SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

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Showing 5351–5400 of 14581 papers

TitleStatusHype
Towards the Data-driven System for Rhetorical Parsing of Russian Texts—0
Predicting Suicide Risk from Online Postings in Reddit The UGent-IDLab submission to the CLPysch 2019 Shared Task A—0
Reviving a psychometric measure: Classification and prediction of the Operant Motive Test—0
SC-UPB at the VarDial 2019 Evaluation Campaign: Moldavian vs. Romanian Cross-Dialect Topic Identification—0
Stylometric Classification of Ancient Greek Literary Texts by Genre—0
Modeling Behavioral Aspects of Social Media Discourse for Moral Classification—0
Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics—0
Nikolov-Radivchev at SemEval-2019 Task 6: Offensive Tweet Classification with BERT and Ensembles—0
T\"uKaSt at SemEval-2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification—0
Podlab at SemEval-2019 Task 3: The Importance of Being Shallow—0
Team Kit Kittredge at SemEval-2019 Task 4: LSTM Voting System—0
Rethinking Complex Neural Network Architectures for Document ClassificationCode0
UPV-28-UNITO at SemEval-2019 Task 7: Exploiting Post's Nesting and Syntax Information for Rumor Stance Classification—0
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations—0
TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks—0
USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings—0
NLP@UIOWA at SemEval-2019 Task 6: Classifying the Crass using Multi-windowed CNNs—0
UA at SemEval-2019 Task 5: Setting A Strong Linear Baseline for Hate Speech Detection—0
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN—0
LIRMM-Advanse at SemEval-2019 Task 3: Attentive Conversation Modeling for Emotion Detection and Classification—0
Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?Code0
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN—0
YNU\_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification—0
MineriaUNAM at SemEval-2019 Task 5: Detecting Hate Speech in Twitter using Multiple Features in a Combinatorial Framework—0
Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment—0
THU-HCSI at SemEval-2019 Task 3: Hierarchical Ensemble Classification of Contextual Emotion in Conversation—0
Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks—0
Saagie at Semeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text Classification—0
UNBNLP at SemEval-2019 Task 5 and 6: Using Language Models to Detect Hate Speech and Offensive Language—0
Spider-Jerusalem at SemEval-2019 Task 4: Hyperpartisan News Detection—0
Self-Discriminative Learning for Unsupervised Document Embedding—0
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions—0
Zeyad at SemEval-2019 Task 6: That's Offensive! An All-Out Search For An Ensemble To Identify And Categorize Offense in Tweets.—0
SINAI at SemEval-2019 Task 6: Incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social media—0
SNU IDS at SemEval-2019 Task 3: Addressing Training-Test Class Distribution Mismatch in Conversational ClassificationCode0
Rouletabille at SemEval-2019 Task 4: Neural Network Baseline for Identification of Hyperpartisan Publishers—0
SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations—0
The Strength of the Weakest Supervision: Topic Classification Using Class Labels—0
SSN-SPARKS at SemEval-2019 Task 9: Mining Suggestions from Online Reviews using Deep Learning Techniques on Augmented Data—0
TDBot at SemEval-2019 Task 3: Context Aware Emotion Detection Using A Conditioned Classification Approach—0
WUT at SemEval-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion Mining—0
Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author DemographicsCode0
Team Fernando-Pessa at SemEval-2019 Task 4: Back to Basics in Hyperpartisan News Detection—0
Practical, Efficient, and Customizable Active Learning for Named Entity Recognition in the Digital HumanitiesCode0
LTL-UDE at SemEval-2019 Task 6: BERT and Two-Vote Classification for Categorizing Offensiveness—0
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection—0
Team Howard Beale at SemEval-2019 Task 4: Hyperpartisan News Detection with BERT—0
Orwellian-times at SemEval-2019 Task 4: A Stylistic and Content-based Classifier—0
LT3 at SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (hatEval)—0
Vector of Locally Aggregated Embeddings for Text Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1KNNAccuracy93.1—Unverified
2RFAccuracy92.96—Unverified
3CARTAccuracy92.82—Unverified
4LDAAccuracy77.22—Unverified
#ModelMetricClaimedVerifiedStatus
1MONT3Accuracy100—Unverified
2XBNETAccuracy100—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy42—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy67.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy68.5—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy13.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy90.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MONT3Accuracy100—Unverified