{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/named-entity-recognition-1/papers/16","list_of":"/task/named-entity-recognition-1","task":"Named Entity Recognition","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":16,"pages_in_order":26,"rows_per_page":100,"rows":[1501,1600],"of":2560,"counts":{"archive_papers_tagged":2560,"with_a_code_link":969,"where_syntology_ran_a_sample":118,"not_listed_spam_title":0,"listed":2560,"listed_where_code_ran":118,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":100,"every_run_a_failure_of_syntologys_instrument":18,"listed_with_a_run_with_no_instrument_failure":100,"listed_every_run_a_failure_of_syntologys_instrument":18,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/named-entity-recognition-1","prev":"/task/named-entity-recognition-1/papers/15","next":"/task/named-entity-recognition-1/papers/17","papers":[{"url":null,"slug":"infrrd-ai-at-semeval-2022-task-11-a-system","title":"Infrrd.ai at SemEval-2022 Task 11: A system for named entity recognition using data augmentation, transformer-based sequence labeling model, and EnsembleCRF","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kddie-at-semeval-2022-task-11-using-deberta","title":"KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-extraction-from-aeronautical","title":"Knowledge extraction from aeronautical messages (NOTAMs) with self-supervised language models for aircraft pilots","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"l3i-at-semeval-2022-task-11-straightforward","title":"L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lmn-at-semeval-2022-task-11-a-transformer-1","title":"LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ncuee-nlp-at-semeval-2022-task-11-chinese","title":"NCUEE-NLP at SemEval-2022 Task 11: Chinese Named Entity Recognition Using the BERT-BiLSTM-CRF Model","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"racai-at-semeval-2022-task-11-complex-named","title":"RACAI at SemEval-2022 Task 11: Complex named entity recognition using a lateral inhibition mechanism","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"raccoons-at-semeval-2022-task-11-leveraging","title":"Raccoons at SemEval-2022 Task 11: Leveraging Concatenated Word Embeddings for Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semeval-2022-task-11-multilingual-complex","title":"SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition (MultiCoNER)","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"seql-at-semeval-2022-task-11-an-ensemble-of","title":"SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"silpa-nlp-at-semeval-2022-tasks-11","title":"silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languages","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"team-dina-at-semeval-2022-task-8-pre-trained","title":"Team dina at SemEval-2022 Task 8: Pre-trained Language Models as Baselines for Semantic Similarity","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ua-ko-at-semeval-2022-task-11-data","title":"UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uc3m-pucpr-at-semeval-2022-task-11-an","title":"UC3M-PUCPR at SemEval-2022 Task 11: An Ensemble Method of Transformer-based Models for Complex Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-embedding-models-for-automatic","title":"Evaluation of Embedding Models for Automatic Extraction and Classification of Acknowledged Entities in Scientific Documents","date":"2022-06-22","arxiv_id":"2206.10939","repositories_listed":0,"syntology":null},{"url":null,"slug":"cmnerone-at-semeval-2022-task-11-code-mixed","title":"CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data","date":"2022-06-15","arxiv_id":"2206.07318","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextualization-and-generalization-in","title":"Contextualization and Generalization in Entity and Relation Extraction","date":"2022-06-15","arxiv_id":"2206.07558","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-experimental-investigation-of-part-of","title":"An Experimental Investigation of Part-Of-Speech Taggers for Vietnamese","date":"2022-06-14","arxiv_id":"2206.06992","repositories_listed":0,"syntology":null},{"url":null,"slug":"enriching-a-fashion-knowledge-graph-from","title":"Enriching a Fashion Knowledge Graph from Product Textual Descriptions","date":"2022-06-02","arxiv_id":"2206.01087","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-case-study-on-the-importance-of-named","title":"A Case Study on the Importance of Named Entities in a Machine Translation Pipeline for Customer Support Content","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-warm-start-and-a-clean-crawled-corpus-a-1","title":"A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"asner-annotated-dataset-and-baseline-for-1","title":"AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"corefud-1-0-coreference-meets-universal","title":"CorefUD 1.0: Coreference Meets Universal Dependencies","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-clinic-de-identification-of-swedish","title":"Cross-Clinic De-Identification of Swedish Electronic Health Records: Nuances and Caveats","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"distant-reading-in-digital-humanities-case","title":"Distant Reading in Digital Humanities: Case Study on the Serbian Part of the ELTeC Collection","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"from-eltec-text-collection-metadata-and-named","title":"From ELTeC Text Collection Metadata and Named Entities to Linked-data (and Back)","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mapa-project-ready-to-go-open-source-datasets","title":"MAPA Project: Ready-to-Go Open-Source Datasets and Deep Learning Technology to Remove Identifying Information from Text Documents","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-named-entity-recognition-for","title":"Multilingual Named Entity Recognition for Medieval Charters Using Stacked Embeddings and Bert-based Models.","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-recognition-to-detect-criminal","title":"Named Entity Recognition to Detect Criminal Texts on the Web","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pre-training-and-evaluating-transformer-based","title":"Pre-training and Evaluating Transformer-based Language Models for Icelandic","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"remplacement-de-mentions-pour-ladaptation-dun","title":"Remplacement de mentions pour l’adaptation d’un corpus de reconnaissance d’entités nommées à un domaine cible (Mention replacement for adapting a named entity recognition dataset to a target domain)","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"strategies-dadaptation-pour-la-reconnaissance","title":"Stratégies d’adaptation pour la reconnaissance d’entités médicales en français (Adaptation strategies for biomedical named entity recognition in French)","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-sentence-level-classification-helps","title":"Using Sentence-level Classification Helps Entity Extraction from Material Science Literature","date":"2022-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sfe-ai-at-semeval-2022-task-11-low-resource","title":"SFE-AI at SemEval-2022 Task 11: Low-Resource Named Entity Recognition using Large Pre-trained Language Models","date":"2022-05-29","arxiv_id":"2205.14660","repositories_listed":0,"syntology":null},{"url":null,"slug":"grammar-detection-for-sentiment-analysis","title":"Grammar Detection for Sentiment Analysis through Improved Viterbi Algorithm","date":"2022-05-26","arxiv_id":"2205.13148","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-natural-language-processing-pipeline-for","title":"A Natural Language Processing Pipeline for Detecting Informal Data References in Academic Literature","date":"2022-05-23","arxiv_id":"2205.11651","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-reproducible-experimental-survey-on","title":"A reproducible experimental survey on biomedical sentence similarity: a string-based method sets the state of the art","date":"2022-05-18","arxiv_id":"2205.08740","repositories_listed":0,"syntology":null},{"url":"/paper/topic-segmentation-of-research-article","slug":"topic-segmentation-of-research-article","title":"Topic Segmentation of Research Article Collections","date":"2022-05-18","arxiv_id":"2205.11249","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-aggregation-in-zero-shot-cross","title":"Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERT","date":"2022-05-17","arxiv_id":"2205.08497","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-open-arabic-named-entity","title":"Comparing Open Arabic Named Entity Recognition Tools","date":"2022-05-12","arxiv_id":"2205.05857","repositories_listed":0,"syntology":null},{"url":null,"slug":"lifting-the-curse-of-multilinguality-by-pre-1","title":"Lifting the Curse of Multilinguality by Pre-training Modular Transformers","date":"2022-05-12","arxiv_id":"2205.06266","repositories_listed":0,"syntology":null},{"url":null,"slug":"ner-mqmrc-formulating-named-entity","title":"NER-MQMRC: Formulating Named Entity Recognition as Multi Question Machine Reading Comprehension","date":"2022-05-12","arxiv_id":"2205.05904","repositories_listed":0,"syntology":null},{"url":"/paper/biographical-a-semi-supervised-relation","slug":"biographical-a-semi-supervised-relation","title":"Biographical: A Semi-Supervised Relation Extraction Dataset","date":"2022-05-02","arxiv_id":"2205.00806","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-unsupervised-multiple-task-and-multiple-1","title":"An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"are-you-a-hero-or-a-villain-a-semantic-role","title":"Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes.","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"auxiliary-learning-for-named-entity","title":"Auxiliary Learning for Named Entity Recognition with Multiple Auxiliary Biomedical Training Data","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/biomedical-ner-using-novel-schema-and-distant","slug":"biomedical-ner-using-novel-schema-and-distant","title":"Biomedical NER using Novel Schema and Distant Supervision","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"de-bias-for-generative-extraction-in-unified","title":"De-Bias for Generative Extraction in Unified NER Task","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extract-select-a-span-selection-framework-for","title":"Extract-Select: A Span Selection Framework for Nested Named Entity Recognition with Generative Adversarial Training","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-person-names-from-user-generated","title":"Extracting Person Names from User Generated Text: Named-Entity Recognition for Combating Human Trafficking","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extreme-multi-label-classification-with-label","title":"Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-class-incremental-learning-for-named","title":"Few-Shot Class-Incremental Learning for Named Entity Recognition","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-romanian-bioner-using-a","title":"Improving Romanian BioNER Using a Biologically Inspired System","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-and-review-enhancing-continual-named","title":"Learn and Review: Enhancing Continual Named Entity Recognition via Reviewing Synthetic Samples","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-recognition-for-cancer","title":"Named Entity Recognition for Cancer Immunology Research Using Distant Supervision","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nested-named-entity-recognition-with-span","title":"Nested Named Entity Recognition with Span-level Graphs","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-semantic-based-data-augmentation-for","title":"Simple Semantic-based Data Augmentation for Named Entity Recognition in Biomedical Texts","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"teluguner-leveraging-multi-domain-named","title":"TeluguNER: Leveraging Multi-Domain Named Entity Recognition with Deep Transformers","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-fine-grained-classification-of","title":"Towards Fine-grained Classification of Climate Change related Social Media Text","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-do-we-really-know-about-state-of-the-art","title":"What do we Really Know about State of the Art NER?","date":"2022-04-29","arxiv_id":"2205.00034","repositories_listed":0,"syntology":null},{"url":null,"slug":"um6p-cs-at-semeval-2022-task-11-enhancing","title":"UM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer","date":"2022-04-28","arxiv_id":"2204.13515","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-hypergraph-based-nested-named-entity","title":"Local Hypergraph-based Nested Named Entity Recognition as Query-based Sequence Labeling","date":"2022-04-25","arxiv_id":"2204.11467","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-for-medical-text-a","title":"Few-shot learning for medical text: A systematic review","date":"2022-04-21","arxiv_id":"2204.14081","repositories_listed":0,"syntology":null},{"url":null,"slug":"recovering-patient-journeys-a-corpus-of","title":"Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)","date":"2022-04-21","arxiv_id":"2204.09952","repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-recognition-for-partially","title":"Named Entity Recognition for Partially Annotated Datasets","date":"2022-04-19","arxiv_id":"2204.09081","repositories_listed":0,"syntology":null},{"url":null,"slug":"nested-named-entity-recognition-as-holistic","title":"Nested Named Entity Recognition as Holistic Structure Parsing","date":"2022-04-17","arxiv_id":"2204.08006","repositories_listed":0,"syntology":null},{"url":null,"slug":"qtrade-ai-at-semeval-2022-task-11-an-unified","title":"Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual NER Task","date":"2022-04-14","arxiv_id":"2204.07459","repositories_listed":0,"syntology":null},{"url":null,"slug":"ehrkit-a-python-natural-language-processing","title":"EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts","date":"2022-04-13","arxiv_id":"2204.06604","repositories_listed":0,"syntology":null},{"url":null,"slug":"delving-deep-into-regularity-a-simple-but","title":"Delving Deep into Regularity: A Simple but Effective Method for Chinese Named Entity Recognition","date":"2022-04-12","arxiv_id":"2204.05544","repositories_listed":0,"syntology":null},{"url":null,"slug":"solving-price-per-unit-problem-around-the","title":"Solving Price Per Unit Problem Around the World: Formulating Fact Extraction as Question Answering","date":"2022-04-12","arxiv_id":"2204.05555","repositories_listed":0,"syntology":null},{"url":null,"slug":"trigger-gnn-a-trigger-based-graph-neural","title":"Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition","date":"2022-04-12","arxiv_id":"2204.05518","repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-character-are-subwords-good-enough-1","title":"Breaking Character: Are Subwords Good Enough for MRLs After All?","date":"2022-04-10","arxiv_id":"2204.04748","repositories_listed":0,"syntology":null},{"url":null,"slug":"lamner-code-comment-generation-using","title":"LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition","date":"2022-04-05","arxiv_id":"2204.09654","repositories_listed":0,"syntology":null},{"url":"/paper/multi-view-approach-to-suggest-moderation","slug":"multi-view-approach-to-suggest-moderation","title":"Multi-View Approach to Suggest Moderation Actions in Community Question Answering Sites","date":"2022-04-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"product-market-demand-analysis-using-nlp-in","title":"Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition","date":"2022-04-04","arxiv_id":"2204.01827","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-dual-contrastive-framework-for-low-resource","title":"A Dual-Contrastive Framework for Low-Resource Cross-Lingual Named Entity Recognition","date":"2022-04-02","arxiv_id":"2204.00796","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-model-for-named-entity-recognition","title":"End-to-end model for named entity recognition from speech without paired training data","date":"2022-04-02","arxiv_id":"2204.00803","repositories_listed":0,"syntology":null},{"url":null,"slug":"scientific-and-technological-text-knowledge","title":"Scientific and Technological Text Knowledge Extraction Method of based on Word Mixing and GRU","date":"2022-03-31","arxiv_id":"2203.17079","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-named-entity-recognition","title":"Federated Named Entity Recognition","date":"2022-03-28","arxiv_id":"2203.15101","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-domain-knowledge-for-low-resource-named","title":"Using Domain Knowledge for Low Resource Named Entity Recognition","date":"2022-03-28","arxiv_id":"2203.14738","repositories_listed":0,"syntology":null},{"url":null,"slug":"mono-vs-multilingual-bert-a-case-study-in","title":"Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition","date":"2022-03-24","arxiv_id":"2203.12907","repositories_listed":0,"syntology":null},{"url":null,"slug":"su-nlp-at-semeval-2022-task-11-complex-named","title":"SU-NLP at SemEval-2022 Task 11: Complex Named Entity Recognition with Entity Linking","date":"2022-03-22","arxiv_id":"2203.11841","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-intellectual-property-entity-recognition","title":"An Intellectual Property Entity Recognition Method Based on Transformer and Technological Word Information","date":"2022-03-21","arxiv_id":"2203.10717","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-token-segmentation-for-high-token","title":"Neural Token Segmentation for High Token-Internal Complexity","date":"2022-03-21","arxiv_id":"2203.10845","repositories_listed":0,"syntology":null},{"url":null,"slug":"ulyssesner-br-a-corpus-of-brazilian","title":"UlyssesNER-Br: A Corpus of Brazilian Legislative Documents for Named Entity Recognition","date":"2022-03-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wcl-bbcd-a-contrastive-learning-and-knowledge","title":"WCL-BBCD: A Contrastive Learning and Knowledge Graph Approach to Named Entity Recognition","date":"2022-03-14","arxiv_id":"2203.06925","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-in-adversarial-defences-and","title":"A Survey of Adversarial Defences and Robustness in NLP","date":"2022-03-12","arxiv_id":"2203.06414","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-neural-networks-for-fine-grained","title":"Deep neural networks for fine-grained surveillance of overdose mortality","date":"2022-02-25","arxiv_id":"2202.12448","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-effective-multi-task-interaction-for","title":"Towards Effective Multi-Task Interaction for Entity-Relation Extraction: A Unified Framework with Selection Recurrent Network","date":"2022-02-15","arxiv_id":"2202.07281","repositories_listed":0,"syntology":null},{"url":null,"slug":"lmn-at-semeval-2022-task-11-a-transformer","title":"LMN at SemEval-2022 Task 11: A Transformer-based System for English Named Entity Recognition","date":"2022-02-13","arxiv_id":"2203.03546","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-two-step-approach-to-leverage-contextual","title":"A two-step approach to leverage contextual data: speech recognition in air-traffic communications","date":"2022-02-08","arxiv_id":"2202.03725","repositories_listed":0,"syntology":null},{"url":null,"slug":"rnn-transducers-for-nested-named-entity","title":"RNN Transducers for Nested Named Entity Recognition with constraints on alignment for long sequences","date":"2022-02-08","arxiv_id":"2203.03543","repositories_listed":0,"syntology":null},{"url":null,"slug":"distantly-supervised-end-to-end-medical-1","title":"Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality","date":"2022-01-25","arxiv_id":"2201.10463","repositories_listed":0,"syntology":null},{"url":null,"slug":"btpk-based-learning-an-interpretable-method","title":"BTPK-based interpretable method for NER tasks based on Talmudic Public Announcement Logic","date":"2022-01-24","arxiv_id":"2201.09523","repositories_listed":0,"syntology":null},{"url":null,"slug":"razmecheno-named-entity-recognition-from","title":"Razmecheno: Named Entity Recognition from Digital Archive of Diaries \"Prozhito\"","date":"2022-01-24","arxiv_id":"2201.09997","repositories_listed":0,"syntology":null},{"url":null,"slug":"legal-entity-extraction-using-a-pointer","title":"Legal Entity Extraction using a Pointer Generator Network","date":"2022-01-20","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-representation-training","title":"An Empirical Study of Representation, Training and Decoding for Span-based Named Entity Recognition","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"breaking-character-are-subwords-good-enough","title":"Breaking Character: Are Subwords Good Enough for MRLs After All?","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-distillation-for-language-models-1","title":"Causal Distillation for Language Models","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"disarm-detecting-the-victims-targeted-by","title":"DISARM: Detecting the Victims Targeted by Harmful Memes","date":"2022-01-16","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"33b1541fb9a93620764ca7c72fa1b192aa4086f763929c4b9e8d86fc939190b9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}