{"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/sentence-classification/papers/3","list_of":"/task/sentence-classification","task":"Sentence 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":3,"pages_in_order":4,"rows_per_page":100,"rows":[201,300],"of":303,"counts":{"archive_papers_tagged":303,"with_a_code_link":115,"where_syntology_ran_a_sample":22,"not_listed_spam_title":0,"listed":303,"listed_where_code_ran":22,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":16,"every_run_a_failure_of_syntologys_instrument":6,"listed_with_a_run_with_no_instrument_failure":16,"listed_every_run_a_failure_of_syntologys_instrument":6,"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/sentence-classification","prev":"/task/sentence-classification/papers/2","next":"/task/sentence-classification/papers/4","papers":[{"url":null,"slug":"enhancing-answer-boundary-detection-for","title":"Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension","date":"2020-04-29","arxiv_id":"2004.14069","repositories_listed":0,"syntology":null},{"url":null,"slug":"integrated-eojeol-embedding-for-erroneous","title":"Integrated Eojeol Embedding for Erroneous Sentence Classification in Korean Chatbots","date":"2020-04-13","arxiv_id":"2004.05744","repositories_listed":0,"syntology":null},{"url":"/paper/trans-blstm-transformer-with-bidirectional","slug":"trans-blstm-transformer-with-bidirectional","title":"TRANS-BLSTM: Transformer with Bidirectional LSTM for Language Understanding","date":"2020-03-16","arxiv_id":"2003.07000","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-data-programming-for-expanding-text","title":"Iterative Data Programming for Expanding Text Classification Corpora","date":"2020-02-04","arxiv_id":"2002.01412","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-factors-affecting","title":"An Empirical Study of Factors Affecting Language-Independent Models","date":"2019-12-30","arxiv_id":"1912.13106","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-robustness-of-task-oriented-dialog","title":"Improving Robustness of Task Oriented Dialog Systems","date":"2019-11-12","arxiv_id":"1911.05153","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-sensitive-bert-for-generalisable","title":"Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data","date":"2019-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-network-structure-for-modeling","title":"Interpretable Network Structure for Modeling Contextual Dependency","date":"2019-09-25","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recognition-of-handwritten-digit-using","title":"Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers","date":"2019-09-12","arxiv_id":"1909.08490","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrofitting-contextualized-word-embeddings","title":"Retrofitting Contextualized Word Embeddings with Paraphrases","date":"2019-09-12","arxiv_id":"1909.09700","repositories_listed":0,"syntology":null},{"url":"/paper/align-mask-and-select-a-simple-method-for","slug":"align-mask-and-select-a-simple-method-for","title":"Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models","date":"2019-08-19","arxiv_id":"1908.06725","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-sensitivity-analysis-of-attention-gated","title":"A Sensitivity Analysis of Attention-Gated Convolutional Neural Networks for Sentence Classification","date":"2019-08-17","arxiv_id":"1908.06263","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-self-supervised-learning-for","title":"Multi-Task Self-Supervised Learning for Disfluency Detection","date":"2019-08-15","arxiv_id":"1908.05378","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-word-embeddings-using-kernel-pca","title":"Improving Word Embeddings Using Kernel PCA","date":"2019-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-temporal-sequence-learning-for-action","title":"A Temporal Sequence Learning for Action Recognition and Prediction","date":"2019-06-17","arxiv_id":"1906.06813","repositories_listed":0,"syntology":null},{"url":null,"slug":"speak-up-fight-back-detection-of-social-media","title":"Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"team-xenophilius-lovegood-at-semeval-2019","title":"Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks","date":"2019-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"human-vs-muppet-a-conservative-estimate-of","title":"Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark","date":"2019-05-24","arxiv_id":"1905.10425","repositories_listed":0,"syntology":null},{"url":null,"slug":"emerald-110k-a-multidisciplinary-dataset-for","title":"Emerald 110k: A Multidisciplinary Dataset for Abstract Sentence Classification","date":"2019-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"transfer-learning-for-sequences-via-learning","title":"Transfer Learning for Sequences via Learning to Collocate","date":"2019-02-25","arxiv_id":"1902.09092","repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-transition-matrix-an-efficient","title":"Sentence transition matrix: An efficient approach that preserves sentence semantics","date":"2019-01-16","arxiv_id":"1901.05219","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-a-terminology-extraction-system-for","title":"TEST: A Terminology Extraction System for Technology Related Terms","date":"2018-12-22","arxiv_id":"1812.09541","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-medical-short-text-classification","title":"Improving Medical Short Text Classification with Semantic Expansion Using Word-Cluster Embedding","date":"2018-12-05","arxiv_id":"1812.01885","repositories_listed":0,"syntology":null},{"url":null,"slug":"syllables-for-sentence-classification-in","title":"Syllables for Sentence Classification in Morphologically Rich Languages","date":"2018-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-encoding-with-tree-constrained","title":"Sentence Encoding with Tree-constrained Relation Networks","date":"2018-11-26","arxiv_id":"1811.10475","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-embedding-compression-for-text","title":"Online Embedding Compression for Text Classification using Low Rank Matrix Factorization","date":"2018-11-01","arxiv_id":"1811.00641","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-confidence-of-neural-network","title":"On the Confidence of Neural Network Predictions for some NLP Tasks","date":"2018-10-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-neural-network-models","title":"A Comparative Study of Neural Network Models for Sentence Classification","date":"2018-10-03","arxiv_id":"1810.01656","repositories_listed":0,"syntology":null},{"url":null,"slug":"disney-at-iest-2018-predicting-emotions-using","title":"Disney at IEST 2018: Predicting Emotions using an Ensemble","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-and-learning-suicidal-ideation","title":"Exploring and Learning Suicidal Ideation Connotations on Social Media with Deep Learning","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-multitask-learning-for-simile","title":"Neural Multitask Learning for Simile Recognition","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"phrase-level-self-attention-networks-for","title":"Phrase-level Self-Attention Networks for Universal Sentence Encoding","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"thumbs-up-and-down-sentiment-analysis-of","title":"Thumbs Up and Down: Sentiment Analysis of Medical Online Forums","date":"2018-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-multi-task-word-embeddings","title":"Meta-Embedding as Auxiliary Task Regularization","date":"2018-09-16","arxiv_id":"1809.05886","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-deep-neural-network-sentence-level","title":"A Deep Neural Network Sentence Level Classification Method with Context Information","date":"2018-08-31","arxiv_id":"1809.00934","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-compose-over-tree-structures-via","title":"Learning to Compose over Tree Structures via POS Tags","date":"2018-08-18","arxiv_id":"1808.06075","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-architectures-for-arabic","title":"Neural Network Architectures for Arabic Dialect Identification","date":"2018-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"emotionx-dlc-self-attentive-bilstm-for","title":"EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentence-classification-for-investment-rules","title":"Sentence Classification for Investment Rules Detection","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-you-can-cram-into-a-single-vector-1","title":"What you can cram into a single \\$\\&!\\#* vector: Probing sentence embeddings for linguistic properties","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embeddings-based-uncertainty-detection","title":"Word Embeddings-Based Uncertainty Detection in Financial Disclosures","date":"2018-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"attr2vec-jointly-learning-word-and-contextual","title":"attr2vec: Jointly Learning Word and Contextual Attribute Embeddings with Factorization Machines","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cnns-for-nlp-in-the-browser-client-side","title":"CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pay-per-request-deployment-of-neural-network","title":"Pay-Per-Request Deployment of Neural Network Models Using Serverless Architectures","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"syntactically-aware-neural-architectures-for","title":"Syntactically Aware Neural Architectures for Definition Extraction","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"teamdl-at-semeval-2018-task-8-cybersecurity","title":"TeamDL at SemEval-2018 Task 8: Cybersecurity Text Analysis using Convolutional Neural Network and Conditional Random Fields","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"uc3m-nii-team-at-semeval-2018-task-7-semantic","title":"UC3M-NII Team at SemEval-2018 Task 7: Semantic Relation Classification in Scientific Papers via Convolutional Neural Network","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-hedge-detection-to-improve-committed","title":"Using Hedge Detection to Improve Committed Belief Tagging","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"zewen-at-semeval-2018-task-1-an-ensemble","title":"Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets","date":"2018-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-inspired-complex-word-embedding","title":"Quantum-inspired Complex Word Embedding","date":"2018-05-29","arxiv_id":"1805.11351","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-for-implicit-discourse-relation","title":"Attention for Implicit Discourse Relation Recognition","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-domain-specific-word-embeddings","title":"Evaluation of Domain-specific Word Embeddings using Knowledge Resources","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"l-optimisation-du-plongement-de-mots-pour-le","title":"L'optimisation du plongement de mots pour le fran\\ccais : une application de la classification des phrases (Optimization of Word Embeddings for French : an Application of Sentence Classification)","date":"2018-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-does-a-textcnn-learn","title":"What Does a TextCNN Learn?","date":"2018-01-19","arxiv_id":"1801.06287","repositories_listed":0,"syntology":null},{"url":null,"slug":"any-gram-kernels-for-sentence-classification","title":"Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study","date":"2017-12-19","arxiv_id":"1712.07004","repositories_listed":0,"syntology":null},{"url":null,"slug":"mining-supervisor-evaluation-and-peer","title":"Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals","date":"2017-12-04","arxiv_id":"1712.00991","repositories_listed":0,"syntology":null},{"url":null,"slug":"joint-sentence-document-model-for-manifesto","title":"Joint Sentence-Document Model for Manifesto Text Analysis","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sentinlp-at-ijcnlp-2017-task-4-customer","title":"SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model","date":"2017-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-lower-bounds-on-number-of-dimensions","title":"Towards Lower Bounds on Number of Dimensions for Word Embeddings","date":"2017-11-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inducing-distant-supervision-in-suggestion","title":"Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings","date":"2017-09-21","arxiv_id":"1709.07403","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-where-to-focus-in-reading","title":"Identifying Where to Focus in Reading Comprehension for Neural Question Generation","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":"using-context-information-for-dialog-act","title":"Using Context Information for Dialog Act Classification in DNN Framework","date":"2017-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-semantic-clause-types-modeling","title":"Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-models-for-multiword-expression","title":"Deep Learning Models For Multiword Expression Identification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"eica-at-semeval-2017-task-4-a-simple","title":"EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"labda-at-semeval-2017-task-10-relation","title":"LABDA at SemEval-2017 Task 10: Relation Classification between keyphrases via Convolutional Neural Network","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"lia-at-semeval-2017-task-4-an-ensemble-of","title":"LIA at SemEval-2017 Task 4: An Ensemble of Neural Networks for Sentiment Classification","date":"2017-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-cnn-rnn-alignment-model-for-phrase","title":"A Hybrid CNN-RNN Alignment Model for Phrase-Aware Sentence Classification","date":"2017-04-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"coarse-semantic-classification-of-rare-nouns","title":"Coarse Semantic Classification of Rare Nouns Using Cross-Lingual Data and Recurrent Neural Networks","date":"2017-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-neural-network-for-information","title":"A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"asm-kernel-graph-kernel-using-approximate","title":"ASM Kernel: Graph Kernel using Approximate Subgraph Matching for Relation Extraction","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"borrow-a-little-from-your-rich-cousin-using","title":"Borrow a Little from your Rich Cousin: Using Embeddings and Polarities of English Words for Multilingual Sentiment Classification","date":"2016-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"word-embeddings-and-their-use-in-sentence","title":"Word Embeddings and Their Use In Sentence Classification Tasks","date":"2016-10-26","arxiv_id":"1610.08229","repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-evaluation-of-rnn-architectures-on","title":"Empirical Evaluation of RNN Architectures on Sentence Classification Task","date":"2016-09-29","arxiv_id":"1609.09171","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-suggestions-in-opinionated-texts","title":"A Study of Suggestions in Opinionated Texts and their Automatic Detection","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-cross-lingual-model-for-sentence","title":"An Efficient Cross-lingual Model for Sentence Classification Using Convolutional Neural Network","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-semantically-coherent-and-reusable","title":"Learning Semantically Coherent and Reusable Kernels in Convolution Neural Nets for Sentence Classification","date":"2016-08-01","arxiv_id":"1608.00466","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-the-combination-of-generic-and","title":"Modelling the Combination of Generic and Target Domain Embeddings in a Convolutional Neural Network for Sentence Classification","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-semi-supervised-query-classification","title":"Scalable Semi-Supervised Query Classification Using Matrix Sketching","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"suggestion-mining-from-opinionated-text","title":"Suggestion Mining from Opinionated Text","date":"2016-08-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dependency-based-embeddings-for-sentence","title":"Dependency Based Embeddings for Sentence Classification Tasks","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"pkudblab-at-semeval-2016-task-6-a-specific","title":"pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sensei-lif-at-semeval-2016-task-4-polarity","title":"SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ufal-at-semeval-2016-task-5-recurrent-neural","title":"UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification","date":"2016-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multichannel-variable-size-convolution-for","title":"Multichannel Variable-Size Convolution for Sentence Classification","date":"2016-03-15","arxiv_id":"1603.04513","repositories_listed":0,"syntology":null},{"url":null,"slug":"mgnc-cnn-a-simple-approach-to-exploiting","title":"MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification","date":"2016-03-03","arxiv_id":"1603.00968","repositories_listed":0,"syntology":null},{"url":null,"slug":"word-substitution-in-short-answer-extraction","title":"Word Substitution in Short Answer Extraction: A WordNet-based Approach","date":"2016-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"effectively-crowdsourcing-radiology-report","title":"Effectively Crowdsourcing Radiology Report Annotations","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-the-extraction-of-customer-to","title":"Towards the Extraction of Customer-to-Customer Suggestions from Reviews","date":"2015-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-decomposition-of-a-multi-author","title":"Unsupervised Decomposition of a Multi-Author Document Based on Naive-Bayesian Model","date":"2015-07-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-discoursive-structure-of-computer","title":"On the Discoursive Structure of Computer Graphics Research Papers","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unitn-training-deep-convolutional-neural","title":"UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification","date":"2015-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"extraction-of-pharmacokinetic-evidence-of","title":"Extraction of Pharmacokinetic Evidence of Drug-drug Interactions from the Literature","date":"2014-12-02","arxiv_id":"1412.0744","repositories_listed":0,"syntology":null},{"url":null,"slug":"handling-plurality-in-bengali-noun-phrases","title":"Handling Plurality in Bengali Noun Phrases","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-automated-method-to-build-a-corpus-of","title":"An automated method to build a corpus of rhetorically-classified sentences in biomedical texts","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"klognlp-graph-kernelbased-relational-learning","title":"kLogNLP: Graph Kernel--based Relational Learning of Natural Language","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grammar-based-lexicon-extension-for-aligning","title":"Grammar-Based Lexicon Extension for Aligning German Radiology Text and Images","date":"2013-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"does-similarity-matter-the-case-of-answer","title":"Does Similarity Matter? The Case of Answer Extraction from Technical Discussion Forums","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"experiments-with-clustering-based-features","title":"Experiments with Clustering-based Features for Sentence Classification in Medical Publications: Macquarie Test's participation in the ALTA 2012 shared task.","date":"2012-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"a100d128b198d607ea7c34bc6ce9b94c07b381ec198e85f4066b06844b398dc8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}