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Found 7 papers, 2 papers with code
ConTextING: Granting Document-Wise Contextual Embeddings to Graph Neural Networks for Inductive Text Classification
In this work, we propose a simple yet effective unified model, coined ConTextING, with a joint training mechanism to learn from both document embeddings and contextual word interactions simultaneously.
Unsupervised Multi-document Summarization for News Corpus with Key Synonyms and Contextual Embeddings
Information overload has been one of the challenges regarding information from the Internet.
Unsupervised Text Summarization of Long Documents using Dependency-based Noun Phrases and Contextual Order Arrangement
This work addresses the common word issue with a phrase-level graph that (1) focuses on the noun phrases of a document based on grammar dependencies and (2) initializes edge weights by term-frequency within the target document and inverse document frequency over the entire corpus.
EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation
By the nature of the framework of BERT, a two-sentence structure, we adapt BERT to continues dialogue emotion prediction tasks, which rely heavily on the sentence-level context-aware understanding.
Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences
The factors examined are gender differences, syntactic patterns, and bipolar recognition performance.
CARER: Contextualized Affect Representations for Emotion Recognition
Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs.
