Abstract
We investigate the problem of extracting Indian-locations from a given crowd-sourced textual dataset. The problem of extracting fine-grained Indian-locations has many challenges. One challenge in the task is to collect relevant dataset from the crowd-sourced platforms that contain locations. The second challenge lies in extracting the location entities from the collected data. We provide an in-depth review of the information collection process and our annotation guidelines such that a reliable dataset annotation is guaranteed. We evaluate many recent algorithms and models, including Conditional Random fields (CRF), Bi-LSTM-CNN and BERT (Bidirectional Encoder Representations from Transformers), on our developed dataset named . The study shows the best F1-score of 72.49% for BERT, followed by Bi-LSTM-CNN and CRF. As a result of our work, we prepare a publicly-available annotated dataset of Indian geolocations that can be used by the research community. Code and dataset are available at https://github.com/vkartik2k/STHAL.- Anthology ID:
- 2020.icon-main.52
- Volume:
- Proceedings of the 17th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2020
- Address:
- Indian Institute of Technology Patna, Patna, India
- Editors:
- Pushpak Bhattacharyya, Dipti Misra Sharma, Rajeev Sangal
- Venue:
- ICON
- SIG:
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 379–383
- Language:
- URL:
- https://aclanthology.org/2020.icon-main.52
- DOI:
- Cite (ACL):
- Kartik Verma, Shobhit Sinha, Md. Shad Akhtar, and Vikram Goyal. 2020. STHAL: Location-mention Identification in Tweets of Indian-context. In Proceedings of the 17th International Conference on Natural Language Processing (ICON), pages 379–383, Indian Institute of Technology Patna, Patna, India. NLP Association of India (NLPAI).
- Cite (Informal):
- STHAL: Location-mention Identification in Tweets of Indian-context (Verma et al., ICON 2020)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2020.icon-main.52.pdf
- Code
- vkartik2k/sthal
- Data
- CoNLL 2002