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Found 7 papers, 2 papers with code
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.
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 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.
