Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition
In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.
Code (1)
Tasks
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Similar Papers 제목 키워드 기반
Word Embeddings vs Word Types for Sequence Labeling: the Curious Case of CV Parsing
Empirical Error Modeling Improves Robustness of Noisy Neural Sequence Labeling
Despite recent advances, standard sequence labeling systems often fail when processing noisy user-generated text or consuming the output of an Optical Character Recognition (OCR) process. In this paper, we improve the no…
Language ModelingLanguage ModellingOptical Character RecognitionOptical Character Recognition (OCR)+1Neural Semantic Role Labeling with Dependency Path Embeddings
This paper introduces a novel model for semantic role labeling that makes use of neural sequence modeling techniques. Our approach is motivated by the observation that complex syntactic structures and related phenomena, …
Semantic Role LabelingCo-Speech Gesture Detection through Multi-Phase Sequence Labeling
Gestures are integral components of face-to-face communication. They unfold over time, often following predictable movement phases of preparation, stroke, and retraction. Yet, the prevalent approach to automatic gesture …
Binary ClassificationEmpower Sequence Labeling with Task-Aware Neural Language Model
Linguistic sequence labeling is a general modeling approach that encompasses a variety of problems, such as part-of-speech tagging and named entity recognition. Recent advances in neural networks (NNs) make it possible t…
GPULanguage ModelingLanguage Modellingnamed-entity-recognition+6