Классификатор BERT

method ID: bertclassifier

Related Papers

Contextualizing Hate Speech Classifiers with Post-hoc Explanation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractHate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like “gay” or “black” are used in offensive or prejudiced ways. Such biases manifest in false positives …...

37506

Detecting Stance in Media On Global Warming

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractCiting opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, “Leading scientists agree that global warming is a serious concern,” framing a clause …...

32184

Hitachi at SemEval-2020 Task 12: Offensive Language Identification with Noisy Labels Using Statistical Sampling and Post-Processing

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractIn this paper, we present our participation in SemEval-2020 Task-12 Subtask-A (English Language) which focuses on offensive language identification from noisy labels. To this end, we developed a hybrid system …...

24406

Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering

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Publication: TIPSTER (2020)

DOI: nan

AbstractDespite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to learn from. To address this, …...

31274

Making Information Seeking Easier: An Improved Pipeline for Conversational Search

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThis paper presents a highly effective pipeline for passage retrieval in a conversational search setting. The pipeline comprises of two components: Conversational Term Selection (CTS) and Multi-View Reranking (MVR). CTS …...

32130

Utilizing Bert for Question Retrieval on Vietnameses E-commerce Sites

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

Question retrieval is an important task in ques- tion answering. This task is considered to be challenging due to the lexical gap issue, i.e., similar questions could be expressed in …...

18892

A Corpus for Dimensional Sentiment Classification on YouTube Streaming Service

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Publication: TIPSTER (2021)

DOI: nan

AbstractThe streaming service platform such as YouTube provides a discussion function for audiences worldwide to share comments. YouTubers who upload videos to the YouTube platform want to track the performance …...

1045

UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions

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Publication: TIPSTER (2021)

DOI: nan

AbstractWe propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications. To identify the most important …...

25705

Classifying Implant-Bearing Patients via their Medical Histories: a Pre-Study on Swedish EMRs with Semi-Supervised GanBERT

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Publication: TIPSTER (2022)

DOI: nan

AbstractIn this paper, we compare the performance of two BERT-based text classifiers whose task is to classify patients (more precisely, their medical histories) as having or not having implant(s) in …...

14598

SCU-MESCLab at ROCLING-2022 Shared Task: Named Entity Recognition Using BERT Classifier

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Publication: TIPSTER (2022)

DOI: nan

AbstractIn this study, named entity recognition is constructed and applied in the medical domain. Data is labeled in BIO format. For example, “muscle” would be labeled “B-BODY” and “I-BODY”, and …...

995

FMI-SU at SemEval-2023 Task 7: Two-level Entailment Classification of Clinical Trials Enhanced by Contextual Data Augmentation

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Publication: TIPSTER (2023)

DOI: nan

AbstractThe paper presents an approach for solving SemEval 2023 Task 7 - identifying the inference relation in a clinical trials dataset. The system has two levels for retrieving relevant clinical …...

24771