{"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/cg/papers/4","list_of":"/task/cg","task":"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":4,"pages_in_order":18,"rows_per_page":100,"rows":[301,400],"of":1729,"counts":{"archive_papers_tagged":1729,"with_a_code_link":665,"where_syntology_ran_a_sample":70,"not_listed_spam_title":0,"listed":1729,"listed_where_code_ran":70,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":61,"every_run_a_failure_of_syntologys_instrument":9,"listed_with_a_run_with_no_instrument_failure":61,"listed_every_run_a_failure_of_syntologys_instrument":9,"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/cg","prev":"/task/cg/papers/3","next":"/task/cg/papers/5","papers":[{"url":"/paper/pcbert-parent-and-child-bert-for-chinese-few","slug":"pcbert-parent-and-child-bert-for-chinese-few","title":"PCBERT: Parent and Child BERT for Chinese Few-shot NER","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/slicer-sliced-fine-tuning-for-low-resource","slug":"slicer-sliced-fine-tuning-for-low-resource","title":"SLICER: Sliced Fine-Tuning for Low-Resource Cross-Lingual Transfer for Named Entity Recognition","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/how-to-tackle-an-emerging-topic-combining","slug":"how-to-tackle-an-emerging-topic-combining","title":"How to tackle an emerging topic? Combining strong and weak labels for Covid news NER","date":"2022-09-29","arxiv_id":"2209.15108","repositories_listed":1,"syntology":null},{"url":"/paper/mets-cov-a-dataset-of-medical-entity-and","slug":"mets-cov-a-dataset-of-medical-entity-and","title":"METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets","date":"2022-09-28","arxiv_id":"2209.13773","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/mets-cov-a-dataset-of-medical-entity-and#ran","syntology_url":"https://syntology.ai/paper/2209.13773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.13773"}},"official":{"repos":["ylab-open/mets-cov"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/damo-nlp-at-nlpcc-2022-task-2-knowledge","slug":"damo-nlp-at-nlpcc-2022-task-2-knowledge","title":"DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linking","date":"2022-09-27","arxiv_id":"2209.13187","repositories_listed":1,"syntology":null},{"url":"/paper/application-of-deep-learning-in-generating-1","slug":"application-of-deep-learning-in-generating-1","title":"Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique","date":"2022-09-25","arxiv_id":"2209.12177","repositories_listed":1,"syntology":null},{"url":"/paper/cae-mechanism-to-diminish-the-class","slug":"cae-mechanism-to-diminish-the-class","title":"CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task","date":"2022-09-21","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/t-ner-an-all-round-python-library-for-1","slug":"t-ner-an-all-round-python-library-for-1","title":"T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition","date":"2022-09-09","arxiv_id":"2209.12616","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/t-ner-an-all-round-python-library-for-1#ran","syntology_url":"https://syntology.ai/paper/2209.12616","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.12616"}},"official":{"repos":["asahi417/tner"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/scl-rai-span-based-contrastive-learning-with","slug":"scl-rai-span-based-contrastive-learning-with","title":"SCL-RAI: Span-based Contrastive Learning with Retrieval Augmented Inference for Unlabeled Entity Problem in NER","date":"2022-09-04","arxiv_id":"2209.01646","repositories_listed":1,"syntology":null},{"url":"/paper/kochet-a-korean-cultural-heritage-corpus-for","slug":"kochet-a-korean-cultural-heritage-corpus-for","title":"KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks","date":"2022-09-01","arxiv_id":"2209.00367","repositories_listed":1,"syntology":null},{"url":"/paper/annotated-dataset-creation-through-general","slug":"annotated-dataset-creation-through-general","title":"Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP","date":"2022-08-30","arxiv_id":"2208.14493","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-bi-encoder-for-named-entity","slug":"optimizing-bi-encoder-for-named-entity","title":"Optimizing Bi-Encoder for Named Entity Recognition via Contrastive Learning","date":"2022-08-30","arxiv_id":"2208.14565","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/optimizing-bi-encoder-for-named-entity#ran","syntology_url":"https://syntology.ai/paper/2208.14565","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.14565"}},"official":{"repos":["microsoft/binder"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-embarrassingly-easy-but-strong-baseline","slug":"an-embarrassingly-easy-but-strong-baseline","title":"An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition","date":"2022-08-09","arxiv_id":"2208.04534","repositories_listed":1,"syntology":null},{"url":"/paper/sciannotate-a-tool-for-integrating-weak","slug":"sciannotate-a-tool-for-integrating-weak","title":"SciAnnotate: A Tool for Integrating Weak Labeling Sources for Sequence Labeling","date":"2022-08-07","arxiv_id":"2208.10241","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-clinical-and-biomedical-named-entity","slug":"accurate-clinical-and-biomedical-named-entity","title":"Accurate clinical and biomedical Named entity recognition at scale","date":"2022-07-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/multilinguals-at-semeval-2022-task-11-complex-1","slug":"multilinguals-at-semeval-2022-task-11-complex-1","title":"Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages","date":"2022-07-14","arxiv_id":"2207.06882","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-the-value-of-gazetteer-in-chinese","slug":"rethinking-the-value-of-gazetteer-in-chinese","title":"Rethinking the Value of Gazetteer in Chinese Named Entity Recognition","date":"2022-07-06","arxiv_id":"2207.02802","repositories_listed":1,"syntology":null},{"url":"/paper/a-label-aware-autoregressive-framework-for","slug":"a-label-aware-autoregressive-framework-for","title":"A Label-Aware Autoregressive Framework for Cross-Domain NER","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cross-language-transfer-of-high-quality","slug":"cross-language-transfer-of-high-quality","title":"Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/crossroads-buildings-and-neighborhoods-a","slug":"crossroads-buildings-and-neighborhoods-a","title":"Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/generating-unlabelled-data-for-a-tri-training","slug":"generating-unlabelled-data-for-a-tri-training","title":"Generating unlabelled data for a tri-training approach in a low resourced NER task","date":"2022-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/jbnu-cclab-at-semeval-2022-task-12-machine","slug":"jbnu-cclab-at-semeval-2022-task-12-machine","title":"JBNU-CCLab at SemEval-2022 Task 12: Machine Reading Comprehension and Span Pair Classification for Linking Mathematical Symbols to Their Descriptions","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/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/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/skoltechnlp-at-semeval-2022-task-8","slug":"skoltechnlp-at-semeval-2022-task-8","title":"SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations","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/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/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/searching-for-optimal-subword-tokenization-in","slug":"searching-for-optimal-subword-tokenization-in","title":"Searching for Optimal Subword Tokenization in Cross-domain NER","date":"2022-06-07","arxiv_id":"2206.03352","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/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/fine-grained-error-analysis-and-fair","slug":"fine-grained-error-analysis-and-fair","title":"Fine-Grained Error Analysis and Fair Evaluation of Labeled Spans","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/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/nerkor-cars-ontonotes","slug":"nerkor-cars-ontonotes","title":"NerKor+Cars-OntoNotes++","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/r2d2-robust-data-to-text-with-replacement","slug":"r2d2-robust-data-to-text-with-replacement","title":"R2D2: Robust Data-to-Text with Replacement Detection","date":"2022-05-25","arxiv_id":"2205.12467","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/vector-quantized-input-contextualized-soft","slug":"vector-quantized-input-contextualized-soft","title":"Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding","date":"2022-05-23","arxiv_id":"2205.11024","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/vector-quantized-input-contextualized-soft#ran","syntology_url":"https://syntology.ai/paper/2205.11024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.11024"}},"official":{"repos":["declare-lab/vip"],"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/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/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/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/hybrid-transformer-with-multi-level-fusion","slug":"hybrid-transformer-with-multi-level-fusion","title":"Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion","date":"2022-05-04","arxiv_id":"2205.02357","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/pretrained-biomedical-language-models-for","slug":"pretrained-biomedical-language-models-for","title":"Pretrained Biomedical Language Models for Clinical NLP in Spanish","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/por-que-nao-utiliser-alla-sprak-mixed","slug":"por-que-nao-utiliser-alla-sprak-mixed","title":"Por Qué Não Utiliser Alla Språk? Mixed Training with Gradient Optimization in Few-Shot Cross-Lingual Transfer","date":"2022-04-29","arxiv_id":"2204.13869","repositories_listed":1,"syntology":null},{"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/l3cube-hingcorpus-and-hingbert-a-code-mixed","slug":"l3cube-hingcorpus-and-hingbert-a-code-mixed","title":"L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models","date":"2022-04-18","arxiv_id":"2204.08398","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/multilingual-language-model-adaptive-fine","slug":"multilingual-language-model-adaptive-fine","title":"Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning","date":"2022-04-13","arxiv_id":"2204.06487","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/multilingual-language-model-adaptive-fine#ran","syntology_url":"https://syntology.ai/paper/2204.06487","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.06487"}},"official":{"repos":["uds-lsv/afro-maft"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/the-impact-of-cross-lingual-adjustment-of-1","slug":"the-impact-of-cross-lingual-adjustment-of-1","title":"The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer","date":"2022-04-13","arxiv_id":"2204.06457","repositories_listed":1,"syntology":null},{"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/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/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/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/multilinguals-at-semeval-2022-task-11","slug":"multilinguals-at-semeval-2022-task-11","title":"Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER","date":"2022-04-05","arxiv_id":"2204.02173","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/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/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/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/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/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/l3cube-mahacorpus-and-mahabert-marathi","slug":"l3cube-mahacorpus-and-mahabert-marathi","title":"L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources","date":"2022-02-02","arxiv_id":"2202.01159","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-information-based-approach-to-1","slug":"a-simple-information-based-approach-to-1","title":"A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis","date":"2022-01-29","arxiv_id":"2201.12549","repositories_listed":1,"syntology":null},{"url":"/paper/annotating-the-tweebank-corpus-on-named","slug":"annotating-the-tweebank-corpus-on-named","title":"Annotating the Tweebank Corpus on Named Entity Recognition and Building NLP Models for Social Media Analysis","date":"2022-01-18","arxiv_id":"2201.07281","repositories_listed":1,"syntology":{"n":15,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":6,"n_honours":3,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 3 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/annotating-the-tweebank-corpus-on-named#ran","syntology_url":"https://syntology.ai/paper/2201.07281","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.07281"}},"official":{"repos":["social-machines/tweebanknlp"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/bern2-an-advanced-neural-biomedical-named","slug":"bern2-an-advanced-neural-biomedical-named","title":"BERN2: an advanced neural biomedical named entity recognition and normalization tool","date":"2022-01-06","arxiv_id":"2201.02080","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bern2-an-advanced-neural-biomedical-named#ran","syntology_url":"https://syntology.ai/paper/2201.02080","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.02080"}},"official":{"repos":["dmis-lab/bern2"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/huspacy-an-industrial-strength-hungarian","slug":"huspacy-an-industrial-strength-hungarian","title":"HuSpaCy: an industrial-strength Hungarian natural language processing toolkit","date":"2022-01-06","arxiv_id":"2201.01956","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"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) · 0 unverified","sample_list":"/paper/huspacy-an-industrial-strength-hungarian#ran","syntology_url":"https://syntology.ai/paper/2201.01956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.01956"}},"official":null}},{"url":"/paper/kind-an-italian-multi-domain-dataset-for","slug":"kind-an-italian-multi-domain-dataset-for","title":"KIND: an Italian Multi-Domain Dataset for Named Entity Recognition","date":"2021-12-30","arxiv_id":"2112.15099","repositories_listed":1,"syntology":null},{"url":"/paper/unified-named-entity-recognition-as-word-word","slug":"unified-named-entity-recognition-as-word-word","title":"Unified Named Entity Recognition as Word-Word Relation Classification","date":"2021-12-19","arxiv_id":"2112.10070","repositories_listed":1,"syntology":null},{"url":"/paper/learning-rich-representation-of-keyphrases-1","slug":"learning-rich-representation-of-keyphrases-1","title":"Learning Rich Representation of Keyphrases from Text","date":"2021-12-16","arxiv_id":"2112.08547","repositories_listed":1,"syntology":null},{"url":"/paper/named-entity-recognition-architecture","slug":"named-entity-recognition-architecture","title":"Named entity recognition architecture combining contextual and global features","date":"2021-12-15","arxiv_id":"2112.08033","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-use-of-external-data-for-spoken-named","slug":"on-the-use-of-external-data-for-spoken-named","title":"On the Use of External Data for Spoken Named Entity Recognition","date":"2021-12-14","arxiv_id":"2112.07648","repositories_listed":1,"syntology":null},{"url":"/paper/anea-automated-named-entity-annotation-for","slug":"anea-automated-named-entity-annotation-for","title":"ANEA: Automated (Named) Entity Annotation for German Domain-Specific Texts","date":"2021-12-13","arxiv_id":"2112.06724","repositories_listed":1,"syntology":null},{"url":"/paper/jaber-junior-arabic-bert","slug":"jaber-junior-arabic-bert","title":"JABER and SABER: Junior and Senior Arabic BERt","date":"2021-12-08","arxiv_id":"2112.04329","repositories_listed":1,"syntology":null},{"url":"/paper/open-cykg-an-open-cyber-threat-intelligence","slug":"open-cykg-an-open-cyber-threat-intelligence","title":"Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph","date":"2021-12-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/did-you-enjoy-the-last-supper-an-experimental","slug":"did-you-enjoy-the-last-supper-an-experimental","title":"Did You Enjoy the Last Supper? An Experimental Study on Cross-Domain NER Models for the Art Domain","date":"2021-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cl-neril-a-cross-lingual-model-for-ner-in","slug":"cl-neril-a-cross-lingual-model-for-ner-in","title":"CL-NERIL: A Cross-Lingual Model for NER in Indian Languages","date":"2021-11-23","arxiv_id":"2111.11815","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-based-multilingual-language-model-1","slug":"knowledge-based-multilingual-language-model-1","title":"Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples","date":"2021-11-22","arxiv_id":"2111.10962","repositories_listed":1,"syntology":null},{"url":"/paper/an-unsupervised-multiple-task-and-multiple","slug":"an-unsupervised-multiple-task-and-multiple","title":"An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity Recognition","date":"2021-11-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/distantly-supervised-named-entity-recognition-3","slug":"distantly-supervised-named-entity-recognition-3","title":"Distantly Supervised Named Entity Recognition with Category-Oriented Confidence Calibration","date":"2021-11-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-learning-in-named-entity","slug":"zero-shot-learning-in-named-entity","title":"Zero-Shot Learning in Named-Entity Recognition with External Knowledge","date":"2021-11-15","arxiv_id":"2111.07734","repositories_listed":1,"syntology":null},{"url":"/paper/focusing-on-possible-named-entities-in-active","slug":"focusing-on-possible-named-entities-in-active","title":"Focusing on Potential Named Entities During Active Label Acquisition","date":"2021-11-06","arxiv_id":"2111.03837","repositories_listed":1,"syntology":null}],"record_sha256":"1c0e695084334bfc8d1b270a5093b767ac6918321220c16f46c6caa444541954","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}