17,348 research outputs found
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
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, etc. 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.Comment: AAAI 201
Deep Memory Networks for Attitude Identification
We consider the task of identifying attitudes towards a given set of entities
from text. Conventionally, this task is decomposed into two separate subtasks:
target detection that identifies whether each entity is mentioned in the text,
either explicitly or implicitly, and polarity classification that classifies
the exact sentiment towards an identified entity (the target) into positive,
negative, or neutral.
Instead, we show that attitude identification can be solved with an
end-to-end machine learning architecture, in which the two subtasks are
interleaved by a deep memory network. In this way, signals produced in target
detection provide clues for polarity classification, and reversely, the
predicted polarity provides feedback to the identification of targets.
Moreover, the treatments for the set of targets also influence each other --
the learned representations may share the same semantics for some targets but
vary for others. The proposed deep memory network, the AttNet, outperforms
methods that do not consider the interactions between the subtasks or those
among the targets, including conventional machine learning methods and the
state-of-the-art deep learning models.Comment: Accepted to WSDM'1
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