Papers by Mingyu Wan

4 papers
Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)

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Challenge: Existing deep neural network models lack mechanisms to highlight important sentiment terms.
Approach: They propose a method to incorporate affective knowledge into deep neural network models by mapping affective influence vectors to an affective impact value and integrating them into long-term memory models to highlight affective terms.
Outcome: The proposed approach improves on three large datasets by 1.0% to 1.5% on the benchmark datasets.
When Cantonese NLP Meets Pre-training: Progress and Challenges (2022.aacl-tutorials)

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Challenge: Cantonese is an influential Chinese variant with a large population of speakers worldwide.
Approach: This tutorial will review Cantonese's progress in linguistics and NLP . it will introduce transformer-based pre-training methods for a wide range of downstream tasks .
Outcome: This tutorial will present the main challenges for Cantonese NLP in relation to Cantonesian language idiosyncrasies of colloquialism and multilingualism.
Modality Enriched Neural Network for Metaphor Detection (2020.coling-main)

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Challenge: Existing methods for detecting metaphors are limited due to ontological differences between metaphorical and non-metaphorical expressions.
Approach: They propose a modality enriched deep learning model that integrates deep learning with effective linguistic insight to detect metaphors in texts.
Outcome: The proposed model improves on previous models and shows that it can be used to detect metaphors in a language context.
Sina Mandarin Alphabetical Words:A Web-driven Code-mixing Lexical Resource (2020.aacl-main)

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Challenge: Mandarin Alphabetical Words (MAWs) are a key component of Modern Chinese . they are characterized by unique code-mixing idiosyncrasies influenced by language exchanges .
Approach: They propose to construct a large collection of Mandarin Alphabetic Words from Sina Weibo . they propose to use a web-based technique to identify and validate MAWs .
Outcome: The proposed method identifies 16,207 Mandarin Alphabetic Words (MAWs) using a web-based technique . the results show that the proposed method is useful for linguistic research and inquiries .

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