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        <title>STHAL: Location-mention Identification in Tweets of Indian-context</title>
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    <name type="personal">
        <namePart type="given">Kartik</namePart>
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        <namePart type="given">Shobhit</namePart>
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    <name type="personal">
        <namePart type="given">Vikram</namePart>
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            <title>Proceedings of the 17th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Pushpak</namePart>
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            <namePart type="given">Rajeev</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T STHAL: Location-mention Identification in Tweets of Indian-context
%A Verma, Kartik
%A Sinha, Shobhit
%A Akhtar, Md. Shad
%A Goyal, Vikram
%Y Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Sangal, Rajeev
%S Proceedings of the 17th International Conference on Natural Language Processing (ICON)
%D 2020
%8 December
%I NLP Association of India (NLPAI)
%C Indian Institute of Technology Patna, Patna, India
%F verma-etal-2020-sthal
%X 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.
%U https://aclanthology.org/2020.icon-main.52
%P 379-383

Markdown (Informal)

[STHAL: Location-mention Identification in Tweets of Indian-context](https://aclanthology.org/2020.icon-main.52) (Verma et al., ICON 2020)

• STHAL: Location-mention Identification in Tweets of Indian-context (Verma et al., ICON 2020)

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).