Papers with social media text
TweetNLP: Cutting-Edge Natural Language Processing for Social Media (2022.emnlp-demos)
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Jose Camacho-collados, Kiamehr Rezaee, Talayeh Riahi, Asahi Ushio, Daniel Loureiro, Dimosthenis Antypas, Joanne Boisson, Luis Espinosa Anke, Fangyu Liu, Eugenio Martínez Cámara
| Challenge: | TweetNLP is an integrated platform for natural language processing in social media. |
| Approach: | They propose a Python-based platform for natural language processing in social media that supports a variety of NLP tasks. |
| Outcome: | The proposed platform supports generic focus areas such as sentiment analysis and named entity recognition, as well as social media-specific tasks such as emoji prediction and offensive language identification. |
Addressing the Vulnerability of NMT in Input Perturbations (2021.naacl-industry)
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| Challenge: | Recent advances in NMT have improved translation quality but are vulnerable to input perturbations. |
| Approach: | They propose a method to reduce the effect of noisy inputs by using a Context-Enhanced Reconstruction approach. |
| Outcome: | The proposed approach improves robustness on Chinese-English and French-English translation tasks. |
User-Level Race and Ethnicity Predictors from Twitter Text (C18-1)
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| Challenge: | Using social media text to identify user-level race and ethnicity is a useful tool for a range of downstream applications, including passive polling or quantifying demographic bias. |
| Approach: | They propose to collect data from social media users who self-report their race/ethnicity through a survey to develop models which accurately predict the membership of a user to the four largest racial and ethnic groups with up to .884 AUC. |
| Outcome: | The proposed models accurately predict the membership of a user to the four largest racial and ethnic groups with up to .884 AUC and make available to the research community. |
The Design and Construction of a Chinese Sarcasm Dataset (2020.lrec-1)
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| Challenge: | Existing sarcasm datasets are limited to English and Chinese . sarcasm is a multi-layered semi-conscious language phenomenon . |
| Approach: | They propose to build a high-quality Chinese sarcasm dataset using user comments . they use manual annotated sarkastic texts and non-sarcastic texts to train sarcasm classifier . |
| Outcome: | The proposed dataset contains 2,486 manual annotated sarcastic texts and 89,296 non-sarcatic texts. |