Distinguishing Address vs. Reference Mentions of Personal Names in Text (2023.findings-acl)
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| Challenge: | Named entity recognition (NER) is a core task in the NLP community . but not much work has been done to distinguish between addressing and referring to entities . |
| Approach: | They propose an automatic tagger that captures the address vs. reference distinction in English . they demonstrate how this distinction is important in NLP and computational social science applications . |
| Outcome: | The proposed tagger performs at 85% accuracy in distinguishing between address and reference in English . many modern Indo-European languages do not have such vocative case markers . |
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| Challenge: | a number of studies have focused on detecting named entities in written language. |
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Comparing Annotated Datasets for Named Entity Recognition in English Literature (2022.lrec-1)
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| Challenge: | Generally speaking, the majority of NER tools struggle to perform well when the entities in the text contain specific characteristics. |
| Approach: | They analysed two existing annotated datasets and two additional gold standard datasets to evaluate the performance of two NER tools. |
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Comprehensive Supersense Disambiguation of English Prepositions and Possessives (P18-1)
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Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah R. Moeller, Aviram Stern, Adi Bitan, Omri Abend
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Recognizing Complex Entity Mentions: A Review and Future Directions (P18-3)
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| Challenge: | Named entity recognition (NER) is a task of identifying and classifying named entities (NE) within text. |
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Fine-Grained Evaluation for Entity Linking (D19-1)
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| Challenge: | Entity Linking (EL) is an Information Extraction task that identifies entity mentions in a text corpus and associates them with an unambiguous identifier in KBs such as Wikipedia, BabelNet, DBpedia, Wikidata and YAGO. |
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A Dataset for Named Entity Recognition and Entity Linking in Chinese Historical Newspapers (2024.lrec-main)
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| Challenge: | a novel historical Chinese dataset is used for named entity recognition, entity linking and entity relations. |
| Approach: | They propose a historical Chinese dataset for named entity recognition, entity linking, coreference and entity relations . they use Chinese newspapers from 1872 to 1949 and multilingual bibliographic resources from the same period . |
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| Challenge: | This tutorial reviews the framework of cross-lingual EL and motivates it as a broad paradigm for the Information Extraction task. |
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MOLEMAN: Mention-Only Linking of Entities with a Mention Annotation Network (2021.acl-short)
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| Challenge: | Existing approaches to entity linking represent each entity with a single vector, but instead use a contextualized mention-encoder that learns to place similar mentions of the same entity closer in vector space than mentions from different entities. |
| Approach: | They propose an instance-based nearest neighbor approach to entity linking that allows for a contextualized mention-encoder to learn to place similar mentions of the same entity closer in vector space than mentions from different entities. |
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GenderQuant: Quantifying Mention-Level Genderedness (N19-1)
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| Challenge: | Existing approaches to detect gendered language require considerable annotation efforts for each language, domain, and author, and often require handcrafted lexicons and features. |
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| Challenge: | a new corpus for detecting and linking survey variables is being developed . the corpus is multilingual and includes manually curated word and phrase alignments . |
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