| Challenge: | Named Entity Recognition (NER) is a subtask of information extraction that uses predefined named entities to identify NEs in noisy texts. |
| Approach: | They propose to use a Turkish Twitter Named Entity Recognition dataset to identify predefined named entities (NEs) the dataset contains 5000 tweets from a year-long period with a high agreement score. |
| Outcome: | The proposed dataset contains 5000 tweets from a year-long period and has high agreement scores. |
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| Challenge: | Named Entity Recognition (NER) is a longstanding NLP task that consists of identifying an entity in a sentence or document. |
| Approach: | They construct a dataset of seven entity types annotated over 11,382 tweets . they provide a set of language model baselines and analyze the performance of the model . |
| Outcome: | The proposed dataset contains seven entity types annotated over 11,382 tweets . the authors focus on short-term degradation of NER models over time and strategies to fine-tune a language model over different periods . |
A Broad-coverage Corpus for Finnish Named Entity Recognition (2020.lrec-1)
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| Challenge: | Named entity recognition (NER) is a fundamental task in natural language processing (NLP). |
| Approach: | They propose to annotate Finnish named entity names using a new corpus built on the Universal Dependencies corpus. |
| Outcome: | The new annotation identifies over 10,000 mentions and maintains compatibility with a previously released single-domain corpus for Finnish NER. |
Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis (2022.lrec-1)
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| Challenge: | Social media data such as Twitter messages pose a particular challenge to NLP systems because of their short, noisy nature. |
| Approach: | They create a Twitter-based NER corpus and train Tweet NLP models on it . they annotate named entities in TB2 using Amazon Mechanical Turk . |
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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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A Corpus of Turkish Offensive Language on Social Media (2020.lrec-1)
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| Challenge: | Identifying abusive, offensive, aggressive or in general inappropriate language has recently attracted interest of researchers from academic as well as commercial institutions. |
| Approach: | They propose to classify Turkish offensive language corpus using state-of-the-art annotation methods . they find 19 % of tweets contain some type of offensive language . |
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Towards a Standardized Dataset on Indonesian Named Entity Recognition (2020.aacl-srw)
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| Challenge: | Named entity recognition (NER) tasks in the Indonesian language are still lacking data for the majority of languages, including Indonesian. |
| Approach: | They re-annotated an open dataset with 2,000 sentences and compared the results with a bidirectional long short-term memory and conditional random field approach. |
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BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset (L18-1)
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| Challenge: | Named-entity recognition (NER) is a natural language processing component that aims to identify all the "named entities" (NEs) in an unstructured text. |
| Approach: | They propose a deep learning approach for name-entity recognition in Persian . they publicize an entity-annotated Persian dataset and train word embeddings . |
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Reconstructing NER Corpora: a Case Study on Bulgarian (2020.lrec-1)
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| Challenge: | Named Entity Recognition (NER) and Named Enel Linking (NEL) are two related tasks that are under-resourced for the Slavic languages. |
| Approach: | They propose to use deep learning methods to improve a Named Entity Recognition corpus and to predict and annotate new types in a test corpus. |
| Outcome: | The proposed model improves a type-based Named Entity Recognition (NER) training corpus and predicts and annotates new types in a test corpus. |
A French Corpus for Event Detection on Twitter (2020.lrec-1)
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| Challenge: | Existing datasets may have different definitions of event or topic, which leads to inconsistent results. |
| Approach: | They present a corpus annotated for event detection tasks consisting of 38 million tweets in French and 130,000 manually annotating tweets as related or unrelated to a given event. |
| Outcome: | The proposed method performs best on 38 million tweets in French and another publicly available dataset of tweets. |
A Diverse Set of Freely Available Linguistic Resources for Turkish (2023.acl-long)
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| Challenge: | despite the abundance of Turkish speakers, linguistic resources for natural language processing remain scarce. |
| Approach: | They propose a set of freely available linguistic resources for Turkish natural language processing . they provide corpora and pretrained models to help practitioners build their own applications . |
| Outcome: | The proposed linguistic resources are first of their kind and easy to use in a broad range of implementations. |