Word embedding

About: Word embedding is a research topic. Over the lifetime, 4683 publications have been published within this topic receiving 153378 citations. The topic is also known as: word embeddings.

Extractive document summarization based on convolutional neural networks

Abstract: Extractive summarization aims to generate a summary by ranking sentences, whose performance relies heavily on the quality of sentence features. In this paper, a document summarization framework based on convolutional neural networks is successfully developed to learn sentence features and perform sentence ranking jointly. We adapt the original CNN model to address a regression process for sentence ranking. Pre-trained word vectors are used to enhance the performance of our model. We evaluate our proposed method on the DUC 2002 and 2004 datasets covering single and multi-document summarization tasks respectively. The proposed system achieves competitive or even better performance compared with state-of-the-art document summarization systems.

Sentiment Classification: Feature Selection Based Approaches Versus Deep Learning

Abstract: Classification of text documents is commonly carried out using various models of bag-of-words that are generated using feature selection methods. In these models, selected features are used as input to well-known classifiers such as Support Vector Machines (SVM) and neural networks. In recent years, a technique called word embeddings has been developed for text mining and, deep learning models using word embeddings have become popular for sentiment classification. However, there is no extensive study has been conducted to compare these approaches for sentiment classification. In this paper, we present an in-depth comparative study on these two types of approaches, feature selection based approaches and and deep learning models for document-level sentiment classification. Experiments were conducted using four datasets with varying characteristics. In order to investigate the effectiveness of word embeddings features, feature sets including combination of selected bag-of-words features and averaged word embedding features were used in sentiment classification. For analyzing deep learning models, we implemented three different deep learning architecture, convolutional neural network, long short-term memory network, and long-term recurrent convolutional network. Our experimental results show that that deep learning models performed better on three out of the four datasets, a combination of selected bag-of-words features and averaged word embedding features gave the best performance on one dataset. In addition, we will show that a deep learning model initialized with either one-hot vectors or fine-tuned word embeddings performed better than the model initialized using than word embeddings without tuning.

Performance Comparison of Machine Learning Classifiers for Fake News Detection

Abstract: Information sharing on the web particularly via web-based networking media is increasing. Ability to identify, evaluate and address such information is significantly important. Fake information deliberately created is purposefully or unintentionally engendered over the internet. This is affecting a larger group of society who are blinded by technology. This paper illustrates model and methodology to detect fake news from news article with the assistance of Machine learning and Natural language processing. In this proposed work different feature engineering methods like count vector, TF-IDF and word embedding are used to generate feature vector. Seven different Machine learning Classification algorithms are trained to classify news as fake or real and are compared considering accuracy, F1 Score, recall, precision and best one is selected to build a model to classify news as fake or real.

Cross-Domain Sentiment Encoding through Stochastic Word Embedding

Abstract: Sentiment analysis is an important topic concerning identification of feelings, attitudes, emotions and opinions from text. To automate such analysis, a large amount of example text needs to be manually annotated for model training. This is laborious and expensive, but the cross-domain technique is a key solution to reducing the cost by reusing annotated reviews across domains. However, its success largely relies on the learning of a robust common representation space across domains. In the recent years, significant effort has been invested to improve the cross-domain representation learning by designing increasingly more complex and elaborate model inputs and architectures. We support that it is not necessary to increase design complexity as this inevitably consumes more time in model training. Instead, we propose to explore the word polarity and occurrence information through a simple mapping and encode such information more accurately whilst managing lower computational costs. The proposed approach is unique and takes advantage of the stochastic embedding technique to tackle cross-domain sentiment alignment. Its effectiveness is benchmarked with over ten data tasks constructed from two review corpora and it is compared against ten classical and state-of-the-art methods.

Intrinsic Subspace Evaluation of Word Embedding Representations

Abstract: We introduce a new methodology for intrinsic evaluation of word representations. Specifically, we identify four fundamental criteria based on the characteristics of natural language that pose difficulties to NLP systems; and develop tests that directly show whether or not representations contain the subspaces necessary to satisfy these criteria. Current intrinsic evaluations are mostly based on the overall similarity or full-space similarity of words and thus view vector representations as points. We show the limits of these point-based intrinsic evaluations. We apply our evaluation methodology to the comparison of a count vector model and several neural network models and demonstrate important properties of these models.