HUMIR at IEST-2018: Lexicon-Sensitive and Left-Right Context-Sensitive BiLSTM for Implicit Emotion Recognition
This paper describes the approaches used in HUMIR system for the WASSA-2018 shared task on the implicit emotion recognition. The objective of this task is to predict the emotion expressed by the target word that has been excluded from the given tweet. We suppose this task as a word sense disambiguation in which the target word is considered as a synthetic word that can express 6 emotions depending on the context. To predict the correct emotion, we propose a deep neural network model that uses two BiLSTM networks to represent the contexts in the left and right sides of the target word. The BiLSTM outputs achieved from the left and right contexts are considered as context-sensitive features. These features are used in a feed-forward neural network to predict the target word emotion. Besides this approach, we also combine the BiLSTM model with lexicon-based and emotion-based features. Finally, we employ all models in the final system using Bagging ensemble method. We achieved macro F-measure value of 68.8 on the official test set and ranked sixth out of 30 participants.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionFeature EngineeringWord Sense DisambiguationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
EmotiKLUE at IEST 2018: Topic-Informed Classification of Implicit Emotions
EmotiKLUE is a submission to the Implicit Emotion Shared Task. It is a deep learning system that combines independent representations of the left and right contexts of the emotion word with the topic distribution of an L…
ClassificationGeneral ClassificationOpinion MiningTreating clitics with minimalist grammars
We propose an extension of Stabler's version of clitics treatment for a wider coverage of the French language. For this, we present the lexical entries needed in the lexicon. Then, we show the recognition of complex synt…
Constrained Decoding for Fill-in-the-Middle Code Language Models via Efficient Left and Right Quotienting of Context-Sensitive Grammars
Large Language Models are powerful tools for program synthesis and advanced auto-completion, but come with no guarantee that their output code is syntactically correct. This paper contributes an incremental parser that a…
Program SynthesisExploring Unsupervised Pretraining Objectives for Machine Translation
Unsupervised cross-lingual pretraining has achieved strong results in neural machine translation (NMT), by drastically reducing the need for large parallel data. Most approaches adapt masked-language modeling (MLM) to se…
DecoderLanguage ModelingLanguage ModellingMachine Translation+3Planted Dense Subgraphs in Dense Random Graphs Can Be Recovered using Graph-based Machine Learning
Multiple methods of finding the vertices belonging to a planted dense subgraph in a random dense $G(n, p)$ graph have been proposed, with an emphasis on planted cliques. Such methods can identify the planted subgraph in …
Graph Neural NetworkPAC learning