{"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/document-classification/papers/5","list_of":"/task/document-classification","task":"Document 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":5,"pages_in_order":7,"rows_per_page":100,"rows":[401,500],"of":641,"counts":{"archive_papers_tagged":641,"with_a_code_link":235,"where_syntology_ran_a_sample":33,"not_listed_spam_title":0,"listed":641,"listed_where_code_ran":33,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":29,"every_run_a_failure_of_syntologys_instrument":4,"listed_with_a_run_with_no_instrument_failure":29,"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/document-classification","prev":"/task/document-classification/papers/4","next":"/task/document-classification/papers/6","papers":[{"url":null,"slug":"gender-detection-on-social-networks-using","title":"Gender Detection on Social Networks using Ensemble Deep Learning","date":"2020-04-13","arxiv_id":"2004.06518","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-approach-to-learning-unsupervised","title":"A Simple Approach to Learning Unsupervised Multilingual Embeddings","date":"2020-04-10","arxiv_id":"2004.05991","repositories_listed":0,"syntology":null},{"url":null,"slug":"source-printer-identification-from-document","title":"Source Printer Identification from Document Images Acquired using Smartphone","date":"2020-03-27","arxiv_id":"2003.12602","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-gcn-enhancing-graph-convolutional","title":"Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions","date":"2020-03-05","arxiv_id":"2003.02587","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-estimation-reveals-topic","title":"Contrastive estimation reveals topic posterior information to linear models","date":"2020-03-04","arxiv_id":"2003.02234","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmark-performance-of-machine-and-deep","title":"Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification","date":"2020-03-03","arxiv_id":"2003.01345","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-replicability-of-combining-word","title":"On the Replicability of Combining Word Embeddings and Retrieval Models","date":"2020-01-13","arxiv_id":"2001.04484","repositories_listed":0,"syntology":null},{"url":null,"slug":"long-length-legal-document-classification","title":"Long-length Legal Document Classification","date":"2019-12-14","arxiv_id":"1912.06905","repositories_listed":0,"syntology":null},{"url":null,"slug":"flatm-a-fuzzy-logic-approach-topic-model-for","title":"FLATM: A Fuzzy Logic Approach Topic Model for Medical Documents","date":"2019-11-25","arxiv_id":"1911.10953","repositories_listed":0,"syntology":null},{"url":null,"slug":"asynchronous-distributed-learning-from","title":"Asynchronous Distributed Learning from Constraints","date":"2019-11-13","arxiv_id":"1911.05473","repositories_listed":0,"syntology":null},{"url":null,"slug":"keeping-consistency-of-sentence-generation","title":"Keeping Consistency of Sentence Generation and Document Classification with Multi-Task Learning","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prado-projection-attention-networks-for","title":"PRADO: Projection Attention Networks for Document Classification On-Device","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"wasserstein-distances-for-evaluating-cross","title":"Wasserstein distances for evaluating cross-lingual embeddings","date":"2019-10-24","arxiv_id":"1910.11005","repositories_listed":0,"syntology":null},{"url":null,"slug":"progress-notes-classification-and-keyword","title":"Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT","date":"2019-10-13","arxiv_id":"1910.05786","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-only-from-relevant-keywords-and","title":"Learning Only from Relevant Keywords and Unlabeled Documents","date":"2019-10-10","arxiv_id":"1910.04385","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-pretrained-language-models-for-long","title":"ADAPTING PRETRAINED LANGUAGE MODELS FOR LONG DOCUMENT CLASSIFICATION","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-generative-rhetorical-structure","title":"Neural Generative Rhetorical Structure Parsing","date":"2019-09-24","arxiv_id":"1909.11049","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-classification-methods","title":"Document classification methods","date":"2019-09-16","arxiv_id":"1909.07368","repositories_listed":0,"syntology":null},{"url":null,"slug":"turning-silver-into-gold-error-focused-corpus","title":"Turning silver into gold: error-focused corpus reannotation with active learning","date":"2019-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"successive-projection-algorithm-robust-to","title":"Successive Projection Algorithm Robust to Outliers","date":"2019-08-12","arxiv_id":"1908.04109","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-relational-classification-via-bayesian","title":"Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings","date":"2019-08-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"analysing-representations-of-memory","title":"Analysing Representations of Memory Impairment in a Clinical Notes Classification Model","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"argument-component-classification-by-relation","title":"Argument Component Classification by Relation Identification by Neural Network and TextRank","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pentagon-at-mediqa-2019-multi-task-learning","title":"Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment","date":"2019-07-01","arxiv_id":"1907.01643","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-semantic-cover-approach-for-topic-modeling","title":"A Semantic Cover Approach for Topic Modeling","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"doris-martin-at-semeval-2019-task-4","title":"Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-knowledge-graph-embedding-into","title":"Integration of Knowledge Graph Embedding Into Topic Modeling with Hierarchical Dirichlet Process","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-unsupervised-multilingual-word","title":"Learning Unsupervised Multilingual Word Embeddings with Incremental Multilingual Hubs","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-discriminative-learning-for-unsupervised","title":"Self-Discriminative Learning for Unsupervised Document Embedding","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"team-fernando-pessa-at-semeval-2019-task-4","title":"Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"190511498","title":"FAN: Focused Attention Networks","date":"2019-05-27","arxiv_id":"1905.11498","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-text-classification-in-legal","title":"Explainable Text Classification in Legal Document Review A Case Study of Explainable Predictive Coding","date":"2019-04-03","arxiv_id":"1904.01721","repositories_listed":0,"syntology":null},{"url":null,"slug":"190408499","title":"Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction","date":"2019-04-01","arxiv_id":"1904.08499","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-tree-lstm-structure-aware","title":"Interpretable Structure-aware Document Encoders with Hierarchical Attention","date":"2019-02-26","arxiv_id":"1902.09713","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-sentiment-analysis-using-a-graph-based","title":"Leveraging Deep Graph-Based Text Representation for Sentiment Polarity Applications","date":"2019-02-23","arxiv_id":"1902.10247","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-cross-lingual-subword-similarities","title":"Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification","date":"2018-12-22","arxiv_id":"1812.09617","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-topic-identification-in-telephone","title":"Multiple topic identification in telephone conversations","date":"2018-12-21","arxiv_id":"1812.09321","repositories_listed":0,"syntology":null},{"url":null,"slug":"persian-phonemes-recognition-using-ppnet","title":"The Recognition Of Persian Phonemes Using PPNet","date":"2018-12-17","arxiv_id":"1812.08600","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-similarity-computationally","title":"Measuring Similarity: Computationally Reproducing the Scholar's Interests","date":"2018-12-14","arxiv_id":"1812.05984","repositories_listed":0,"syntology":null},{"url":null,"slug":"clinical-document-classification-using","title":"Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals","date":"2018-12-03","arxiv_id":"1812.00677","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-labeling-by-word-embeddings-and","title":"Cluster Labeling by Word Embeddings and WordNet's Hypernymy","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"document-classification-using-a-bi-lstm-to","title":"Document classification using a Bi-LSTM to unclog Brazil's supreme court","date":"2018-11-27","arxiv_id":"1811.11569","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-term-blurring-and-stochastic","title":"Semantic Term \"Blurring\" and Stochastic \"Barcoding\" for Improved Unsupervised Text Classification","date":"2018-11-06","arxiv_id":"1811.02456","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutions-are-all-you-need-for-classifying","title":"Convolutions Are All You Need (For Classifying Character Sequences)","date":"2018-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"when-does-deep-multi-task-learning-work-for","title":"When does deep multi-task learning work for loosely related document classification tasks?","date":"2018-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-learning-across-domains-with-1","title":"Variational learning across domains with triplet information","date":"2018-10-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-topic-models-with-latent-feature","title":"Improving Topic Models with Latent Feature Word Representations","date":"2018-10-15","arxiv_id":"1810.06306","repositories_listed":0,"syntology":null},{"url":null,"slug":"labeled-anchors-and-a-scalable-transparent","title":"Labeled Anchors and a Scalable, Transparent, and Interactive Classifier","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"language-independent-sentiment-analysis-with","title":"Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"streaming-word-similarity-mining-on-the-cheap","title":"Streaming word similarity mining on the cheap","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uzhsmm4h-system-descriptions","title":"UZH@SMM4H: System Descriptions","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-document-representation-using","title":"Unsupervised Document Representation using Partition Word-Vectors Averaging","date":"2018-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-analysis-of-hierarchical-text","title":"An Analysis of Hierarchical Text Classification Using Word Embeddings","date":"2018-09-06","arxiv_id":"1809.01771","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaussian-word-embedding-with-a-wasserstein","title":"Gaussian Word Embedding with a Wasserstein Distance Loss","date":"2018-08-21","arxiv_id":"1808.07016","repositories_listed":0,"syntology":null},{"url":null,"slug":"linked-recurrent-neural-networks","title":"Linked Recurrent Neural Networks","date":"2018-08-19","arxiv_id":"1808.06170","repositories_listed":0,"syntology":null},{"url":null,"slug":"paying-attention-to-attention-highlighting","title":"Paying Attention to Attention: Highlighting Influential Samples in Sequential Analysis","date":"2018-08-06","arxiv_id":"1808.02113","repositories_listed":0,"syntology":null},{"url":null,"slug":"ta14bingen-oslo-team-at-the-vardial-2018","title":"T\\\"ubingen-Oslo Team at the VarDial 2018 Evaluation Campaign: An Analysis of N-gram Features in Language Variety Identification","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-machine-translation-framework-based","title":"NMT-based Cross-lingual Document Embeddings","date":"2018-07-29","arxiv_id":"1807.11057","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-reconstructive-preserving","title":"Multi-view Reconstructive Preserving Embedding for Dimension Reduction","date":"2018-07-25","arxiv_id":"1807.10614","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-multi-task-approach-to-learning","title":"A Multi-task Approach to Learning Multilingual Representations","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"batch-is-not-heavy-learning-word","title":"Batch IS NOT Heavy: Learning Word Representations From All Samples","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-representations-of-named","title":"Comparison of Representations of Named Entities for Document Classification","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"harrigt-a-tool-for-linking-news-to-science","title":"HarriGT: A Tool for Linking News to Science","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-convolutional-attention-networks","title":"Hierarchical Convolutional Attention Networks for Text Classification","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-longer-term-dependencies-in-rnns-1","title":"Learning Longer-term Dependencies in RNNs with Auxiliary Losses","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-glance-reading-model-for-text","title":"Multi-glance Reading Model for Text Understanding","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multilingual-seq2seq-training-with-similarity","title":"Multilingual Seq2seq Training with Similarity Loss for Cross-Lingual Document Classification","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-accuracy-on-large-datasets-from","title":"Predicting accuracy on large datasets from smaller pilot data","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"paragraph-based-complex-networks-application","title":"Paragraph-based complex networks: application to document classification and authenticity verification","date":"2018-06-22","arxiv_id":"1806.08467","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-learning-across-domains-with","title":"Variational learning across domains with triplet information","date":"2018-06-22","arxiv_id":"1806.08672","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparison-of-two-paraphrase-models-for","title":"A Comparison of Two Paraphrase Models for Taxonomy Augmentation","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"systemt-declarative-text-understanding-for","title":"SystemT: Declarative Text Understanding for Enterprise","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ta14bingen-oslo-at-semeval-2018-task-2-svms","title":"T\\\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-leveled-reading-corpus-of-modern-standard","title":"A Leveled Reading Corpus of Modern Standard Arabic","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"author-profiling-from-facebook-corpora","title":"Author Profiling from Facebook Corpora","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"can-domain-adaptation-be-handled-as-analogies","title":"Can Domain Adaptation be Handled as Analogies?","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"kit-multi-a-translation-oriented-multilingual","title":"KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-vector-representation-of-utterances-in","title":"On the Vector Representation of Utterances in Dialogue Context","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"supervising-unsupervised-learning-with","title":"Supervising Unsupervised Learning with Evolutionary Algorithm in Deep Neural Network","date":"2018-03-28","arxiv_id":"1803.10397","repositories_listed":0,"syntology":null},{"url":null,"slug":"admm-based-networked-stochastic-variational","title":"ADMM-based Networked Stochastic Variational Inference","date":"2018-02-27","arxiv_id":"1802.10168","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-topic-models-by-neighborhood","title":"Learning Topic Models by Neighborhood Aggregation","date":"2018-02-22","arxiv_id":"1802.08012","repositories_listed":0,"syntology":null},{"url":null,"slug":"ntmaldetect-a-machine-learning-approach-to","title":"NtMalDetect: A Machine Learning Approach to Malware Detection Using Native API System Calls","date":"2018-02-15","arxiv_id":"1802.05412","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-classification-using-distributed","title":"Document Classification Using Distributed Machine Learning","date":"2018-02-10","arxiv_id":"1802.03597","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-dropout-with-rademacher-complexity","title":"Adaptive Dropout with Rademacher Complexity Regularization","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"public-apologies-in-india-semantics-sentiment","title":"Public Apologies in India - Semantics, Sentiment and Emotion","date":"2018-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iitp-at-ijcnlp-2017-task-4-auto-analysis-of","title":"IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-linguistic-resources-for-improving","title":"Leveraging Linguistic Resources for Improving Neural Text Classification","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quality-efficiency-trade-offs-in-machine","title":"Quality-Efficiency Trade-offs in Machine Learning for Text Processing","date":"2017-11-07","arxiv_id":"1711.02295","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-document-image-classification-using","title":"Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines","date":"2017-11-03","arxiv_id":"1711.05862","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-categorization-of-tagalog-documents","title":"Automatic Categorization of Tagalog Documents Using Support Vector Machines","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-convolutional-networks-for","title":"Graph Convolutional Networks for Classification with a Structured Label Space","date":"2017-10-12","arxiv_id":"1710.04908","repositories_listed":0,"syntology":null},{"url":null,"slug":"czech-text-document-corpus-v-20","title":"Czech Text Document Corpus v 2.0","date":"2017-10-06","arxiv_id":"1710.02365","repositories_listed":0,"syntology":null},{"url":null,"slug":"document-retrieval-and-question-answering-in","title":"Document retrieval and question answering in medical documents. A large-scale corpus challenge.","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-morphological-regularities-in","title":"Exploiting Morphological Regularities in Distributional Word Representations","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extractive-summarization-using-multi-task","title":"Extractive Summarization Using Multi-Task Learning with Document Classification","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grasp-rich-patterns-for-argumentation-mining","title":"GRASP: Rich Patterns for Argumentation Mining","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"human-centered-nlp-with-user-factor","title":"Human Centered NLP with User-Factor Adaptation","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"nite-a-neural-inductive-teaching-framework","title":"NITE: A Neural Inductive Teaching Framework for Domain Specific NER","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":"vecshare-a-framework-for-sharing-word","title":"VecShare: A Framework for Sharing Word Representation Vectors","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"7dc56d584b9fadb2c5541ed7bfda85813b3ae09ebe496d63da94230dc2222d08","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}