{"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/papers/33","list_of":"/task/text-classification","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":33,"pages_in_order":37,"rows_per_page":100,"rows":[3201,3300],"of":3635,"counts":{"archive_papers_tagged":3635,"with_a_code_link":1308,"where_syntology_ran_a_sample":272,"not_listed_spam_title":0,"listed":3635,"listed_where_code_ran":272,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":219,"every_run_a_failure_of_syntologys_instrument":53,"listed_with_a_run_with_no_instrument_failure":219,"listed_every_run_a_failure_of_syntologys_instrument":53,"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","prev":"/task/text-classification/papers/32","next":"/task/text-classification/papers/34","papers":[{"url":null,"slug":"co-training-for-demographic-classification","title":"Co-training for Demographic Classification Using Deep Learning from Label Proportions","date":"2017-09-13","arxiv_id":"1709.04108","repositories_listed":0,"syntology":null},{"url":null,"slug":"linguistic-features-of-genre-and-method","title":"Linguistic Features of Genre and Method Variation in Translation: A Computational Perspective","date":"2017-09-13","arxiv_id":"1709.04359","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-review-to-rating-exploring-dependency","title":"From Review to Rating: Exploring Dependency Measures for Text Classification","date":"2017-09-04","arxiv_id":"1709.00813","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-training-for-relation-extraction","title":"Adversarial Training for Relation Extraction","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-nlp-analysis-of-exaggerated-claims-in","title":"An NLP Analysis of Exaggerated Claims in Science News","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bibi-system-description-building-with-cnns","title":"BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-based-extraction-of-numeric","title":"Classification based extraction of numeric values from clinical narratives","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"classifier-stacking-for-native-language","title":"Classifier Stacking for Native Language Identification","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deception-detection-for-the-russian-language","title":"Deception Detection for the Russian Language: Lexical and Syntactic Parameters","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deception-detection-in-news-reports-in-the","title":"Deception Detection in News Reports in the Russian Language: Lexics and Discourse","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gradascent-at-emoint-2017-character-and-word-1","title":"GradAscent at EmoInt-2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"idiom-aware-compositional-distributed","title":"Idiom-Aware Compositional Distributed Semantics","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"initializing-convolutional-filters-with","title":"Initializing Convolutional Filters with Semantic Features for Text Classification","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inter-annotator-agreement-in-sentiment","title":"Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-different-syntactic-context","title":"Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-embeddings-of-chinese-words-characters","title":"Joint Embeddings of Chinese Words, Characters, and Fine-grained Subcharacter Components","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"knowyournyms-a-game-of-semantic-relationships","title":"KnowYourNyms? A Game of Semantic Relationships","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"language-based-mapping-of-science-assessment","title":"Language Based Mapping of Science Assessment Items to Skills","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-audiences-laughter-during","title":"Predicting Audience's Laughter During Presentations Using Convolutional Neural Network","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recognizing-textual-entailment-in-twitter","title":"Recognizing Textual Entailment in Twitter Using Word Embeddings","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stacked-sentence-document-classifier-approach","title":"Stacked Sentence-Document Classifier Approach for Improving Native Language Identification","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-impact-of-modeling-overall-argumentation","title":"The Impact of Modeling Overall Argumentation with Tree Kernels","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-power-of-character-n-grams-in-native","title":"The Power of Character N-grams in Native Language Identification","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transparent-text-quality-assessment-with","title":"Transparent text quality assessment with convolutional neural networks","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embeddings-for-multi-label-document","title":"Word Embeddings for Multi-label Document Classification","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-learning-for-short-text-expansion","title":"End-to-end Learning for Short Text Expansion","date":"2017-08-30","arxiv_id":"1709.00389","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-feature-selection-on-micro-text","title":"Impact of Feature Selection on Micro-Text Classification","date":"2017-08-27","arxiv_id":"1708.08123","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-nearest-neighbor-augmented-neural-networks","title":"$k$-Nearest Neighbor Augmented Neural Networks for Text Classification","date":"2017-08-25","arxiv_id":"1708.07863","repositories_listed":0,"syntology":null},{"url":null,"slug":"vector-space-model-as-cognitive-space-for","title":"Vector Space Model as Cognitive Space for Text Classification","date":"2017-08-21","arxiv_id":"1708.06068","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-the-law-area-and-decisions-of","title":"Predicting the Law Area and Decisions of French Supreme Court Cases","date":"2017-08-04","arxiv_id":"1708.01681","repositories_listed":0,"syntology":null},{"url":"/paper/projectionnet-learning-efficient-on-device","slug":"projectionnet-learning-efficient-on-device","title":"ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections","date":"2017-08-02","arxiv_id":"1708.00630","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-diagnosis-coding-of-radiology","title":"Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"class-based-prediction-errors-to-detect-hate","title":"Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-lingual-classification-of-topics-in","title":"Cross-Lingual Classification of Topics in Political Texts","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deepsa-at-semeval-2017-task-4-interpolated","title":"deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"does-the-geometry-of-word-embeddings-help","title":"Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology-Based Representations","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ej-sa-2017-at-semeval-2017-task-4-experiments","title":"ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-as-causal-inference","title":"Feature Selection as Causal Inference: Experiments with Text Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"funsentiment-at-semeval-2017-task-4-topic","title":"funSentiment at SemEval-2017 Task 4: Topic-Based Message Sentiment Classification by Exploiting Word Embeddings, Text Features and Target Contexts","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hccl-at-semeval-2017-task-2-combining","title":"HCCL at SemEval-2017 Task 2: Combining Multilingual Word Embeddings and Transliteration Model for Semantic Similarity","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hitachi-at-semeval-2017-task-12-system-for","title":"Hitachi at SemEval-2017 Task 12: System for temporal information extraction from clinical notes","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hlpupenn-at-semeval-2017-task-4a-a-simple","title":"HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"in-your-wildest-dreams-the-language-and","title":"In your wildest dreams: the language and psychological features of dreams","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"initializing-neural-networks-for-hierarchical","title":"Initializing neural networks for hierarchical multi-label text classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nru-hse-at-semeval-2017-task-4-tweet","title":"NRU-HSE at SemEval-2017 Task 4: Tweet Quantification Using Deep Learning Architecture","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"personality-driven-differences-in-paraphrase","title":"Personality Driven Differences in Paraphrase Preference","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pku_icl-at-semeval-2017-task-10-keyphrase","title":"PKU\\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"proactive-learning-for-named-entity","title":"Proactive Learning for Named Entity Recognition","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-graph-based-method-of-multimodal","title":"Spectral Graph-Based Method of Multimodal Word Embedding","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tagging-funding-agencies-and-grants-in","title":"Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tsa-inf-at-semeval-2017-task-4-an-ensemble-of","title":"TSA-INF at SemEval-2017 Task 4: An Ensemble of Deep Learning Architectures Including Lexicon Features for Twitter Sentiment Analysis","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-convolutional-neural-networks-to-1","title":"Using Convolutional Neural Networks to Classify Hate-Speech","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ynu-hpcc-at-semeval-2017-task-4-using-a-multi","title":"YNU-HPCC at SemEval 2017 Task 4: Using A Multi-Channel CNN-LSTM Model for Sentiment Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-graphical-models-for","title":"Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities","date":"2017-07-26","arxiv_id":"1707.08309","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-image-to-text-classification-a-novel","title":"From Image to Text Classification: A Novel Approach based on Clustering Word Embeddings","date":"2017-07-25","arxiv_id":"1707.08098","repositories_listed":0,"syntology":null},{"url":null,"slug":"improve-lexicon-based-word-embeddings-by-word","title":"Improve Lexicon-based Word Embeddings By Word Sense Disambiguation","date":"2017-07-24","arxiv_id":"1707.07628","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-oriented-text-detection-and","title":"Multi-Oriented Text Detection and Verification in Video Frames and Scene Images","date":"2017-07-22","arxiv_id":"1707.07150","repositories_listed":0,"syntology":null},{"url":null,"slug":"do-convolutional-networks-need-to-be-deep-for","title":"Do Convolutional Networks need to be Deep for Text Classification ?","date":"2017-07-13","arxiv_id":"1707.04108","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generalized-recurrent-neural-architecture","title":"A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning","date":"2017-07-10","arxiv_id":"1707.02892","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-network-with-visual-text-composition","title":"A Deep Network with Visual Text Composition Behavior","date":"2017-07-05","arxiv_id":"1707.01555","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-cognitive-features-from-gaze-data","title":"Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-depression-for-japanese-blog-text","title":"Predicting Depression for Japanese Blog Text","date":"2017-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"topic-modeling-for-classification-of-clinical","title":"Topic Modeling for Classification of Clinical Reports","date":"2017-06-19","arxiv_id":"1706.06177","repositories_listed":0,"syntology":null},{"url":null,"slug":"jctc-a-large-job-posting-corpus-for-text","title":"JCTC: A Large Job posting Corpus for Text Classification","date":"2017-06-12","arxiv_id":"1705.06123","repositories_listed":0,"syntology":null},{"url":null,"slug":"trimming-and-improving-skip-thought-vectors","title":"Trimming and Improving Skip-thought Vectors","date":"2017-06-09","arxiv_id":"1706.03148","repositories_listed":0,"syntology":null},{"url":null,"slug":"character-based-text-classification-using-top","title":"Character-Based Text Classification using Top Down Semantic Model for Sentence Representation","date":"2017-05-29","arxiv_id":"1705.10586","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-wl-sppim-semantic-model-for-document","title":"A WL-SPPIM Semantic Model for Document Classification","date":"2017-05-26","arxiv_id":"1706.01758","repositories_listed":0,"syntology":null},{"url":null,"slug":"utility-of-general-and-specific-word","title":"Utility of General and Specific Word Embeddings for Classifying Translational Stages of Research","date":"2017-05-17","arxiv_id":"1705.06262","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-with-learner-corpora-using-the-tle","title":"Learning with learner corpora: Using the TLE for native language identification","date":"2017-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-effect-of-translationese-on-tuning-for","title":"The Effect of Translationese on Tuning for Statistical Machine Translation","date":"2017-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-of-neural-network-techniques-for","title":"A Survey of Neural Network Techniques for Feature Extraction from Text","date":"2017-04-27","arxiv_id":"1704.08531","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-text-classification-can-be-fooled","title":"Deep Text Classification Can be Fooled","date":"2017-04-26","arxiv_id":"1704.08006","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-based-isolated-arabic-scene","title":"Deep Learning based Isolated Arabic Scene Character Recognition","date":"2017-04-22","arxiv_id":"1704.06821","repositories_listed":0,"syntology":null},{"url":null,"slug":"medical-text-classification-using","title":"Medical Text Classification using Convolutional Neural Networks","date":"2017-04-22","arxiv_id":"1704.06841","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-multi-task-learning-for-text","title":"Adversarial Multi-task Learning for Text Classification","date":"2017-04-19","arxiv_id":"1704.05742","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-multi-view-networks-for-text","title":"End-to-End Multi-View Networks for Text Classification","date":"2017-04-19","arxiv_id":"1704.05907","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluzh-at-vardial-gdi-2017-testing-a-variety","title":"CLUZH at VarDial GDI 2017: Testing a Variety of Machine Learning Tools for the Classification of Swiss German Dialects","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-short-text-sentiment-analysis","title":"Comparison of Short-Text Sentiment Analysis Methods for Croatian","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-bidirectional-long-short-term","title":"Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-n-gram-representations-for","title":"Continuous N-gram Representations for Authorship Attribution","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deception-detection-in-russian-texts","title":"Deception detection in Russian texts","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminating-between-similar-languages-with","title":"Discriminating between Similar Languages with Word-level Convolutional Neural Networks","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-lexical-and-syntactic-features-for","title":"Exploring Lexical and Syntactic Features for Language Variety Identification","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-topic-coherence-through-optimal","title":"Measuring Topic Coherence through Optimal Word Buckets","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"online-learning-of-task-specific-word","title":"Online Learning of Task-specific Word Representations with a Joint Biconvex Passive-Aggressive Algorithm","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-similarity-of-arabic-sentences-with","title":"Semantic Similarity of Arabic Sentences with Word Embeddings","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"single-and-cross-domain-polarity","title":"Single and Cross-domain Polarity Classification using String Kernels","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-intermodal-and-intramodal-label","title":"Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes","date":"2017-03-22","arxiv_id":"1703.07519","repositories_listed":0,"syntology":null},{"url":null,"slug":"metalearning-for-feature-selection","title":"Metalearning for Feature Selection","date":"2017-03-20","arxiv_id":"1703.06990","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-graphs-of-classifiers-to-impose","title":"Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning","date":"2017-03-05","arxiv_id":"1703.01557","repositories_listed":0,"syntology":null},{"url":null,"slug":"studying-positive-speech-on-twitter","title":"Studying Positive Speech on Twitter","date":"2017-02-24","arxiv_id":"1702.08866","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-optimization-of-fasttext-linear","title":"Analysis and Optimization of fastText Linear Text Classifier","date":"2017-02-17","arxiv_id":"1702.05531","repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-audiences-laughter-using","title":"Predicting Audience's Laughter Using Convolutional Neural Network","date":"2017-02-08","arxiv_id":"1702.02584","repositories_listed":0,"syntology":null},{"url":null,"slug":"bangla-word-clustering-based-on-tri-gram-4","title":"Bangla Word Clustering Based on Tri-gram, 4-gram and 5-gram Language Model","date":"2017-01-27","arxiv_id":"1701.08702","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-classifier-approach-to-document","title":"Semantic classifier approach to document classification","date":"2017-01-16","arxiv_id":"1701.04292","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-mixing-for-latent-dirichlet-allocation","title":"Fast mixing for Latent Dirichlet allocation","date":"2017-01-11","arxiv_id":"1701.02960","repositories_listed":0,"syntology":null},{"url":null,"slug":"heterogeneous-transfer-learning-an","title":"Heterogeneous domain adaptation: An unsupervised approach","date":"2017-01-10","arxiv_id":"1701.02511","repositories_listed":0,"syntology":null},{"url":null,"slug":"universal-dependencies-based-syntactic","title":"Universal Dependencies-based syntactic features in detecting human translation varieties","date":"2017-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unraveling-reported-dreams-with-text","title":"Unraveling reported dreams with text analytics","date":"2016-12-12","arxiv_id":"1612.03659","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-feature-selection-technique-combined","title":"A New Feature Selection Technique Combined with ELM Feature Space for Text Classification","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"6e915da541555075ec309b166d1e7f58e73318282b0520d4ef2d64541c913d4f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}