scholarly journals Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings

2015 ◽  
Author(s):  
Pengfei Liu ◽  
Shafiq Joty ◽  
Helen Meng
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Opinion Mining ◽  
Word Embeddings ◽  
Fine Grained

scholarly journals DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

2019 ◽  
Vol 33 ◽  
pp. 6818-6825 ◽  
Author(s):  
Navonil Majumder ◽  
Soujanya Poria ◽  
Devamanyu Hazarika ◽  
Rada Mihalcea ◽  
Alexander Gelbukh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Recurrent Neural Networks ◽  
Opinion Mining ◽  
State Of The Art ◽  
Emotion Detection ◽  
Emotion Classification ◽  
Argumentation Mining ◽  
Consumer Feedback ◽  
The Individual

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, and so on. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state-of-the-art by a significant margin on two different datasets.

scholarly journals Predicting Protein Binding Affinity With Word Embeddings and Recurrent Neural Networks

2017 ◽  
Author(s):  
Carlo Mazzaferro
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Antigenic Determinants ◽  
Underlying Structure ◽  
Specific Response ◽  
Network Architectures ◽  
Learning Approaches ◽  
Word Embeddings ◽  
Viral Pathogens ◽  
Histocompatibility Complex

AbstractAt the core of our immunological system lies a group of proteins named Major Histocompatibility Complex (MHC), to which epitopes (also proteins sometimes named antigenic determinants), bind to eliciting a response. These responses are extremely varied and of widely different nature. For instance, Killer and Helper T cells are responsible for, respectively, counteracting viral pathogens and tumorous cells. Many other types exist, but their underlying structure can be very similar due to the fact that they all are proteins and bind to the MHC receptor in a similar fashion. With this framework in mind, being able to predict with precision the structure of a protein that will elicit a specific response in the human body represents a novel computational approach to drug discovery. Although many machine learning approaches have been used, no attempt to solve this problem using Recurrent Neural Networks (RNNs) exist. We extend the current efforts in the field by applying a variety of network architectures based on RNNs and word embeddings (WE). The code is freely available and under current development at https://github.com/carlomazzaferro/mhcPreds

scholarly journals Deep recurrent neural networks with word embeddings for Urdu named entity recognition

ETRI Journal ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 90-100
Author(s):  
Wahab Khan ◽  
Ali Daud ◽  
Fahd Alotaibi ◽  
Naif Aljohani ◽  
Sachi Arafat
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Word Embeddings ◽  
Named Entity
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