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.

A Survey of Textual Emotion Recognition and Its Challenges

Abstract: Textual language is the most natural carrier of human emotion. In natural language processing, textual emotion recognition (TER) has become an important topic due to its significant academic and commercial potential. With the advanced development of deep learning technologies, TER has attracted growing attention and has significantly been promoted in recent years. This paper provides a systematic survey of latest TER advances, focusing on approaches using deep neural networks. According to how deep learning works at each stage, TER approaches are reviewed on word embedding, architecture, and training levels, respectively. We discussed the remaining challenges and opportunities from four aspects: the shortage of large-scale and high-quality dataset, fuzzy emotional boundaries, incomplete extractable emotional information in texts, and TER in dialogue. This paper creates a systematic and in-depth overview of deep TER technologies. It provides the necessary knowledge and new insights for relevant researchers to understand better the research state, remaining challenges, and future directions in this field.

A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

Abstract: Accurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical location prediction neural network for Twitter user geolocation. Our model first predicts the home country for a user, then uses the country result to guide the city-level prediction. In addition, we employ a character-aware word embedding layer to overcome the noisy information in tweets. With the feature fusion layer, our model can accommodate various feature combinations and achieves state-of-the-art results over three commonly used benchmarks under different feature settings. It not only improves the prediction accuracy but also greatly reduces the mean error distance.

Comparison of Pre-Trained Word Vectors for Arabic Text Classification Using Deep Learning Approach

Abstract: Artificial Intelligence (AI) has been used widely to extract people's opinions from social media websites. However, most of the existing works focus on eliciting the features from English text. In this paper, we describe an Arabic text sentiment analysis approach using a Deep Neural network, namely Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). In this research, we investigate how the different pre-trained Word Embedding (WE) models affect our model's accuracy. The dataset includes Arabic corpus collected from Twitter. The results show significant improvement in Arabic text classification.

Emotion Recognition from Text Stories Using an Emotion Embedding Model

Abstract: In this paper, we analyze emotions in a story text using an emotion embedding model. Firstly, we collected 144,701 tweets, and each tweet is given an emotional hashtag. Using the emotion hashtag as an emotion label, we built a CNN model for emotion classification. We then extracted the embedding model created during the learning process. We then extracted word embedding layer created during the emotion classification learning process. We defined this as an ‘Emotion embedding model’, and applied it to classify story text emotions. The story text used in emotion analysis was ROC story data, and those story sentences are classified into eight emotions based on plutchik’s emotion model.

Density Matching for Bilingual Word Embedding.

Abstract: Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages. In this paper, we propose an approach that instead expresses the two monolingual embedding spaces as probability densities defined by a Gaussian mixture model, and matches the two densities using a method called normalizing flow. The method requires no explicit supervision, and can be learned with only a seed dictionary of words that have identical strings. We argue that this formulation has several intuitively attractive properties, particularly with the respect to improving robustness and generalization to mappings between difficult language pairs or word pairs. On a benchmark data set of bilingual lexicon induction and cross-lingual word similarity, our approach can achieve competitive or superior performance compared to state-of-the-art published results, with particularly strong results being found on etymologically distant and/or morphologically rich languages.