{"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/25","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":25,"pages_in_order":29,"rows_per_page":100,"rows":[2401,2500],"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/24","next":"/task/named-entity-recognition-ner/papers/26","papers":[{"url":null,"slug":"segment-level-sequence-modeling-using-gated","title":"Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supersense-embeddings-a-unified-model-for","title":"Supersense Embeddings: A Unified Model for Supersense Interpretation, Prediction, and Utilization","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"syntactic-analyses-and-named-entity","title":"Syntactic analyses and named entity recognition for PubMed and PubMed Central --- up-to-the-minute","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"visualizing-and-curating-knowledge-graphs","title":"Visualizing and Curating Knowledge Graphs over Time and Space","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-named-entity-recognizers-in-a","title":"Comparing Named-Entity Recognizers in a Targeted Domain: Handcrafted Rules vs Machine Learning","date":"2016-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sharing-network-parameters-for-crosslingual","title":"Sharing Network Parameters for Crosslingual Named Entity Recognition","date":"2016-07-01","arxiv_id":"1607.00198","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-ku-at-semeval-2016-task-11-word-embeddings","title":"AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-automatic-chinese-word","title":"An Empirical Study of Automatic Chinese Word Segmentation for Spoken Language Understanding and Named Entity Recognition","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clipumd-at-semeval-2016-task-8-parser-for","title":"CLIP@UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-alignment-transfer-a-chicken","title":"Cross-lingual alignment transfer: a chicken-and-egg story?","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cu-nlp-at-semeval-2016-task-8-amr-parsing","title":"CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dependency-based-embeddings-for-sentence","title":"Dependency Based Embeddings for Sentence Classification Tasks","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"drop-out-conditional-random-fields-for","title":"Drop-out Conditional Random Fields for Twitter with Huge Mined Gazetteer","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-feature-induction-the-last-gist-to","title":"Dynamic Feature Induction: The Last Gist to the State-of-the-Art","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hhu-at-semeval-2016-task-1-multiple","title":"HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"icl-hd-at-semeval-2016-task-10-improving-the","title":"ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-distributed-word-representations-for-1","title":"Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-the-verifiability-of-details-as-a-test","title":"Using the verifiability of details as a test of deception: A conceptual framework for the automation of the verifiability approach","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uta-dlnlp-at-semeval-2016-task-12-deep","title":"UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uthealth-at-semeval-2016-task-12-an-end-to","title":"UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-question-answering-by-deep-entity","title":"Boosting Question Answering by Deep Entity Recognition","date":"2016-05-27","arxiv_id":"1605.08675","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-japanese-chess-commentary-corpus","title":"A Japanese Chess Commentary Corpus","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bridge-language-capitalization-inference-in","title":"Bridge-Language Capitalization Inference in Western Iranian: Sorani, Kurmanji, Zazaki, and Tajik","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-named-entity-1","title":"Domain Adaptation for Named Entity Recognition Using CRFs","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-task-pertinent-sorted-error-lists","title":"Generating Task-Pertinent sorted Error Lists for Speech Recognition","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"government-domain-named-entity-recognition","title":"Government Domain Named Entity Recognition for South African Languages","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"incorporating-lexico-semantic-heuristics-into","title":"Incorporating Lexico-semantic Heuristics into Coreference Resolution Sieves for Named Entity Recognition at Document-level","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"name-translation-based-on-fine-grained-named","title":"Name Translation based on Fine-grained Named Entity Recognition in a Single Language","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nlp-infrastructure-for-the-lithuanian","title":"NLP Infrastructure for the Lithuanian Language","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"port4nooj-v30-integrated-linguistic-resources","title":"Port4NooJ v3.0: Integrated Linguistic Resources for Portuguese NLP","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"qtleap-wsdned-corpora-semantic-annotation-of","title":"QTLeap WSD/NED Corpora: Semantic Annotation of Parallel Corpora in Six Languages","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-a-language-technology-infrastructure","title":"Using a Language Technology Infrastructure for German in order to Anonymize German Sign Language Corpus Data","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-word-embeddings-to-translate-named","title":"Using Word Embeddings to Translate Named Entities","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wtf-lod-a-new-resource-for-large-scale-ner","title":"WTF-LOD - A New Resource for Large-Scale NER Evaluation","date":"2016-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"parallelizing-word2vec-in-shared-and","title":"Parallelizing Word2Vec in Shared and Distributed Memory","date":"2016-04-15","arxiv_id":"1604.04661","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-named-entity-recognition-for","title":"Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning","date":"2016-03-02","arxiv_id":"1603.00786","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-mention-detection-robustness-with","title":"Toward Mention Detection Robustness with Recurrent Neural Networks","date":"2016-02-24","arxiv_id":"1602.07749","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-lists-of-names-for-named-entity","title":"Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents","date":"2016-02-14","arxiv_id":"1602.04427","repositories_listed":0,"syntology":null},{"url":null,"slug":"numerical-atrribute-extraction-from-clinical","title":"Numerical Atrribute Extraction from Clinical Texts","date":"2016-01-31","arxiv_id":"1602.00269","repositories_listed":0,"syntology":null},{"url":null,"slug":"j-nerd-joint-named-entity-recognition-and","title":"J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features","date":"2016-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embeddings-as-metric-recovery-in","title":"Word Embeddings as Metric Recovery in Semantic Spaces","date":"2016-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-temporal-expression-recognition-system-for","title":"A temporal expression recognition system for medical documents by","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-multiword-expressions-for-hindi","title":"Detection of Multiword Expressions for Hindi Language using Word Embeddings and WordNet-based Features","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/domain-adaption-of-named-entity-recognition","slug":"domain-adaption-of-named-entity-recognition","title":"Domain Adaption of Named Entity Recognition to Support Credit Risk Assessment","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-names-in-trove-named-entity","title":"Finding Names in Trove: Named Entity Recognition for Australian Historical Newspapers","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-language-processing-from-bytes","title":"Multilingual Language Processing From Bytes","date":"2015-12-01","arxiv_id":"1512.00103","repositories_listed":0,"syntology":null},{"url":null,"slug":"ruchi-rating-individual-food-items-in","title":"Ruchi: Rating Individual Food Items in Restaurant Reviews","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-entity-information-from-a-knowledge","title":"Using Entity Information from a Knowledge Base to Improve Relation Extraction","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-bootstrapping-approach-for","title":"Semi-supervised Bootstrapping approach for Named Entity Recognition","date":"2015-11-21","arxiv_id":"1511.06833","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-tagging-solution-bidirectional-lstm","title":"A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding","date":"2015-11-01","arxiv_id":"1511.00215","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tool-for-web-ner-model-generation-using","title":"基於已知名稱搜尋結果的網路實體辨識模型建立工具(A Tool for Web NER Model Generation Using Search Snippets of Known Entities) [In Chinese]","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-entity-linking-with-user-history-and","title":"Improved Entity Linking with User History and News Articles","date":"2015-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kannada-named-entity-recognition-and","title":"Kannada named entity recognition and classification (nerc) based on multinomial naïve bayes (mnb) classifier","date":"2015-09-12","arxiv_id":"1509.04385","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-timeml-compliant-temporal-expression","title":"On TimeML-Compliant Temporal Expression Extraction in Turkish","date":"2015-09-03","arxiv_id":"1509.00963","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-tv-program-discovery-dialog-system-using","title":"A TV Program Discovery Dialog System using recommendations","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-biomedical-tokenization","title":"An Analysis of Biomedical Tokenization: Problems and Strategies","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"arabic-named-entity-recognition-process-using","title":"Arabic Named Entity Recognition Process using Transducer Cascade and Arabic Wikipedia","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-construction-of-complex-features-in","title":"Automatic construction of complex features in Conditional Random Fields for Named Entities Recognition","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"c3el-a-joint-model-for-cross-document-co","title":"C3EL: A Joint Model for Cross-Document Co-Reference Resolution and Entity Linking","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-with-filtering-for-named","title":"Domain Adaptation with Filtering for Named Entity Extraction of Japanese Anime-Related Words","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-named-entity-annotation-through-pre","title":"Efficient Named Entity Annotation through Pre-empting","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-methods-for-unsupervised-word","title":"Evaluation methods for unsupervised word embeddings","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-word-embedding-for-drug-name","title":"Exploring Word Embedding for Drug Name Recognition","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extracting-relations-between-non-standard","title":"Extracting Relations between Non-Standard Entities using Distant Supervision and Imitation Learning","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-entity-recognition-and-disambiguation","title":"Joint Entity Recognition and Disambiguation","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-mention-extraction-and-classification","title":"Joint Mention Extraction and Classification with Mention Hypergraphs","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"language-and-domain-independent-entity","title":"Language and Domain Independent Entity Linking with Quantified Collective Validation","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mr-bennet-his-coachman-and-the-archbishop","title":"Mr. Bennet, his coachman, and the Archbishop walk into a bar but only one of them gets recognized: On The Difficulty of Detecting Characters in Literary Texts","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-recognition-of-persons-names-in","title":"Named Entity Recognition of Persons' Names in Arabic Tweets","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-recognition-with-document","title":"Named entity recognition with document-specific KB tag gazetteers","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"open-domain-name-error-detection-using-a","title":"Open-Domain Name Error Detection using a Multi-Task RNN","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"specializing-word-embeddings-for-similarity","title":"Specializing Word Embeddings for Similarity or Relatedness","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"squibs-evaluation-methods-for-statistically","title":"Squibs: Evaluation Methods for Statistically Dependent Text","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"training-automatic-transliteration-models-on","title":"Training Automatic Transliteration Models on DBPedia Data","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"whats-in-an-embedding-analyzing-word","title":"What's in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/challenges-in-clinical-natural-language","slug":"challenges-in-clinical-natural-language","title":"Challenges in clinical natural language processing for automated disorder normalization","date":"2015-07-14","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-procedural-text-understanding","title":"A Framework for Procedural Text Understanding","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simultaneous-recognition-framework-for-the","title":"A Simultaneous Recognition Framework for the Spoken Language Understanding Module of Intelligent Personal Assistant Software on Smart Phones","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aligning-opinions-cross-lingual-opinion","title":"Aligning Opinions: Cross-Lingual Opinion Mining with Dependencies","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-keyword-extraction-on-twitter","title":"Automatic Keyword Extraction on Twitter","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"big-data-small-data-in-domain-out-of-domain","title":"Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representations on Sequence Labelling Tasks","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"boosting-named-entity-recognition-with-neural-1","title":"Boosting Named Entity Recognition with Neural Character Embeddings","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chinese-grammatical-error-diagnosis-by","title":"Chinese Grammatical Error Diagnosis by Conditional Random Fields","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"chinese-named-entity-recognition-with-graph","title":"Chinese Named Entity Recognition with Graph-based Semi-supervised Learning Model","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-transfer-of-named-entity","title":"Cross-lingual Transfer of Named Entity Recognizers without Parallel Corpora","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"data-adaptation-for-named-entity-recognition","title":"Data Adaptation for Named Entity Recognition on Tweets with Features-Rich CRF","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"edrak-entity-centric-data-resource-for-arabic","title":"EDRAK: Entity-Centric Data Resource for Arabic Knowledge","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-methods-for-fine-grained-entity","title":"Embedding Methods for Fine Grained Entity Type Classification","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-named-entity-recognition-in-twitter","title":"Enhancing Named Entity Recognition in Twitter Messages Using Entity Linking","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"entity-linking-korean-text-an-unsupervised","title":"Entity Linking Korean Text: An Unsupervised Learning Approach using Semantic Relations","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-distributed-word-representations","title":"Evaluating distributed word representations for capturing semantics of biomedical concepts","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"from-dbpedia-and-wordnet-hierarchies-to","title":"From DBpedia and WordNet hierarchies to LinkedIn and Twitter","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hallym-named-entity-recognition-on-twitter","title":"Hallym: Named Entity Recognition on Twitter with Word Representation","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"harem-and-klue-how-to-put-two-tagsets-for","title":"HAREM and Klue: how to put two tagsets for named entities annotation together","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iitp-multiobjective-differential-evolution","title":"IITP: Multiobjective Differential Evolution based Twitter Named Entity Recognition","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-named-entity-recognition-in-tweets","title":"Improving Named Entity Recognition in Tweets via Detecting Non-Standard Words","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-twitter-named-entity-recognition","title":"Improving Twitter Named Entity Recognition using Word Representations","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-arabic-segmentation-and-part-of-speech","title":"Joint Arabic Segmentation and Part-Of-Speech Tagging","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-hidden-markov-models-with","title":"Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-lexical-embeddings-with-syntactic","title":"Learning Lexical Embeddings with Syntactic and Lexicographic Knowledge","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"96e8b3c9c48583e89e4e195c8d14da336768c17af5b918729d522845d6061380","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}