Papers Token Classification
“Token Classification” 태그가 달린 논문 99편 · 필터 해제
Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter
The widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news. The current vogue is to employ manual fact-checkers to efficiently classify a…
Misinformationtoken-classificationToken ClassificationShort Text Pre-training with Extended Token Classification for E-commerce Query Understanding
E-commerce query understanding is the process of inferring the shopping intent of customers by extracting semantic meaning from their search queries. The recent progress of pre-trained masked language models (MLM) in nat…
token-classificationToken ClassificationDetecting Label Errors in Token Classification Data
Mislabeled examples are a common issue in real-world data, particularly for tasks like token classification where many labels must be chosen on a fine-grained basis. Here we consider the task of finding sentences that co…
General ClassificationToken ClassificationToken Classification for Disambiguating Medical Abbreviations
Abbreviations are unavoidable yet critical parts of the medical text. Using abbreviations, especially in clinical patient notes, can save time and space, protect sensitive information, and help avoid repetitions. However…
Classificationtext-classificationText Classificationtoken-classification+1John_Snow_Labs@SMM4H’22: Social Media Mining for Health (#SMM4H) with Spark NLP
Social media has become a major source of information for healthcare professionals but due to the growing volume of data in unstructured format, analyzing these resources accurately has become a challenge. In this study,…
ClassificationGPUNERtext-classification+3Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors
Current deep learning models trained to generate radiology reports from chest radiographs are capable of producing clinically accurate, clear, and actionable text that can advance patient care. However, such systems all …
token-classificationToken ClassificationTradeoffs in Resampling and Filtering for Imbalanced Classification
Imbalanced classification problems are extremely common in natural language processing and are solved using a variety of resampling and filtering techniques, which often involve making decisions on how to select training…
Classificationimbalanced classificationRetrievaltoken-classification+1Leveraging Three Types of Embeddings from Masked Language Models in Idiom Token Classification
Many linguistic expressions have idiomatic and literal interpretations, and the automatic distinction of these two interpretations has been studied for decades. Recent research has shown that contextualized word embeddin…
Classificationtoken-classificationToken ClassificationWord EmbeddingsNamedEntityRangers at SemEval-2022 Task 11: Transformer-based Approaches for Multilingual Complex Named Entity Recognition
This paper presents the two submissions of NamedEntityRangers Team to the MultiCoNER Shared Task, hosted at SemEval-2022. We evaluate two state-of-the-art approaches, of which both utilize pre-trained multi-lingual langu…
Decodernamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3Utilizing coarse-grained data in low-data settings for event extraction
Annotating text data for event information extraction systems is hard, expensive, and error-prone. We investigate the feasibility of integrating coarse-grained data (document or sentence labels), which is far more feasib…
Binary ClassificationClassificationEvent ExtractionSentence+2Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification
Certainty calibration is an important goal on the path to interpretability and trustworthy AI. Particularly in the context of human-in-the-loop systems, high-quality low to mid-range certainty estimates are essential. In…
NERtoken-classificationToken ClassificationUM6P-CS at SemEval-2022 Task 11: Enhancing Multilingual and Code-Mixed Complex Named Entity Recognition via Pseudo Labels using Multilingual Transformer
Building real-world complex Named Entity Recognition (NER) systems is a challenging task. This is due to the complexity and ambiguity of named entities that appear in various contexts such as short input sentences, emerg…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+2General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining
The lack of label data is one of the significant bottlenecks for Chinese Spelling Check (CSC). Existing researches use the method of automatic generation by exploiting unlabeled data to expand the supervised corpus. Howe…
Domain Adaptationtoken-classificationToken ClassificationBERT got a Date: Introducing Transformers to Temporal Tagging
Temporal expressions in text play a significant role in language understanding, and correctly identifying them is fundamental to various retrieval and natural language processing systems. Previous works have slowly shift…
ClassificationDecoderLanguage ModelingLanguage Modelling+4A Bit Bayesian Facilitates Efficient Training in Token Classification
Token classification is a fundamental subject matter in computational linguistics. Token classification models, like other modern deep neural network models, are usually trained on the entire training set in each epoch, …
Classificationtoken-classificationToken ClassificationECSpell$^{UD}$: Zero-shot Domain Adaptive Chinese Spelling Check with User Dictionary
Spellers often work within a particular domain in real life. Due to lots of uncommon domain terms, experiments on our built domain specific dataset show that general models perform terribly. Inspired by the common practi…
Domain Adaptationtoken-classificationToken ClassificationSeqAttack: On Adversarial Attacks for Named Entity Recognition
Named Entity Recognition is a fundamental task in information extraction and is an essential element for various Natural Language Processing pipelines. Adversarial attacks have been shown to greatly affect the performanc…
Classificationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4Sesame Street to Mount Sinai: BERT-constrained character-level Moses models for multilingual lexical normalization
This paper describes the HEL-LJU submissions to the MultiLexNorm shared task on multilingual lexical normalization. Our system is based on a BERT token classification preprocessing step, where for each token the type of …
Lexical Normalizationtoken-classificationToken ClassificationUoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction
Toxicity is pervasive in social media and poses a major threat to the health of online communities. The recent introduction of pre-trained language models, which have achieved state-of-the-art results in many NLP tasks, …
token-classificationToken ClassificationToxic Spans DetectionBERT got a Date: Introducing Transformers to Temporal Tagging
Temporal expressions in text play a significant role in language understanding and correctly identifying them is fundamental to various retrieval and natural language processing systems. Previous works have slowly shifte…
ClassificationDecoderLanguage ModelingLanguage Modelling+4