{"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-ner/papers/5","list_of":"/task/named-entity-recognition-ner","task":"Named Entity Recognition (NER)","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":5,"pages_in_order":29,"rows_per_page":100,"rows":[401,500],"of":2874,"counts":{"archive_papers_tagged":2874,"with_a_code_link":955,"where_syntology_ran_a_sample":119,"not_listed_spam_title":0,"listed":2874,"listed_where_code_ran":119,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":99,"every_run_a_failure_of_syntologys_instrument":20,"listed_with_a_run_with_no_instrument_failure":99,"listed_every_run_a_failure_of_syntologys_instrument":20,"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-ner","prev":"/task/named-entity-recognition-ner/papers/4","next":"/task/named-entity-recognition-ner/papers/6","papers":[{"url":"/paper/end-to-end-chinese-speaker-identification","slug":"end-to-end-chinese-speaker-identification","title":"End-to-End Chinese Speaker Identification","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/good-visual-guidance-make-a-better-extractor","slug":"good-visual-guidance-make-a-better-extractor","title":"Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/label-refinement-via-contrastive-learning-for","slug":"label-refinement-via-contrastive-learning-for","title":"Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/marsan-at-semeval-2022-task-11-multilingual","slug":"marsan-at-semeval-2022-task-11-multilingual","title":"MarSan at SemEval-2022 Task 11: Multilingual complex named entity recognition using T5 and transformer encoder","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multi-features-based-semantic-augmentation","slug":"multi-features-based-semantic-augmentation","title":"Multi-features based Semantic Augmentation Networks for Named Entity Recognition in Threat Intelligence","date":"2022-07-01","arxiv_id":"2207.00232","repositories_listed":1,"syntology":null},{"url":"/paper/multinerd-a-multilingual-multi-genre-and-fine","slug":"multinerd-a-multilingual-multi-genre-and-fine","title":"MultiNERD: A Multilingual, Multi-Genre and Fine-Grained Dataset for Named Entity Recognition (and Disambiguation)","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/namedentityrangers-at-semeval-2022-task-11","slug":"namedentityrangers-at-semeval-2022-task-11","title":"NamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ner4id-at-semeval-2022-task-2-named-entity","slug":"ner4id-at-semeval-2022-task-2-named-entity","title":"NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pai-at-semeval-2022-task-11-name-entity","slug":"pai-at-semeval-2022-task-11-name-entity","title":"PAI at SemEval-2022 Task 11: Name Entity Recognition with Contextualized Entity Representations and Robust Loss Functions","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pathway2text-dataset-and-method-for-1","slug":"pathway2text-dataset-and-method-for-1","title":"Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/resilience-of-named-entity-recognition-models","slug":"resilience-of-named-entity-recognition-models","title":"Resilience of Named Entity Recognition Models under Adversarial Attack","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sentence-level-resampling-for-named-entity-1","slug":"sentence-level-resampling-for-named-entity-1","title":"Sentence-Level Resampling for Named Entity Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/turkishdelightnlp-a-neural-turkish-nlp","slug":"turkishdelightnlp-a-neural-turkish-nlp","title":"TurkishDelightNLP: A Neural Turkish NLP Toolkit","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-cross-lingual-transfer-with","slug":"uncertainty-aware-cross-lingual-transfer-with","title":"Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gernermed-transfer-learning-in-german-medical","slug":"gernermed-transfer-learning-in-german-medical","title":"GERNERMED++: Transfer Learning in German Medical NLP","date":"2022-06-29","arxiv_id":"2206.14504","repositories_listed":1,"syntology":null},{"url":"/paper/tweetnlp-cutting-edge-natural-language","slug":"tweetnlp-cutting-edge-natural-language","title":"TweetNLP: Cutting-Edge Natural Language Processing for Social Media","date":"2022-06-29","arxiv_id":"2206.14774","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/tweetnlp-cutting-edge-natural-language#ran","syntology_url":"https://syntology.ai/paper/2206.14774","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.14774"}},"official":{"repos":["cardiffnlp/tweetnlp"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/nerda-con-extending-ner-models-for-continual","slug":"nerda-con-extending-ner-models-for-continual","title":"NERDA-Con: Extending NER models for Continual Learning -- Integrating Distinct Tasks and Updating Distribution Shifts","date":"2022-06-28","arxiv_id":"2206.14607","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/nerda-con-extending-ner-models-for-continual#ran","syntology_url":"https://syntology.ai/paper/2206.14607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.14607"}},"official":{"repos":["supritivijay/nerda-con"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/endowing-language-models-with-multimodal","slug":"endowing-language-models-with-multimodal","title":"Endowing Language Models with Multimodal Knowledge Graph Representations","date":"2022-06-27","arxiv_id":"2206.13163","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-self-attention-for-language","slug":"adversarial-self-attention-for-language","title":"Adversarial Self-Attention for Language Understanding","date":"2022-06-25","arxiv_id":"2206.12608","repositories_listed":1,"syntology":null},{"url":"/paper/token-is-a-mask-few-shot-named-entity","slug":"token-is-a-mask-few-shot-named-entity","title":"TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models","date":"2022-06-15","arxiv_id":"2206.07841","repositories_listed":1,"syntology":null},{"url":"/paper/sscibert-a-pre-trained-language-model-for","slug":"sscibert-a-pre-trained-language-model-for","title":"SsciBERT: A Pre-trained Language Model for Social Science Texts","date":"2022-06-09","arxiv_id":"2206.04510","repositories_listed":1,"syntology":null},{"url":"/paper/scideberta-learning-deberta-for-science","slug":"scideberta-learning-deberta-for-science","title":"SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction Tasks","date":"2022-06-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-named-entity-recognition-corpus-for","slug":"a-named-entity-recognition-corpus-for","title":"A Named Entity Recognition Corpus for Vietnamese Biomedical Texts to Support Tuberculosis Treatment","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-twitter-corpus-for-named-entity-recognition","slug":"a-twitter-corpus-for-named-entity-recognition","title":"A Twitter Corpus for Named Entity Recognition in Turkish","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/chimst-a-chinese-medical-corpus-for-word","slug":"chimst-a-chinese-medical-corpus-for-word","title":"ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/comparing-annotated-datasets-for-named-entity","slug":"comparing-annotated-datasets-for-named-entity","title":"Comparing Annotated Datasets for Named Entity Recognition in English Literature","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/enhanced-entity-annotations-for-multilingual","slug":"enhanced-entity-annotations-for-multilingual","title":"Enhanced Entity Annotations for Multilingual Corpora","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-deep-learning-with-embedded","slug":"enhancing-deep-learning-with-embedded","title":"Enhancing Deep Learning with Embedded Features for Arabic Named Entity Recognition","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-relation-extraction-via-adversarial","slug":"enhancing-relation-extraction-via-adversarial","title":"Enhancing Relation Extraction via Adversarial Multi-task Learning","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fenec-un-corpus-equilibre-pour-levaluation","slug":"fenec-un-corpus-equilibre-pour-levaluation","title":"FENEC : un corpus équilibré pour l’évaluation des entités nommées en français (FENEC : a balanced sample corpus for French named entity recognition )","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ggponc-2-0-the-german-clinical-guideline","slug":"ggponc-2-0-the-german-clinical-guideline","title":"GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/igbobert-models-building-and-training","slug":"igbobert-models-building-and-training","title":"IgboBERT Models: Building and Training Transformer Models for the Igbo Language","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/named-entity-recognition-in-estonian-19th","slug":"named-entity-recognition-in-estonian-19th","title":"Named Entity Recognition in Estonian 19th Century Parish Court Records","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/russian-jeopardy-data-set-for-question","slug":"russian-jeopardy-data-set-for-question","title":"Russian Jeopardy! Data Set for Question-Answering Systems","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/specnfs-a-challenge-dataset-towards","slug":"specnfs-a-challenge-dataset-towards","title":"SpecNFS: A Challenge Dataset Towards Extracting Formal Models from Natural Language Specifications","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/finbert-mrc-financial-named-entity","slug":"finbert-mrc-financial-named-entity","title":"FinBERT-MRC: financial named entity recognition using BERT under the machine reading comprehension paradigm","date":"2022-05-31","arxiv_id":"2205.15485","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/finbert-mrc-financial-named-entity#ran","syntology_url":"https://syntology.ai/paper/2205.15485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15485"}},"official":{"repos":["zyz0000/finbert-mrc"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/hmbert-historical-multilingual-language","slug":"hmbert-historical-multilingual-language","title":"hmBERT: Historical Multilingual Language Models for Named Entity Recognition","date":"2022-05-31","arxiv_id":"2205.15575","repositories_listed":1,"syntology":null},{"url":"/paper/l3cube-mahanlp-marathi-natural-language","slug":"l3cube-mahanlp-marathi-natural-language","title":"L3Cube-MahaNLP: Marathi Natural Language Processing Datasets, Models, and Library","date":"2022-05-29","arxiv_id":"2205.14728","repositories_listed":1,"syntology":null},{"url":"/paper/sparse-conditional-hidden-markov-model-for","slug":"sparse-conditional-hidden-markov-model-for","title":"Sparse Conditional Hidden Markov Model for Weakly Supervised Named Entity Recognition","date":"2022-05-27","arxiv_id":"2205.14228","repositories_listed":1,"syntology":null},{"url":"/paper/babybear-cheap-inference-triage-for-expensive","slug":"babybear-cheap-inference-triage-for-expensive","title":"BabyBear: Cheap inference triage for expensive language models","date":"2022-05-24","arxiv_id":"2205.11747","repositories_listed":1,"syntology":null},{"url":"/paper/formulating-few-shot-fine-tuning-towards","slug":"formulating-few-shot-fine-tuning-towards","title":"Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition","date":"2022-05-24","arxiv_id":"2205.11799","repositories_listed":1,"syntology":null},{"url":"/paper/runne-2022-shared-task-recognizing-nested","slug":"runne-2022-shared-task-recognizing-nested","title":"RuNNE-2022 Shared Task: Recognizing Nested Named Entities","date":"2022-05-23","arxiv_id":"2205.11159","repositories_listed":1,"syntology":null},{"url":"/paper/deepstruct-pretraining-of-language-models-for-1","slug":"deepstruct-pretraining-of-language-models-for-1","title":"DeepStruct: Pretraining of Language Models for Structure Prediction","date":"2022-05-21","arxiv_id":"2205.10475","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":9,"n_pointer_only":3,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/deepstruct-pretraining-of-language-models-for-1#ran","syntology_url":"https://syntology.ai/paper/2205.10475","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.10475"}},"official":{"repos":["cgraywang/deepstruct"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pre-training-data-quality-and-quantity-for-a","slug":"pre-training-data-quality-and-quantity-for-a","title":"Pre-training Data Quality and Quantity for a Low-Resource Language: New Corpus and BERT Models for Maltese","date":"2022-05-21","arxiv_id":"2205.10517","repositories_listed":1,"syntology":null},{"url":"/paper/named-entity-recognition-multi-task-learning","slug":"named-entity-recognition-multi-task-learning","title":"Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT","date":"2022-05-19","arxiv_id":"2205.09651","repositories_listed":1,"syntology":null},{"url":"/paper/hero-gang-neural-model-for-named-entity","slug":"hero-gang-neural-model-for-named-entity","title":"Hero-Gang Neural Model For Named Entity Recognition","date":"2022-05-15","arxiv_id":"2205.07177","repositories_listed":1,"syntology":null},{"url":"/paper/vit5-pretrained-text-to-text-transformer-for","slug":"vit5-pretrained-text-to-text-transformer-for","title":"ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation","date":"2022-05-13","arxiv_id":"2205.06457","repositories_listed":1,"syntology":null},{"url":"/paper/nflat-non-flat-lattice-transformer-for","slug":"nflat-non-flat-lattice-transformer-for","title":"NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition","date":"2022-05-12","arxiv_id":"2205.05832","repositories_listed":1,"syntology":null},{"url":"/paper/disarm-detecting-the-victims-targeted-by-1","slug":"disarm-detecting-the-victims-targeted-by-1","title":"DISARM: Detecting the Victims Targeted by Harmful Memes","date":"2022-05-11","arxiv_id":"2205.05738","repositories_listed":1,"syntology":null},{"url":"/paper/ontology-based-and-weakly-supervised-rare","slug":"ontology-based-and-weakly-supervised-rare","title":"Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes","date":"2022-05-11","arxiv_id":"2205.05656","repositories_listed":1,"syntology":null},{"url":"/paper/good-visual-guidance-makes-a-better-extractor","slug":"good-visual-guidance-makes-a-better-extractor","title":"Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction","date":"2022-05-07","arxiv_id":"2205.03521","repositories_listed":1,"syntology":null},{"url":"/paper/wav2seq-pre-training-speech-to-text-encoder","slug":"wav2seq-pre-training-speech-to-text-encoder","title":"Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages","date":"2022-05-02","arxiv_id":"2205.01086","repositories_listed":1,"syntology":null},{"url":"/paper/distant-cto-a-zero-cost-distantly-supervised","slug":"distant-cto-a-zero-cost-distantly-supervised","title":"DISTANT-CTO: A Zero Cost, Distantly Supervised Approach to Improve Low-Resource Entity Extraction Using Clinical Trials Literature","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gnner-reducing-overlapping-in-span-based-ner","slug":"gnner-reducing-overlapping-in-span-based-ner","title":"GNNer: Reducing Overlapping in Span-based NER Using Graph Neural Networks","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/phone-ing-it-in-towards-flexible-multi-modal-1","slug":"phone-ing-it-in-towards-flexible-multi-modal-1","title":"Phone-ing it in: Towards Flexible Multi-Modal Language Model Training by Phonetic Representations of Data","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/thai-nested-named-entity-recognition-corpus","slug":"thai-nested-named-entity-recognition-corpus","title":"Thai Nested Named Entity Recognition Corpus","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-estimation-of-transformer","slug":"uncertainty-estimation-of-transformer","title":"Uncertainty Estimation of Transformer Predictions for Misclassification Detection","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/polyglot-prompt-multilingual-multitask","slug":"polyglot-prompt-multilingual-multitask","title":"Polyglot Prompt: Multilingual Multitask PrompTraining","date":"2022-04-29","arxiv_id":"2204.14264","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/polyglot-prompt-multilingual-multitask#ran","syntology_url":"https://syntology.ai/paper/2204.14264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.14264"}},"official":{"repos":["jinlanfu/polyglot_prompt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hiner-a-large-hindi-named-entity-recognition","slug":"hiner-a-large-hindi-named-entity-recognition","title":"HiNER: A Large Hindi Named Entity Recognition Dataset","date":"2022-04-28","arxiv_id":"2204.13743","repositories_listed":1,"syntology":null},{"url":"/paper/propose-and-refine-a-two-stage-set-prediction","slug":"propose-and-refine-a-two-stage-set-prediction","title":"Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition","date":"2022-04-27","arxiv_id":"2204.12732","repositories_listed":1,"syntology":null},{"url":"/paper/boundary-smoothing-for-named-entity-1","slug":"boundary-smoothing-for-named-entity-1","title":"Boundary Smoothing for Named Entity Recognition","date":"2022-04-26","arxiv_id":"2204.12031","repositories_listed":1,"syntology":null},{"url":"/paper/self-augmentation-for-named-entity","slug":"self-augmentation-for-named-entity","title":"Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting","date":"2022-04-25","arxiv_id":"2204.11406","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/self-augmentation-for-named-entity#ran","syntology_url":"https://syntology.ai/paper/2204.11406","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.11406"}},"official":{"repos":["LindgeW/MetaAug4NER"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/kala-knowledge-augmented-language-model-1","slug":"kala-knowledge-augmented-language-model-1","title":"KALA: Knowledge-Augmented Language Model Adaptation","date":"2022-04-22","arxiv_id":"2204.10555","repositories_listed":1,"syntology":{"n":16,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":14,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 14 unverified","sample_list":"/paper/kala-knowledge-augmented-language-model-1#ran","syntology_url":"https://syntology.ai/paper/2204.10555","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.10555"}},"official":{"repos":["nardien/kala"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/a-benchmark-for-automatic-medical","slug":"a-benchmark-for-automatic-medical","title":"A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets","date":"2022-04-19","arxiv_id":"2204.08997","repositories_listed":1,"syntology":null},{"url":"/paper/tasteset-recipe-dataset-and-food-entities","slug":"tasteset-recipe-dataset-and-food-entities","title":"TASTEset -- Recipe Dataset and Food Entities Recognition Benchmark","date":"2022-04-16","arxiv_id":"2204.07775","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/tasteset-recipe-dataset-and-food-entities#ran","syntology_url":"https://syntology.ai/paper/2204.07775","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07775"}},"official":{"repos":["taisti/tasteset"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/decomposed-meta-learning-for-few-shot-named","slug":"decomposed-meta-learning-for-few-shot-named","title":"Decomposed Meta-Learning for Few-Shot Named Entity Recognition","date":"2022-04-12","arxiv_id":"2204.05751","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/decomposed-meta-learning-for-few-shot-named#ran","syntology_url":"https://syntology.ai/paper/2204.05751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.05751"}},"official":{"repos":["microsoft/vert-papers"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/l3cube-mahaner-a-marathi-named-entity","slug":"l3cube-mahaner-a-marathi-named-entity","title":"L3Cube-MahaNER: A Marathi Named Entity Recognition Dataset and BERT models","date":"2022-04-12","arxiv_id":"2204.06029","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparative-study-of-pre-trained-encoders-1","slug":"a-comparative-study-of-pre-trained-encoders-1","title":"A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition","date":"2022-04-11","arxiv_id":"2204.04980","repositories_listed":1,"syntology":null},{"url":"/paper/doctor-xavier-explainable-diagnosis-using","slug":"doctor-xavier-explainable-diagnosis-using","title":"Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI Evaluation","date":"2022-04-11","arxiv_id":"2204.10178","repositories_listed":1,"syntology":null},{"url":"/paper/entities-dates-and-languages-zero-shot-on","slug":"entities-dates-and-languages-zero-shot-on","title":"Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0","date":"2022-04-11","arxiv_id":"2204.05211","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-cross-lingual-transfer-for-coarse","slug":"few-shot-cross-lingual-transfer-for-coarse","title":"Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts","date":"2022-04-10","arxiv_id":"2204.04775","repositories_listed":1,"syntology":null},{"url":"/paper/biobart-pretraining-and-evaluation-of-a","slug":"biobart-pretraining-and-evaluation-of-a","title":"BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model","date":"2022-04-08","arxiv_id":"2204.03905","repositories_listed":1,"syntology":null},{"url":"/paper/biored-a-comprehensive-biomedical-relation","slug":"biored-a-comprehensive-biomedical-relation","title":"BioRED: A Rich Biomedical Relation Extraction Dataset","date":"2022-04-08","arxiv_id":"2204.04263","repositories_listed":1,"syntology":null},{"url":"/paper/cyner-a-python-library-for-cybersecurity","slug":"cyner-a-python-library-for-cybersecurity","title":"CyNER: A Python Library for Cybersecurity Named Entity Recognition","date":"2022-04-08","arxiv_id":"2204.05754","repositories_listed":1,"syntology":null},{"url":"/paper/rubioroberta-a-pre-trained-biomedical","slug":"rubioroberta-a-pre-trained-biomedical","title":"RuBioRoBERTa: a pre-trained biomedical language model for Russian language biomedical text mining","date":"2022-04-08","arxiv_id":"2204.03951","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-extraction-of-nested-entities-in","slug":"automatic-extraction-of-nested-entities-in","title":"Automatic Extraction of Nested Entities in Clinical Referrals in Spanish","date":"2022-04-07","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/extracting-impact-model-narratives-from","slug":"extracting-impact-model-narratives-from","title":"Extracting Impact Model Narratives from Social Services' Text","date":"2022-04-04","arxiv_id":"2204.09557","repositories_listed":1,"syntology":null},{"url":"/paper/k-nn-ner-named-entity-recognition-with","slug":"k-nn-ner-named-entity-recognition-with","title":"$k$NN-NER: Named Entity Recognition with Nearest Neighbor Search","date":"2022-03-31","arxiv_id":"2203.17103","repositories_listed":1,"syntology":null},{"url":"/paper/linkbert-pretraining-language-models-with","slug":"linkbert-pretraining-language-models-with","title":"LinkBERT: Pretraining Language Models with Document Links","date":"2022-03-29","arxiv_id":"2203.15827","repositories_listed":1,"syntology":{"n":14,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/linkbert-pretraining-language-models-with#ran","syntology_url":"https://syntology.ai/paper/2203.15827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15827"}},"official":{"repos":["michiyasunaga/LinkBERT"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/computer-science-named-entity-recognition-in","slug":"computer-science-named-entity-recognition-in","title":"Computer Science Named Entity Recognition in the Open Research Knowledge Graph","date":"2022-03-28","arxiv_id":"2203.14579","repositories_listed":1,"syntology":null},{"url":"/paper/hierarchical-transformer-model-for-scientific","slug":"hierarchical-transformer-model-for-scientific","title":"Hierarchical Transformer Model for Scientific Named Entity Recognition","date":"2022-03-28","arxiv_id":"2203.14710","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-named-entity-recognition-with-self","slug":"few-shot-named-entity-recognition-with-self","title":"Few-shot Named Entity Recognition with Self-describing Networks","date":"2022-03-23","arxiv_id":"2203.12252","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-expert-guided-adversarial","slug":"leveraging-expert-guided-adversarial","title":"Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition","date":"2022-03-21","arxiv_id":"2203.10693","repositories_listed":1,"syntology":null},{"url":"/paper/parallel-instance-query-network-for-named","slug":"parallel-instance-query-network-for-named","title":"Parallel Instance Query Network for Named Entity Recognition","date":"2022-03-20","arxiv_id":"2203.10545","repositories_listed":1,"syntology":null},{"url":"/paper/radiology-text-analysis-system-radtext","slug":"radiology-text-analysis-system-radtext","title":"Radiology Text Analysis System (RadText): Architecture and Evaluation","date":"2022-03-19","arxiv_id":"2204.09599","repositories_listed":1,"syntology":null},{"url":"/paper/kinyabert-a-morphology-aware-kinyarwanda-1","slug":"kinyabert-a-morphology-aware-kinyarwanda-1","title":"KinyaBERT: a Morphology-aware Kinyarwanda Language Model","date":"2022-03-16","arxiv_id":"2203.08459","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":2,"n_ran_checked":2,"n_instrument":4,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"6 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/kinyabert-a-morphology-aware-kinyarwanda-1#ran","syntology_url":"https://syntology.ai/paper/2203.08459","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.08459"}},"official":{"repos":["anzeyimana/kinyabert-acl2022"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/label-semantics-for-few-shot-named-entity","slug":"label-semantics-for-few-shot-named-entity","title":"Label Semantics for Few Shot Named Entity Recognition","date":"2022-03-16","arxiv_id":"2203.08985","repositories_listed":1,"syntology":null},{"url":"/paper/thinking-about-gpt-3-in-context-learning-for","slug":"thinking-about-gpt-3-in-context-learning-for","title":"Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again","date":"2022-03-16","arxiv_id":"2203.08410","repositories_listed":1,"syntology":null},{"url":"/paper/markbert-marking-word-boundaries-improves-1","slug":"markbert-marking-word-boundaries-improves-1","title":"MarkBERT: Marking Word Boundaries Improves Chinese BERT","date":"2022-03-12","arxiv_id":"2203.06378","repositories_listed":1,"syntology":null},{"url":"/paper/nested-named-entity-recognition-as-latent-1","slug":"nested-named-entity-recognition-as-latent-1","title":"Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing","date":"2022-03-09","arxiv_id":"2203.04665","repositories_listed":1,"syntology":null},{"url":"/paper/pretrained-domain-specific-language-model-for","slug":"pretrained-domain-specific-language-model-for","title":"Pretrained Domain-Specific Language Model for General Information Retrieval Tasks in the AEC Domain","date":"2022-03-09","arxiv_id":"2203.04729","repositories_listed":1,"syntology":null},{"url":"/paper/instructionner-a-multi-task-instruction-based","slug":"instructionner-a-multi-task-instruction-based","title":"InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NER","date":"2022-03-08","arxiv_id":"2203.03903","repositories_listed":1,"syntology":null},{"url":"/paper/ustc-nelslip-at-semeval-2022-task-11","slug":"ustc-nelslip-at-semeval-2022-task-11","title":"USTC-NELSLIP at SemEval-2022 Task 11: Gazetteer-Adapted Integration Network for Multilingual Complex Named Entity Recognition","date":"2022-03-07","arxiv_id":"2203.03216","repositories_listed":1,"syntology":null},{"url":"/paper/distantly-supervised-named-entity-recognition-4","slug":"distantly-supervised-named-entity-recognition-4","title":"Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning","date":"2022-03-03","arxiv_id":"2204.09589","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":7,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":9,"phrase":"7 ran (of which 7 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 7 samples that ran constructed an object rather than computing a result","sample_list":"/paper/distantly-supervised-named-entity-recognition-4#ran","syntology_url":"https://syntology.ai/paper/2204.09589","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09589"}},"official":{"repos":["kangISU/Conf-MPU-DS-NER"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":7,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/qaner-prompting-question-answering-models-for","slug":"qaner-prompting-question-answering-models-for","title":"QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition","date":"2022-03-03","arxiv_id":"2203.01543","repositories_listed":1,"syntology":null},{"url":"/paper/damo-nlp-at-semeval-2022-task-11-a-knowledge","slug":"damo-nlp-at-semeval-2022-task-11-a-knowledge","title":"DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition","date":"2022-03-01","arxiv_id":"2203.00545","repositories_listed":1,"syntology":null},{"url":"/paper/paranames-a-massively-multilingual-entity","slug":"paranames-a-massively-multilingual-entity","title":"ParaNames: A Massively Multilingual Entity Name Corpus","date":"2022-02-28","arxiv_id":"2202.14035","repositories_listed":1,"syntology":null},{"url":"/paper/datamux-data-multiplexing-for-neural-networks","slug":"datamux-data-multiplexing-for-neural-networks","title":"DataMUX: Data Multiplexing for Neural Networks","date":"2022-02-18","arxiv_id":"2202.09318","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":3,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/datamux-data-multiplexing-for-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2202.09318","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.09318"}},"official":{"repos":["princeton-nlp/datamux"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":3,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/aishell-ner-named-entity-recognition-from","slug":"aishell-ner-named-entity-recognition-from","title":"AISHELL-NER: Named Entity Recognition from Chinese Speech","date":"2022-02-17","arxiv_id":"2202.08533","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-fine-tuning-of-transformer-based","slug":"adaptive-fine-tuning-of-transformer-based","title":"Adaptive Fine-Tuning of Transformer-Based Language Models for Named Entity Recognition","date":"2022-02-05","arxiv_id":"2202.02617","repositories_listed":1,"syntology":null}],"record_sha256":"48461b86a94d2a3abacbba8c826b442f09de117c7dfc5b212cf0abdf6817b819","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}