previous research indicates that understanding user behaviour, particularly in terms of sentiments expressed during elections, can indicate the election outcome. Recently, there has been tremendous progress in language modelling with deep learning via long short-term memory (LSTM) models and variants known as Bidirectional Encoder Representations from Transformers (BERT). Motivated by these innovations, we develop a framework to model the US general elections. We investigate if sentiment analysis can provide a means to predict election outcomes. We use the LSTM and BERT language models for Twitter sentiment analysis leading to the US 2020 presidential elections. Our results show that sentiment analysis can form a general basis for modelling election outcomes where the BERT model indicates Biden winning the elections.

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“…Word vector can also be called word embedding technology, which can map words containing rich semantic information to abstract high-dimensional vector space. It is a method of continuous digital vectorization of words using a shallow neural network [24,25]. e advantage of word vector technology is that it is helpful in solving the problem of data sparsity in traditional question classification methods.…”

Section: Word Vector Matrix Input Layermentioning

confidence: 99%

The Research of Convolutional Neural Network Based on Integrated Classification in Question Classification

Zhen

Sun

2021

Scientific Programming

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As a new generation of search engine, automatic question answering system (QAS) is becoming more and more important and has become one of the hotspots of computer application research and natural language processing (NLP). However, as an indispensable part of the QAS, the role of question classification is an understood thing in the system. In view of this, to further make the performance of question classification much better, both the feature extraction and the classification model were explored. On the study of existing CNN research, an improved CNN model based on Bagging integrated classification (“W2V + B-CNN” for short) is proposed and applied to question classification. Firstly, we combine the characteristics of short texts, use the Word2Vec tool to map the features of the words to a certain dimension, and organize the question sentences into the form of a two-dimensional matrix similar to the image. Then, the trained word vectors are used as the input of the CNN for feature extraction. Finally, the Bagging integrated classification algorithm is used to replace the Softmax classification of the traditional CNN for classification. In other words, the good of W2V + B-CNN model is that it can make use of the advantages of CNN and Bagging integrated classification at the same time. Overall, the new model can not only use the powerful feature extraction capabilities of CNN to extract the potential features of natural language questions but also use the good data classification capabilities of the integrated classification algorithm for feature classification at the same time, which can help improve the accuracy of the W2V + B-CNN in the application of question classification. The comparative experiment results prove that the effect of the W2V + B-CNN is significantly better than that of the CNN and other classification algorithms in question classification.

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A deep learning analysis on question classification task using Word2vec representations

Cited by 82 publications

References 38 publications

Exploiting Syntactic and Semantic Information for Textual Similarity Estimation

Exploiting Syntactic and Semantic Information for Textual Similarity Estimation

Biden vs Trump: Modeling US General Elections Using BERT Language Model

The Research of Convolutional Neural Network Based on Integrated Classification in Question Classification

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