Bert-Caps
A Transformer-Based Capsule Network for Tweet Act Classification
Bibliographic Data
| ID | 22107752 |
|---|---|
| Authors | Tulika Saha (0000-0002-3252-0997, Indian Institute of Technology Patna), Srivatsa Ramesh Jayashree (0000-0002-3985-1211, Indian Institute of Technology Patna), Sriparna Saha (0000-0001-5458-9381, Indian Institute of Technology Patna), Pushpak Bhattacharyya (0000-0001-5319-5508, Indian Institute of Technology Patna) |
| Year | 2020 |
| Volume | 7 |
| Issue | 5 |
| Pages | 1168-1179 |
| Publication date | 2020-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2020.3014128 |
| OpenAlex | W3101904655 |
| Language | EN |
| Citations received | 5 |
| References cited | 24 |
Identification of speech acts provides essential cues in understanding the pragmatics of a user utterance. It typically helps in comprehending the communicative intention of a speaker. This holds true for conversations or discussions on any fora, including social media platforms, such as Twitter. This article presents a novel tweet act classifier (speech act for Twitter) for assessing the content and intent of tweets, thereby exploring the valuable communication among the tweeters. With the recent success of Bidirectional Encoder Representations from Transformers (BERT), a newly introduced language representation model that provides pretrained deep bidirectional representations of vast unlabeled data, we introduce BERT-Caps that is built on top of BERT. The proposed model tends to learn traits and attributes by leveraging from the joint optimization of features from the BERT and capsule layer to develop a robust classifier for the task. Some Twitter-specific symbols are also included in the model to observe its influence and importance. The proposed model attained a benchmark accuracy of 77.52% and outperformed several strong baselines and state-of-the-art approaches
Encoder · Language model · Linguistics · Machine learning · Natural language processing · Pragmatics · Social media · Speech recognition · Transformer · Utterance · World Wide Web · Computer Science · Natural Language Processing Techniques · Speech and dialogue systems · Topic Modeling · Artificial Intelligence
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| Unique citing works | 5 |
|---|---|
| Citations per year | 1 |
| Citation span | 2021 - 2025 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |