{"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/pos/papers/3","list_of":"/task/pos","task":"POS","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":3,"pages_in_order":12,"rows_per_page":100,"rows":[201,300],"of":1146,"counts":{"archive_papers_tagged":1146,"with_a_code_link":277,"where_syntology_ran_a_sample":20,"not_listed_spam_title":0,"listed":1146,"listed_where_code_ran":20,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":16,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":16,"listed_every_run_a_failure_of_syntologys_instrument":4,"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/pos","prev":"/task/pos/papers/2","next":"/task/pos/papers/4","papers":[{"url":"/paper/parameter-space-factorization-for-zero-shot","slug":"parameter-space-factorization-for-zero-shot","title":"Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages","date":"2020-01-30","arxiv_id":"2001.11453","repositories_listed":1,"syntology":null},{"url":"/paper/a-primal-dual-formulation-for-deep-learning","slug":"a-primal-dual-formulation-for-deep-learning","title":"A Primal Dual Formulation For Deep Learning With Constraints","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ufal-mrpipe-at-mrp-2019-udpipe-goes-semantic-1","slug":"ufal-mrpipe-at-mrp-2019-udpipe-goes-semantic-1","title":"\\'UFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared Task","date":"2019-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/low-resource-sequence-labeling-via","slug":"low-resource-sequence-labeling-via","title":"Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations","date":"2019-10-24","arxiv_id":"1910.10893","repositories_listed":1,"syntology":null},{"url":"/paper/ufal-mrpipe-at-mrp-2019-udpipe-goes-semantic","slug":"ufal-mrpipe-at-mrp-2019-udpipe-goes-semantic","title":"ÚFAL MRPipe at MRP 2019: UDPipe Goes Semantic in the Meaning Representation Parsing Shared Task","date":"2019-10-24","arxiv_id":"1910.11295","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-parsing-with-polyglot-training","slug":"cross-lingual-parsing-with-polyglot-training","title":"Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study","date":"2019-10-17","arxiv_id":"1910.07938","repositories_listed":1,"syntology":null},{"url":"/paper/read-highlight-and-summarize-a-hierarchical","slug":"read-highlight-and-summarize-a-hierarchical","title":"Read, Highlight and Summarize: A Hierarchical Neural Semantic Encoder-based Approach","date":"2019-10-08","arxiv_id":"1910.03177","repositories_listed":1,"syntology":null},{"url":"/paper/specializing-word-embeddings-for-parsing-by","slug":"specializing-word-embeddings-for-parsing-by","title":"Specializing Word Embeddings (for Parsing) by Information Bottleneck","date":"2019-10-01","arxiv_id":"1910.00163","repositories_listed":1,"syntology":null},{"url":"/paper/gated-task-interaction-framework-for-multi","slug":"gated-task-interaction-framework-for-multi","title":"Gated Task Interaction Framework for Multi-task Sequence Tagging","date":"2019-09-29","arxiv_id":"1909.13193","repositories_listed":1,"syntology":null},{"url":"/paper/language-agnostic-syllabification-with-neural","slug":"language-agnostic-syllabification-with-neural","title":"Language-Agnostic Syllabification with Neural Sequence Labeling","date":"2019-09-29","arxiv_id":"1909.13362","repositories_listed":1,"syntology":null},{"url":"/paper/automatically-learning-data-augmentation","slug":"automatically-learning-data-augmentation","title":"Automatically Learning Data Augmentation Policies for Dialogue Tasks","date":"2019-09-27","arxiv_id":"1909.12868","repositories_listed":1,"syntology":null},{"url":"/paper/190909922","slug":"190909922","title":"Using Chinese Glyphs for Named Entity Recognition","date":"2019-09-22","arxiv_id":"1909.09922","repositories_listed":1,"syntology":null},{"url":"/paper/from-english-to-code-switching-transfer","slug":"from-english-to-code-switching-transfer","title":"From English to Code-Switching: Transfer Learning with Strong Morphological Clues","date":"2019-09-11","arxiv_id":"1909.05158","repositories_listed":1,"syntology":null},{"url":"/paper/controllable-video-captioning-with-pos","slug":"controllable-video-captioning-with-pos","title":"Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network","date":"2019-08-27","arxiv_id":"1908.10072","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-automatic-collocation","slug":"evaluation-of-automatic-collocation","title":"Evaluation of automatic collocation extraction methods for language learning","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cmu-01-at-the-sigmorphon-2019-shared-task-on","slug":"cmu-01-at-the-sigmorphon-2019-shared-task-on","title":"CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology","date":"2019-07-23","arxiv_id":"1907.10129","repositories_listed":1,"syntology":null},{"url":"/paper/augmenting-a-bilstm-tagger-with-a","slug":"augmenting-a-bilstm-tagger-with-a","title":"Augmenting a BiLSTM tagger with a Morphological Lexicon and a Lexical Category Identification Step","date":"2019-07-21","arxiv_id":"1907.09038","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-syntactic-transfer-through","slug":"cross-lingual-syntactic-transfer-through","title":"Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections","date":"2019-06-06","arxiv_id":"1906.02656","repositories_listed":1,"syntology":null},{"url":"/paper/190600247","slug":"190600247","title":"COS960: A Chinese Word Similarity Dataset of 960 Word Pairs","date":"2019-06-01","arxiv_id":"1906.00247","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-ccg-induction","slug":"cross-lingual-ccg-induction","title":"Cross-lingual CCG Induction","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-task-specific-representation-for","slug":"learning-task-specific-representation-for","title":"Learning Task-specific Representation for Novel Words in Sequence Labeling","date":"2019-05-29","arxiv_id":"1905.12277","repositories_listed":1,"syntology":null},{"url":"/paper/bert-rediscovers-the-classical-nlp-pipeline","slug":"bert-rediscovers-the-classical-nlp-pipeline","title":"BERT Rediscovers the Classical NLP Pipeline","date":"2019-05-15","arxiv_id":"1905.05950","repositories_listed":1,"syntology":null},{"url":"/paper/a-grounded-unsupervised-universal-part-of","slug":"a-grounded-unsupervised-universal-part-of","title":"A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages","date":"2019-04-10","arxiv_id":"1904.05426","repositories_listed":1,"syntology":null},{"url":"/paper/syntactic-interchangeability-in-word","slug":"syntactic-interchangeability-in-word","title":"Syntactic Interchangeability in Word Embedding Models","date":"2019-04-01","arxiv_id":"1904.00669","repositories_listed":1,"syntology":null},{"url":"/paper/dense-relational-captioning-triple-stream","slug":"dense-relational-captioning-triple-stream","title":"Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning","date":"2019-03-14","arxiv_id":"1903.05942","repositories_listed":1,"syntology":null},{"url":"/paper/character-eyes-seeing-language-through","slug":"character-eyes-seeing-language-through","title":"Character Eyes: Seeing Language through Character-Level Taggers","date":"2019-03-12","arxiv_id":"1903.05041","repositories_listed":1,"syntology":null},{"url":"/paper/universal-dependency-parsing-from-scratch","slug":"universal-dependency-parsing-from-scratch","title":"Universal Dependency Parsing from Scratch","date":"2019-01-29","arxiv_id":"1901.10457","repositories_listed":1,"syntology":null},{"url":"/paper/multi-task-deep-morphological-analyzer","slug":"multi-task-deep-morphological-analyzer","title":"Multi Task Deep Morphological Analyzer: Context Aware Joint Morphological Tagging and Lemma Prediction","date":"2018-11-21","arxiv_id":"1811.08619","repositories_listed":1,"syntology":null},{"url":"/paper/parser-extraction-of-triples-in-unstructured","slug":"parser-extraction-of-triples-in-unstructured","title":"Parser Extraction of Triples in Unstructured Text","date":"2018-11-06","arxiv_id":"1811.05768","repositories_listed":1,"syntology":null},{"url":"/paper/pd3-better-low-resource-cross-lingual","slug":"pd3-better-low-resource-cross-lingual","title":"PD3: Better Low-Resource Cross-Lingual Transfer By Combining Direct Transfer and Annotation Projection","date":"2018-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/modeling-composite-labels-for-neural","slug":"modeling-composite-labels-for-neural","title":"Modeling Composite Labels for Neural Morphological Tagging","date":"2018-10-20","arxiv_id":"1810.08815","repositories_listed":1,"syntology":null},{"url":"/paper/an-amr-aligner-tuned-by-transition-based","slug":"an-amr-aligner-tuned-by-transition-based","title":"An AMR Aligner Tuned by Transition-based Parser","date":"2018-10-08","arxiv_id":"1810.03541","repositories_listed":1,"syntology":null},{"url":"/paper/braint-at-iest-2018-fine-tuning-multiclass","slug":"braint-at-iest-2018-fine-tuning-multiclass","title":"BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion Classification","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/joint-learning-of-pos-and-dependencies-for","slug":"joint-learning-of-pos-and-dependencies-for","title":"Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weeding-out-conventionalized-metaphors-a","slug":"weeding-out-conventionalized-metaphors-a","title":"Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-brief-review-of-real-world-color-image","slug":"a-brief-review-of-real-world-color-image","title":"A Brief Review of Real-World Color Image Denoising","date":"2018-09-10","arxiv_id":"1809.03298","repositories_listed":1,"syntology":null},{"url":"/paper/toward-a-standardized-and-more-accurate","slug":"toward-a-standardized-and-more-accurate","title":"Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging","date":"2018-09-10","arxiv_id":"1809.03391","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-of-syntactic-structure","slug":"unsupervised-learning-of-syntactic-structure","title":"Unsupervised Learning of Syntactic Structure with Invertible Neural Projections","date":"2018-08-28","arxiv_id":"1808.09111","repositories_listed":1,"syntology":null},{"url":"/paper/sequence-labeling-a-practical-approach","slug":"sequence-labeling-a-practical-approach","title":"Sequence Labeling: A Practical Approach","date":"2018-08-12","arxiv_id":"1808.03926","repositories_listed":1,"syntology":null},{"url":"/paper/from-text-to-lexicon-bridging-the-gap-between","slug":"from-text-to-lexicon-bridging-the-gap-between","title":"From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-improved-neural-network-model-for-joint","slug":"an-improved-neural-network-model-for-joint","title":"An improved neural network model for joint POS tagging and dependency parsing","date":"2018-07-11","arxiv_id":"1807.03955","repositories_listed":1,"syntology":null},{"url":"/paper/part-of-speech-tagging-on-an-endangered","slug":"part-of-speech-tagging-on-an-endangered","title":"Part-of-Speech Tagging on an Endangered Language: a Parallel Griko-Italian Resource","date":"2018-06-11","arxiv_id":"1806.03757","repositories_listed":1,"syntology":null},{"url":"/paper/learning-sentence-embeddings-using-recursive","slug":"learning-sentence-embeddings-using-recursive","title":"Learning sentence embeddings using Recursive Networks","date":"2018-05-22","arxiv_id":"1805.08353","repositories_listed":1,"syntology":null},{"url":"/paper/mutual-information-maximization-for-simple","slug":"mutual-information-maximization-for-simple","title":"Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction","date":"2018-04-20","arxiv_id":"1804.07849","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/mutual-information-maximization-for-simple#ran","syntology_url":"https://syntology.ai/paper/1804.07849","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.07849"}},"official":{"repos":["karlstratos/mmi-tagger"],"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/end-to-end-graph-based-tag-parsing-with","slug":"end-to-end-graph-based-tag-parsing-with","title":"End-to-end Graph-based TAG Parsing with Neural Networks","date":"2018-04-18","arxiv_id":"1804.06610","repositories_listed":1,"syntology":null},{"url":"/paper/a-feature-rich-vietnamese-named-entity","slug":"a-feature-rich-vietnamese-named-entity","title":"A Feature-Rich Vietnamese Named-Entity Recognition Model","date":"2018-03-12","arxiv_id":"1803.04375","repositories_listed":1,"syntology":null},{"url":"/paper/structured-triplet-learning-with-pos-tag","slug":"structured-triplet-learning-with-pos-tag","title":"Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering","date":"2018-01-24","arxiv_id":"1801.07853","repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-accuracy-of-pre-trained-word","slug":"improving-the-accuracy-of-pre-trained-word","title":"Improving the Accuracy of Pre-trained Word Embeddings for Sentiment Analysis","date":"2017-11-23","arxiv_id":"1711.08609","repositories_listed":1,"syntology":null},{"url":"/paper/from-word-segmentation-to-pos-tagging-for","slug":"from-word-segmentation-to-pos-tagging-for","title":"From Word Segmentation to POS Tagging for Vietnamese","date":"2017-11-14","arxiv_id":"1711.04951","repositories_listed":1,"syntology":null},{"url":"/paper/robust-multilingual-part-of-speech-tagging","slug":"robust-multilingual-part-of-speech-tagging","title":"Robust Multilingual Part-of-Speech Tagging via Adversarial Training","date":"2017-11-14","arxiv_id":"1711.04903","repositories_listed":1,"syntology":null},{"url":"/paper/replicability-analysis-for-natural-language","slug":"replicability-analysis-for-natural-language","title":"Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets","date":"2017-09-27","arxiv_id":"1709.09500","repositories_listed":1,"syntology":null},{"url":"/paper/a-study-of-n-gram-and-embedding","slug":"a-study-of-n-gram-and-embedding","title":"A study of N-gram and Embedding Representations for Native Language Identification","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-the-morphological-compositionality","slug":"evaluating-the-morphological-compositionality","title":"Evaluating the morphological compositionality of polarity","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-structured-prediction-with","slug":"semi-supervised-structured-prediction-with","title":"Semi-supervised Structured Prediction with Neural CRF Autoencoder","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/combining-discrete-and-neural-features-for","slug":"combining-discrete-and-neural-features-for","title":"Combining Discrete and Neural Features for Sequence Labeling","date":"2017-08-24","arxiv_id":"1708.07279","repositories_listed":1,"syntology":null},{"url":"/paper/nnvlp-a-neural-network-based-vietnamese","slug":"nnvlp-a-neural-network-based-vietnamese","title":"NNVLP: A Neural Network-Based Vietnamese Language Processing Toolkit","date":"2017-08-24","arxiv_id":"1708.07241","repositories_listed":1,"syntology":null},{"url":"/paper/udl-at-semeval-2017-task-1-semantic-textual","slug":"udl-at-semeval-2017-task-1-semantic-textual","title":"UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features","date":"2017-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/to-normalize-or-not-to-normalize-the-impact","slug":"to-normalize-or-not-to-normalize-the-impact","title":"To Normalize, or Not to Normalize: The Impact of Normalization on Part-of-Speech Tagging","date":"2017-07-17","arxiv_id":"1707.05116","repositories_listed":1,"syntology":null},{"url":"/paper/a-non-projective-greedy-dependency-parser","slug":"a-non-projective-greedy-dependency-parser","title":"A non-projective greedy dependency parser with bidirectional LSTMs","date":"2017-07-11","arxiv_id":"1707.03228","repositories_listed":1,"syntology":null},{"url":"/paper/a-novel-neural-network-model-for-joint-pos","slug":"a-novel-neural-network-model-for-joint-pos","title":"A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing","date":"2017-05-16","arxiv_id":"1705.05952","repositories_listed":1,"syntology":null},{"url":"/paper/neural-word-segmentation-with-rich","slug":"neural-word-segmentation-with-rich","title":"Neural Word Segmentation with Rich Pretraining","date":"2017-04-28","arxiv_id":"1704.08960","repositories_listed":1,"syntology":null},{"url":"/paper/character-based-joint-segmentation-and-pos","slug":"character-based-joint-segmentation-and-pos","title":"Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF","date":"2017-04-05","arxiv_id":"1704.01314","repositories_listed":1,"syntology":null},{"url":"/paper/multilingual-lexicalized-constituency-parsing","slug":"multilingual-lexicalized-constituency-parsing","title":"Multilingual Lexicalized Constituency Parsing with Word-Level Auxiliary Tasks","date":"2017-04-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/lepor-an-augmented-machine-translation","slug":"lepor-an-augmented-machine-translation","title":"LEPOR: An Augmented Machine Translation Evaluation Metric","date":"2017-03-26","arxiv_id":"1703.08748","repositories_listed":1,"syntology":null},{"url":"/paper/smpost-parts-of-speech-tagger-for-code-mixed","slug":"smpost-parts-of-speech-tagger-for-code-mixed","title":"SMPOST: Parts of Speech Tagger for Code-Mixed Indic Social Media Text","date":"2017-02-01","arxiv_id":"1702.00167","repositories_listed":1,"syntology":null},{"url":"/paper/grammar-induction-from-lots-of-words-alone","slug":"grammar-induction-from-lots-of-words-alone","title":"Grammar induction from (lots of) words alone","date":"2016-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/building-a-comprehensive-syntactic-and","slug":"building-a-comprehensive-syntactic-and","title":"Building a comprehensive syntactic and semantic corpus of Chinese clinical texts","date":"2016-11-07","arxiv_id":"1611.02091","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-tagging-with-deep-residual-networks","slug":"semantic-tagging-with-deep-residual-networks","title":"Semantic Tagging with Deep Residual Networks","date":"2016-09-22","arxiv_id":"1609.07053","repositories_listed":1,"syntology":{"n":3,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"0 ran · 3 unverified","sample_list":"/paper/semantic-tagging-with-deep-residual-networks#ran","syntology_url":"https://syntology.ai/paper/1609.07053","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.07053"}},"official":{"repos":["bjerva/semantic-tagging"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"url":"/paper/read-tag-and-parse-all-at-once-or-fully","slug":"read-tag-and-parse-all-at-once-or-fully","title":"Read, Tag, and Parse All at Once, or Fully-neural Dependency Parsing","date":"2016-09-12","arxiv_id":"1609.03441","repositories_listed":1,"syntology":null},{"url":"/paper/neural-relation-extraction-with-selective","slug":"neural-relation-extraction-with-selective","title":"Neural Relation Extraction with Selective Attention over Instances","date":"2016-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/bangla-parts-of-speech-tagging-using-bangla","slug":"bangla-parts-of-speech-tagging-using-bangla","title":"Bangla Parts-of-Speech Tagging using Bangla Stemmer and Rule based Analyzer","date":"2016-06-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/database-of-mandarin-neighborhood-statistics","slug":"database-of-mandarin-neighborhood-statistics","title":"Database of Mandarin Neighborhood Statistics","date":"2016-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/integrated-sequence-tagging-for-medieval","slug":"integrated-sequence-tagging-for-medieval","title":"Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning","date":"2016-03-04","arxiv_id":"1603.01597","repositories_listed":1,"syntology":null},{"url":"/paper/many-languages-one-parser","slug":"many-languages-one-parser","title":"Many Languages, One Parser","date":"2016-02-04","arxiv_id":"1602.01595","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-part-of-speech-tagging-with","slug":"unsupervised-part-of-speech-tagging-with","title":"Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models","date":"2016-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-robust-transformation-based-learning","slug":"a-robust-transformation-based-learning","title":"A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging","date":"2014-12-12","arxiv_id":"1412.4021","repositories_listed":1,"syntology":null},{"url":"/paper/exploiting-synergies-between-open-resources","slug":"exploiting-synergies-between-open-resources","title":"Exploiting Synergies Between Open Resources for German Dependency Parsing, POS-tagging, and Morphological Analysis","date":"2013-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":null,"slug":"lingoloop-attack-trapping-mllms-via","title":"LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops","date":"2025-06-17","arxiv_id":"2506.14493","repositories_listed":0,"syntology":null},{"url":null,"slug":"private-mev-protection-rpcs-benchmark-stud","title":"Private MEV Protection RPCs: Benchmark Stud","date":"2025-05-26","arxiv_id":"2505.19708","repositories_listed":0,"syntology":null},{"url":null,"slug":"fillm-a-filipino-optimized-large-language","title":"FiLLM -- A Filipino-optimized Large Language Model based on Southeast Asia Large Language Model (SEALLM)","date":"2025-05-25","arxiv_id":"2505.18995","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-health-mention-classification","title":"Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuning","date":"2025-04-30","arxiv_id":"2504.21685","repositories_listed":0,"syntology":null},{"url":null,"slug":"proof-of-useful-intelligence-poui-blockchain","title":"Proof of Useful Intelligence (PoUI): Blockchain Consensus Beyond Energy Waste","date":"2025-04-24","arxiv_id":"2504.17539","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-unanimous-consensus-in-decision","title":"Achieving Unanimous Consensus in Decision Making Using Multi-Agents","date":"2025-04-02","arxiv_id":"2504.02128","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-analysis-of-word-segmentation","title":"A Comparative Analysis of Word Segmentation, Part-of-Speech Tagging, and Named Entity Recognition for Historical Chinese Sources, 1900-1950","date":"2025-03-25","arxiv_id":"2503.19844","repositories_listed":0,"syntology":null},{"url":null,"slug":"untangling-the-influence-of-typology-data-and","title":"Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging","date":"2025-03-25","arxiv_id":"2503.19979","repositories_listed":0,"syntology":null},{"url":null,"slug":"dreamrenderer-taming-multi-instance-attribute","title":"DreamRenderer: Taming Multi-Instance Attribute Control in Large-Scale Text-to-Image Models","date":"2025-03-17","arxiv_id":"2503.12885","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistical-analysis-of-sentence-structures","title":"Statistical Analysis of Sentence Structures through ASCII, Lexical Alignment and PCA","date":"2025-03-13","arxiv_id":"2503.10470","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-study-of-zero-shot-cross-lingual","title":"Comparative Study of Zero-Shot Cross-Lingual Transfer for Bodo POS and NER Tagging Using Gemini 2.0 Flash Thinking Experimental Model","date":"2025-03-06","arxiv_id":"2503.04405","repositories_listed":0,"syntology":null},{"url":null,"slug":"2502-06659","title":"Who Taught You That? Tracing Teachers in Model Distillation","date":"2025-02-10","arxiv_id":"2502.06659","repositories_listed":0,"syntology":null},{"url":null,"slug":"analyzing-the-effect-of-linguistic-similarity","title":"Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Experimental Setups Matter","date":"2025-01-24","arxiv_id":"2501.14491","repositories_listed":0,"syntology":null},{"url":null,"slug":"author-specific-linguistic-patterns-unveiled","title":"Author-Specific Linguistic Patterns Unveiled: A Deep Learning Study on Word Class Distributions","date":"2025-01-17","arxiv_id":"2501.10072","repositories_listed":0,"syntology":null},{"url":null,"slug":"bbpos-bert-based-part-of-speech-tagging-for","title":"BBPOS: BERT-based Part-of-Speech Tagging for Uzbek","date":"2025-01-17","arxiv_id":"2501.10107","repositories_listed":0,"syntology":null},{"url":null,"slug":"babylms-for-isixhosa-data-efficient-language","title":"BabyLMs for isiXhosa: Data-Efficient Language Modelling in a Low-Resource Context","date":"2025-01-07","arxiv_id":"2501.03855","repositories_listed":0,"syntology":null},{"url":null,"slug":"part-of-speech-sensitivity-of-routers-in","title":"Part-Of-Speech Sensitivity of Routers in Mixture of Experts Models","date":"2024-12-22","arxiv_id":"2412.16971","repositories_listed":0,"syntology":null},{"url":null,"slug":"p-yalli-un-nouveau-corpus-pour-le-nahuatl","title":"$π$-yalli: un nouveau corpus pour le nahuatl","date":"2024-12-20","arxiv_id":"2412.15821","repositories_listed":0,"syntology":null},{"url":null,"slug":"progressive-fine-to-coarse-reconstruction-for","title":"Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers","date":"2024-12-19","arxiv_id":"2412.14633","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-it-the-end-of-generative-linguistics-as-we","title":"Is it the end of (generative) linguistics as we know it?","date":"2024-12-17","arxiv_id":"2412.12797","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-llm-based-table-question","title":"Interpretable LLM-based Table Question Answering","date":"2024-12-16","arxiv_id":"2412.12386","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-llms-assist-with-ambiguity-a-quantitative","title":"Can LLMs assist with Ambiguity? A Quantitative Evaluation of various Large Language Models on Word Sense Disambiguation","date":"2024-11-27","arxiv_id":"2411.18337","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-attempt-to-develop-a-neural-parser-based","title":"An Attempt to Develop a Neural Parser based on Simplified Head-Driven Phrase Structure Grammar on Vietnamese","date":"2024-11-26","arxiv_id":"2411.17270","repositories_listed":0,"syntology":null}],"record_sha256":"f6d9648b15730be30a10aac7bfcd2cfdd10db1dc84e7df4fe1e59d9afd1e6807","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}