Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City (2023.emnlp-main)
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| Challenge: | Critical toponymy studies the dynamics of power, capital, and resistance through place names and the sites to which they refer. |
| Approach: | They propose a model that measures how cultural and economic capital shape the ways in which people refer to places through an annotated dataset of Airbnb listings in New York City. |
| Outcome: | The proposed model can identify important discourse categories integral to the characterization of place. |
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| Challenge: | Named Entity Recognition (NER) datasets annotate coarse-grained entities such as a continent, a country, or a city. |
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
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| Challenge: | Existing datasets built on news focus on non-named entities, but not literary texts. |
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| Challenge: | Existing image-text grounding approaches require detailed annotations, authors say . existing methods are difficult to adapt to unlabeled multi-image, multi-sentence documents, they say . |
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| Challenge: | null |
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| Challenge: | Entities are a central element of knowledge bases and are used in many knowledge-centric tasks including text analysis. |
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NNE: A Dataset for Nested Named Entity Recognition in English Newswire (P19-1)
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| Challenge: | Named entity recognition (NER) is widely used in downstream tasks but most tools focus on flat mention structure over coarse schemas. |
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Tagging Location Phrases in Text (2020.lrec-1)
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| Challenge: | a number of studies have focused on detecting named entities in written language. |
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MasakhaNER: Named Entity Recognition for African Languages (2021.tacl-1)
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David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, Jesujoba Alabi, Seid Muhie Yimam, Tajuddeen Rabiu Gwadabe, Ignatius Ezeani, Rubungo Andre Niyongabo, Jonathan Mukiibi, Verrah Otiende, Iroro Orife, Davis David, Samba Ngom, Tosin Adewumi, Paul Rayson, Mofetoluwa Adeyemi, Gerald Muriuki, Emmanuel Anebi, Chiamaka Chukwuneke, Nkiruka Odu, Eric Peter Wairagala, Samuel Oyerinde, Clemencia Siro, Tobius Saul Bateesa, Temilola Oloyede, Yvonne Wambui, Victor Akinode, Deborah Nabagereka, Maurice Katusiime, Ayodele Awokoya, Mouhamadane MBOUP, Dibora Gebreyohannes, Henok Tilaye, Kelechi Nwaike, Degaga Wolde, Abdoulaye Faye, Blessing Sibanda, Orevaoghene Ahia, Bonaventure F. P. Dossou, Kelechi Ogueji, Thierno Ibrahima DIOP, Abdoulaye Diallo, Adewale Akinfaderin, Tendai Marengereke, Salomey Osei
| Challenge: | (2020) African languages are underrepresented in existing natural language processing datasets, research, and tools due to lack of datasets and reproducible results. |
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| Outcome: | The results of the first large dataset for named entity recognition (NER) in ten African languages are released to inform future research on African NLP. |
Discontinuous Named Entity Recognition as Maximal Clique Discovery (2021.acl-long)
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| Challenge: | Existing methods for named entity recognition break the recognition process into several sequential steps. |
| Approach: | They propose a method that breaks the recognition process into several sequential steps . they construct a segment graph for each sentence and a grid tagging scheme to learn it . |
| Outcome: | Experiments show that the proposed method outperforms the state-of-the-art model and achieves 5x speedup over the SOTA model. |