{"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/text-classification-1/papers/11","list_of":"/task/text-classification-1","task":"text-classification","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":11,"pages_in_order":31,"rows_per_page":100,"rows":[1001,1100],"of":3054,"counts":{"archive_papers_tagged":3054,"with_a_code_link":1083,"where_syntology_ran_a_sample":204,"not_listed_spam_title":0,"listed":3054,"listed_where_code_ran":204,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":161,"every_run_a_failure_of_syntologys_instrument":43,"listed_with_a_run_with_no_instrument_failure":161,"listed_every_run_a_failure_of_syntologys_instrument":43,"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/text-classification-1","prev":"/task/text-classification-1/papers/10","next":"/task/text-classification-1/papers/12","papers":[{"url":"/paper/learning-to-weight-for-text-classification","slug":"learning-to-weight-for-text-classification","title":"Learning to Weight for Text Classification","date":"2019-03-28","arxiv_id":"1903.12090","repositories_listed":1,"syntology":null},{"url":"/paper/low-resource-text-classification-with-ulmfit","slug":"low-resource-text-classification-with-ulmfit","title":"Low Resource Text Classification with ULMFit and Backtranslation","date":"2019-03-21","arxiv_id":"1903.09244","repositories_listed":1,"syntology":null},{"url":"/paper/an-embarrassingly-simple-approach-for","slug":"an-embarrassingly-simple-approach-for","title":"An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models","date":"2019-02-27","arxiv_id":"1902.10547","repositories_listed":1,"syntology":null},{"url":"/paper/how-large-a-vocabulary-does-text","slug":"how-large-a-vocabulary-does-text","title":"How Large a Vocabulary Does Text Classification Need? A Variational Approach to Vocabulary Selection","date":"2019-02-27","arxiv_id":"1902.10339","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/how-large-a-vocabulary-does-text#ran","syntology_url":"https://syntology.ai/paper/1902.10339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.10339"}},"official":{"repos":["wenhuchen/Variational-Vocabulary-Selection"],"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/semantic-hilbert-space-for-text","slug":"semantic-hilbert-space-for-text","title":"Semantic Hilbert Space for Text Representation Learning","date":"2019-02-26","arxiv_id":"1902.09802","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-path-prediction-for-semi-supervised","slug":"efficient-path-prediction-for-semi-supervised","title":"Efficient Path Prediction for Semi-Supervised and Weakly Supervised Hierarchical Text Classification","date":"2019-02-25","arxiv_id":"1902.09347","repositories_listed":1,"syntology":null},{"url":"/paper/vector-of-locally-aggregated-word-embeddings","slug":"vector-of-locally-aggregated-word-embeddings","title":"Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation","date":"2019-02-23","arxiv_id":"1902.08850","repositories_listed":1,"syntology":null},{"url":"/paper/deep-short-text-classification-with-knowledge","slug":"deep-short-text-classification-with-knowledge","title":"Deep Short Text Classification with Knowledge Powered Attention","date":"2019-02-21","arxiv_id":"1902.08050","repositories_listed":1,"syntology":null},{"url":"/paper/tax2vec-constructing-interpretable-features","slug":"tax2vec-constructing-interpretable-features","title":"tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification","date":"2019-02-01","arxiv_id":"1902.00438","repositories_listed":1,"syntology":null},{"url":"/paper/squeezed-very-deep-convolutional-neural","slug":"squeezed-very-deep-convolutional-neural","title":"Squeezed Very Deep Convolutional Neural Networks for Text Classification","date":"2019-01-28","arxiv_id":"1901.09821","repositories_listed":1,"syntology":null},{"url":"/paper/identifying-unclear-questions-in-community","slug":"identifying-unclear-questions-in-community","title":"Identifying Unclear Questions in Community Question Answering Websites","date":"2019-01-18","arxiv_id":"1901.06168","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-hierarchical-text","slug":"weakly-supervised-hierarchical-text","title":"Weakly-Supervised Hierarchical Text Classification","date":"2018-12-29","arxiv_id":"1812.11270","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/weakly-supervised-hierarchical-text#ran","syntology_url":"https://syntology.ai/paper/1812.11270","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.11270"}},"official":{"repos":["yumeng5/WeSHClass"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/textbugger-generating-adversarial-text","slug":"textbugger-generating-adversarial-text","title":"TextBugger: Generating Adversarial Text Against Real-world Applications","date":"2018-12-13","arxiv_id":"1812.05271","repositories_listed":1,"syntology":null},{"url":"/paper/practical-text-classification-with-large-pre","slug":"practical-text-classification-with-large-pre","title":"Practical Text Classification With Large Pre-Trained Language Models","date":"2018-12-04","arxiv_id":"1812.01207","repositories_listed":1,"syntology":null},{"url":"/paper/discrete-attacks-and-submodular-optimization","slug":"discrete-attacks-and-submodular-optimization","title":"Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification","date":"2018-12-01","arxiv_id":"1812.00151","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/discrete-attacks-and-submodular-optimization#ran","syntology_url":"https://syntology.ai/paper/1812.00151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.00151"}},"official":{"repos":["cecilialeiqi/adversarial_text"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/explicit-interaction-model-towards-text","slug":"explicit-interaction-model-towards-text","title":"Explicit Interaction Model towards Text Classification","date":"2018-11-23","arxiv_id":"1811.09386","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/explicit-interaction-model-towards-text#ran","syntology_url":"https://syntology.ai/paper/1811.09386","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.09386"}},"official":{"repos":["NonvolatileMemory/AAAI_2019_EXAM"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/evolutionary-data-measures-understanding-the","slug":"evolutionary-data-measures-understanding-the","title":"Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks","date":"2018-11-05","arxiv_id":"1811.01910","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/evolutionary-data-measures-understanding-the#ran","syntology_url":"https://syntology.ai/paper/1811.01910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.01910"}},"official":{"repos":["Wluper/edm"],"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/the-data-challenge-in-misinformation","slug":"the-data-challenge-in-misinformation","title":"The Data Challenge in Misinformation Detection: Source Reputation vs. Content Veracity","date":"2018-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/word-movers-embedding-from-word2vec-to","slug":"word-movers-embedding-from-word2vec-to","title":"Word Mover's Embedding: From Word2Vec to Document Embedding","date":"2018-10-30","arxiv_id":"1811.01713","repositories_listed":1,"syntology":null},{"url":"/paper/compositional-coding-capsule-network-with-k","slug":"compositional-coding-capsule-network-with-k","title":"Compositional Coding Capsule Network with K-Means Routing for Text Classification","date":"2018-10-22","arxiv_id":"1810.09177","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-distributional-correspondence","slug":"revisiting-distributional-correspondence","title":"Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments","date":"2018-10-19","arxiv_id":"1810.09311","repositories_listed":1,"syntology":null},{"url":"/paper/infodens-an-open-source-framework-for","slug":"infodens-an-open-source-framework-for","title":"INFODENS: An Open-source Framework for Learning Text Representations","date":"2018-10-16","arxiv_id":"1810.07091","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-text-classification-via-image","slug":"end-to-end-text-classification-via-image","title":"End-to-End Text Classification via Image-based Embedding using Character-level Networks","date":"2018-10-08","arxiv_id":"1810.03595","repositories_listed":1,"syntology":null},{"url":"/paper/zero-resource-multilingual-model-transfer","slug":"zero-resource-multilingual-model-transfer","title":"Multi-Source Cross-Lingual Model Transfer: Learning What to Share","date":"2018-10-08","arxiv_id":"1810.03552","repositories_listed":1,"syntology":null},{"url":"/paper/a-hierarchical-neural-attention-based-text","slug":"a-hierarchical-neural-attention-based-text","title":"A Hierarchical Neural Attention-based Text Classifier","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/did-you-offend-me-classification-of-offensive","slug":"did-you-offend-me-classification-of-offensive","title":"Did you offend me? Classification of Offensive Tweets in Hinglish Language","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-and-zero-shot-multi-label-learning","slug":"few-shot-and-zero-shot-multi-label-learning","title":"Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/increasing-in-class-similarity-by","slug":"increasing-in-class-similarity-by","title":"Increasing In-Class Similarity by Retrofitting Embeddings with Demographic Information","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-governing-neural-networks-for-on-device","slug":"self-governing-neural-networks-for-on-device","title":"Self-Governing Neural Networks for On-Device Short Text Classification","date":"2018-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cross-domain-labeled-lda-for-cross-domain","slug":"cross-domain-labeled-lda-for-cross-domain","title":"Cross-Domain Labeled LDA for Cross-Domain Text Classification","date":"2018-09-16","arxiv_id":"1809.05820","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-cross-lingual-transfer-of-word","slug":"unsupervised-cross-lingual-transfer-of-word","title":"Unsupervised Cross-lingual Transfer of Word Embedding Spaces","date":"2018-09-10","arxiv_id":"1809.03633","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/unsupervised-cross-lingual-transfer-of-word#ran","syntology_url":"https://syntology.ai/paper/1809.03633","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.03633"}},"official":{"repos":["xrc10/unsup-cross-lingual-embedding-transfer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/interpreting-neural-networks-with-nearest","slug":"interpreting-neural-networks-with-nearest","title":"Interpreting Neural Networks With Nearest Neighbors","date":"2018-09-08","arxiv_id":"1809.02847","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-reprogramming-of-sequence","slug":"adversarial-reprogramming-of-sequence","title":"Adversarial Reprogramming of Text Classification Neural Networks","date":"2018-09-06","arxiv_id":"1809.01829","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-neural-text-classification","slug":"weakly-supervised-neural-text-classification","title":"Weakly-Supervised Neural Text Classification","date":"2018-09-02","arxiv_id":"1809.01478","repositories_listed":1,"syntology":null},{"url":"/paper/rule-induction-for-global-explanation-of","slug":"rule-induction-for-global-explanation-of","title":"Rule induction for global explanation of trained models","date":"2018-08-29","arxiv_id":"1808.09744","repositories_listed":1,"syntology":null},{"url":"/paper/rational-recurrences","slug":"rational-recurrences","title":"Rational Recurrences","date":"2018-08-28","arxiv_id":"1808.09357","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":1,"n_no_contract":6,"n_pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 1 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/rational-recurrences#ran","syntology_url":"https://syntology.ai/paper/1808.09357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.09357"}},"official":{"repos":["Noahs-ARK/rational-recurrences"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-unit-based-dilated-convolution-for","slug":"semantic-unit-based-dilated-convolution-for","title":"Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification","date":"2018-08-26","arxiv_id":"1808.08561","repositories_listed":1,"syntology":null},{"url":"/paper/building-a-robust-text-classifier-on-a-test","slug":"building-a-robust-text-classifier-on-a-test","title":"Robust Text Classifier on Test-Time Budgets","date":"2018-08-24","arxiv_id":"1808.08270","repositories_listed":1,"syntology":null},{"url":"/paper/from-random-to-supervised-a-novel-dropout","slug":"from-random-to-supervised-a-novel-dropout","title":"From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information","date":"2018-08-24","arxiv_id":"1808.08149","repositories_listed":1,"syntology":null},{"url":"/paper/an-improvement-of-data-classification-using","slug":"an-improvement-of-data-classification-using","title":"An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)","date":"2018-08-23","arxiv_id":"1808.08121","repositories_listed":1,"syntology":null},{"url":"/paper/task-oriented-word-embedding-for-text","slug":"task-oriented-word-embedding-for-text","title":"Task-oriented Word Embedding for Text Classification","date":"2018-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/orthogonal-matching-pursuit-for-text","slug":"orthogonal-matching-pursuit-for-text","title":"Orthogonal Matching Pursuit for Text Classification","date":"2018-07-12","arxiv_id":"1807.04715","repositories_listed":1,"syntology":null},{"url":"/paper/automated-labeling-of-bugs-and-tickets-using","slug":"automated-labeling-of-bugs-and-tickets-using","title":"Automated labeling of bugs and tickets using attention-based mechanisms in recurrent neural networks","date":"2018-07-08","arxiv_id":"1807.02892","repositories_listed":1,"syntology":null},{"url":"/paper/incorporating-latent-meanings-of","slug":"incorporating-latent-meanings-of","title":"Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-how-to-actively-learn-a-deep","slug":"learning-how-to-actively-learn-a-deep","title":"Learning How to Actively Learn: A Deep Imitation Learning Approach","date":"2018-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/gile-a-generalized-input-label-embedding-for","slug":"gile-a-generalized-input-label-embedding-for","title":"GILE: A Generalized Input-Label Embedding for Text Classification","date":"2018-06-16","arxiv_id":"1806.06219","repositories_listed":1,"syntology":null},{"url":"/paper/smhd-a-large-scale-resource-for-exploring","slug":"smhd-a-large-scale-resource-for-exploring","title":"SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions","date":"2018-06-13","arxiv_id":"1806.05258","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-neural-text-classification-for","slug":"adapting-neural-text-classification-for","title":"Adapting Neural Text Classification for Improved Software Categorization","date":"2018-06-05","arxiv_id":"1806.01742","repositories_listed":1,"syntology":null},{"url":"/paper/fusing-document-collection-and-label-graph","slug":"fusing-document-collection-and-label-graph","title":"Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification","date":"2018-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/abstractive-text-classification-using","slug":"abstractive-text-classification-using","title":"Abstractive Text Classification Using Sequence-to-convolution Neural Networks","date":"2018-05-20","arxiv_id":"1805.07745","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-text-classification-with-pre-trained","slug":"few-shot-text-classification-with-pre-trained","title":"Few-Shot Text Classification with Pre-Trained Word Embeddings and a Human in the Loop","date":"2018-04-05","arxiv_id":"1804.02063","repositories_listed":1,"syntology":null},{"url":"/paper/near-lossless-binarization-of-word-embeddings","slug":"near-lossless-binarization-of-word-embeddings","title":"Near-lossless Binarization of Word Embeddings","date":"2018-03-24","arxiv_id":"1803.09065","repositories_listed":1,"syntology":null},{"url":"/paper/convolutional-neural-networks-for-toxic","slug":"convolutional-neural-networks-for-toxic","title":"Convolutional Neural Networks for Toxic Comment Classification","date":"2018-02-27","arxiv_id":"1802.09957","repositories_listed":1,"syntology":null},{"url":"/paper/multinomial-adversarial-networks-for-multi","slug":"multinomial-adversarial-networks-for-multi","title":"Multinomial Adversarial Networks for Multi-Domain Text Classification","date":"2018-02-15","arxiv_id":"1802.05694","repositories_listed":1,"syntology":null},{"url":"/paper/intentional-control-of-type-i-error-over","slug":"intentional-control-of-type-i-error-over","title":"Intentional Control of Type I Error over Unconscious Data Distortion: a Neyman-Pearson Approach to Text Classification","date":"2018-02-07","arxiv_id":"1802.02558","repositories_listed":1,"syntology":null},{"url":"/paper/a-practitioners-guide-to-transfer-learning","slug":"a-practitioners-guide-to-transfer-learning","title":"A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks","date":"2018-01-19","arxiv_id":"1801.06480","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-the-working-of-text-classifiers","slug":"investigating-the-working-of-text-classifiers","title":"Investigating the Working of Text Classifiers","date":"2018-01-19","arxiv_id":"1801.06261","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-movie-genres-based-on-plot","slug":"predicting-movie-genres-based-on-plot","title":"Predicting Movie Genres Based on Plot Summaries","date":"2018-01-15","arxiv_id":"1801.04813","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-method-of-region-embedding-for-text","slug":"a-new-method-of-region-embedding-for-text","title":"A New Method of Region Embedding for Text Classification","date":"2018-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/train-once-test-anywhere-zero-shot-learning","slug":"train-once-test-anywhere-zero-shot-learning","title":"Train Once, Test Anywhere: Zero-Shot Learning for Text Classification","date":"2017-12-16","arxiv_id":"1712.05972","repositories_listed":1,"syntology":null},{"url":"/paper/multi-label-dataless-text-classification-with","slug":"multi-label-dataless-text-classification-with","title":"Multi-label Dataless Text Classification with Topic Modeling","date":"2017-11-05","arxiv_id":"1711.01563","repositories_listed":1,"syntology":null},{"url":"/paper/all-in-1-short-text-classification-with-one","slug":"all-in-1-short-text-classification-with-one","title":"ALL-IN-1: Short Text Classification with One Model for All Languages","date":"2017-10-26","arxiv_id":"1710.09589","repositories_listed":1,"syntology":null},{"url":"/paper/think-globally-embed-locally-locally-linear","slug":"think-globally-embed-locally-locally-linear","title":"Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words","date":"2017-09-19","arxiv_id":"1709.06671","repositories_listed":1,"syntology":null},{"url":"/paper/dict2vec-learning-word-embeddings-using","slug":"dict2vec-learning-word-embeddings-using","title":"Dict2vec : Learning Word Embeddings using Lexical Dictionaries","date":"2017-09-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/topical-coherence-in-lda-based-models-through","slug":"topical-coherence-in-lda-based-models-through","title":"Topical Coherence in LDA-based Models through Induced Segmentation","date":"2017-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-text-classification-with-vectors-of","slug":"improving-text-classification-with-vectors-of","title":"Improving text classification with vectors of reduced precision","date":"2017-06-20","arxiv_id":"1706.06363","repositories_listed":1,"syntology":null},{"url":"/paper/crnn-a-joint-neural-network-for-redundancy","slug":"crnn-a-joint-neural-network-for-redundancy","title":"CRNN: A Joint Neural Network for Redundancy Detection","date":"2017-06-04","arxiv_id":"1706.01069","repositories_listed":1,"syntology":null},{"url":"/paper/contextual-explanation-networks","slug":"contextual-explanation-networks","title":"Contextual Explanation Networks","date":"2017-05-29","arxiv_id":"1705.10301","repositories_listed":1,"syntology":null},{"url":"/paper/learning-convolutional-text-representations","slug":"learning-convolutional-text-representations","title":"Learning Convolutional Text Representations for Visual Question Answering","date":"2017-05-18","arxiv_id":"1705.06824","repositories_listed":1,"syntology":null},{"url":"/paper/using-titles-vs-full-text-as-source-for","slug":"using-titles-vs-full-text-as-source-for","title":"Using Titles vs. Full-text as Source for Automated Semantic Document Annotation","date":"2017-05-15","arxiv_id":"1705.05311","repositories_listed":1,"syntology":null},{"url":"/paper/cross-lingual-distillation-for-text","slug":"cross-lingual-distillation-for-text","title":"Cross-lingual Distillation for Text Classification","date":"2017-05-05","arxiv_id":"1705.02073","repositories_listed":1,"syntology":null},{"url":"/paper/regularizing-model-complexity-and-label","slug":"regularizing-model-complexity-and-label","title":"Regularizing Model Complexity and Label Structure for Multi-Label Text Classification","date":"2017-05-01","arxiv_id":"1705.00740","repositories_listed":1,"syntology":null},{"url":"/paper/an-automated-text-categorization-framework","slug":"an-automated-text-categorization-framework","title":"An Automated Text Categorization Framework based on Hyperparameter Optimization","date":"2017-04-06","arxiv_id":"1704.01975","repositories_listed":1,"syntology":null},{"url":"/paper/aggressive-sampling-for-multi-class-to-binary","slug":"aggressive-sampling-for-multi-class-to-binary","title":"Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification","date":"2017-01-23","arxiv_id":"1701.06511","repositories_listed":1,"syntology":null},{"url":"/paper/supervised-word-movers-distance","slug":"supervised-word-movers-distance","title":"Supervised Word Mover's Distance","date":"2016-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weighted-neural-bag-of-n-grams-model-new","slug":"weighted-neural-bag-of-n-grams-model-new","title":"Weighted Neural Bag-of-n-grams Model: New Baselines for Text Classification","date":"2016-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/character-level-convolutional-network-for","slug":"character-level-convolutional-network-for","title":"Character-level Convolutional Network for Text Classification Applied to Chinese Corpus","date":"2016-11-14","arxiv_id":"1611.04358","repositories_listed":1,"syntology":null},{"url":"/paper/ac-blstm-asymmetric-convolutional","slug":"ac-blstm-asymmetric-convolutional","title":"AC-BLSTM: Asymmetric Convolutional Bidirectional LSTM Networks for Text Classification","date":"2016-11-07","arxiv_id":"1611.01884","repositories_listed":1,"syntology":null},{"url":"/paper/active-discriminative-text-representation","slug":"active-discriminative-text-representation","title":"Active Discriminative Text Representation Learning","date":"2016-06-14","arxiv_id":"1606.04212","repositories_listed":1,"syntology":null},{"url":"/paper/on-a-topic-model-for-sentences","slug":"on-a-topic-model-for-sentences","title":"On a Topic Model for Sentences","date":"2016-06-01","arxiv_id":"1606.00253","repositories_listed":1,"syntology":null},{"url":"/paper/inducing-generalized-multi-label-rules-with","slug":"inducing-generalized-multi-label-rules-with","title":"Inducing Generalized Multi-Label Rules with Learning Classifier Systems","date":"2015-12-25","arxiv_id":"1512.07982","repositories_listed":1,"syntology":null},{"url":"/paper/deep-unordered-composition-rivals-syntactic","slug":"deep-unordered-composition-rivals-syntactic","title":"Deep Unordered Composition Rivals Syntactic Methods for Text Classification","date":"2015-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-convolutional-neural-networks-for-2","slug":"recurrent-convolutional-neural-networks-for-2","title":"Recurrent Convolutional Neural Networks for Text Classification","date":"2015-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":null,"slug":"perspectives-in-play-a-multi-perspective","title":"Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems","date":"2025-06-25","arxiv_id":"2506.20209","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-generated-images-serve-as-a-viable","title":"Can Generated Images Serve as a Viable Modality for Text-Centric Multimodal Learning?","date":"2025-06-21","arxiv_id":"2506.17623","repositories_listed":0,"syntology":null},{"url":null,"slug":"shrec-and-pheona-using-large-language-models","title":"SHREC and PHEONA: Using Large Language Models to Advance Next-Generation Computational Phenotyping","date":"2025-06-19","arxiv_id":"2506.16359","repositories_listed":0,"syntology":null},{"url":null,"slug":"flick-few-labels-text-classification-using-k","title":"Flick: Few Labels Text Classification using K-Aware Intermediate Learning in Multi-Task Low-Resource Languages","date":"2025-06-12","arxiv_id":"2506.10292","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08400","title":"mSTEB: Massively Multilingual Evaluation of LLMs on Speech and Text Tasks","date":"2025-06-10","arxiv_id":"2506.08400","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimatch-multihead-consistency","title":"MultiMatch: Multihead Consistency Regularization Matching for Semi-Supervised Text Classification","date":"2025-06-09","arxiv_id":"2506.07801","repositories_listed":0,"syntology":null},{"url":null,"slug":"tokenbreak-bypassing-text-classification","title":"TokenBreak: Bypassing Text Classification Models Through Token Manipulation","date":"2025-06-09","arxiv_id":"2506.07948","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-text-classification-using","title":"Hierarchical Text Classification Using Contrastive Learning Informed Path Guided Hierarchy","date":"2025-06-04","arxiv_id":"2506.04381","repositories_listed":0,"syntology":null},{"url":null,"slug":"climate-eval-a-comprehensive-benchmark-for","title":"Climate-Eval: A Comprehensive Benchmark for NLP Tasks Related to Climate Change","date":"2025-05-24","arxiv_id":"2505.18653","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-in-the-task-of","title":"Large Language Models in the Task of Automatic Validation of Text Classifier Predictions","date":"2025-05-24","arxiv_id":"2505.18688","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-bert-like-bidirectional-models-still","title":"Do BERT-Like Bidirectional Models Still Perform Better on Text Classification in the Era of LLMs?","date":"2025-05-23","arxiv_id":"2505.18215","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-sequence-classification-with","title":"Incremental Sequence Classification with Temporal Consistency","date":"2025-05-22","arxiv_id":"2505.16548","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-language-models-as-computable","title":"Large Language Models as Computable Approximations to Solomonoff Induction","date":"2025-05-21","arxiv_id":"2505.15784","repositories_listed":0,"syntology":null},{"url":null,"slug":"small-language-models-in-the-real-world","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","date":"2025-05-21","arxiv_id":"2505.16078","repositories_listed":0,"syntology":null},{"url":null,"slug":"hausanlp-current-status-challenges-and-future","title":"HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing","date":"2025-05-20","arxiv_id":"2505.14311","repositories_listed":0,"syntology":null},{"url":null,"slug":"ko-kinetics-inspired-neural-optimizer-with","title":"KO: Kinetics-inspired Neural Optimizer with PDE Simulation Approaches","date":"2025-05-20","arxiv_id":"2505.14777","repositories_listed":0,"syntology":null},{"url":null,"slug":"reliable-decision-support-with-llms-a","title":"Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications","date":"2025-05-20","arxiv_id":"2505.14918","repositories_listed":0,"syntology":null}],"record_sha256":"ee3cd9ca06e42d726a46fb6f03621ec8725169694e18f7c72a709c498484555d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}