低资源命名实体识别的知识丰富提示

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-04-17 DOI:10.1145/3659948
Wenlong Hou, Weidong Zhao, Xianhui Liu, WenYan Guo
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引用次数: 0

摘要

低资源环境下的命名实体识别(NER)旨在利用有限的标记数据识别句子中的实体并对其进行分类。虽然基于提示的方法在低资源环境中取得了成功,但在有效利用信息和优化计算效率方面仍然存在挑战。在这项工作中,我们提出了一种新颖的基于提示的方法,无需详尽的模板调整即可增强低资源 NER。首先,我们通过整合具有代表性的实体和背景信息来构建知识丰富的提示,从而为每种实体类型提供量身定制的信息监督。然后,我们受 QA 的启发,引入了一个高效的反向生成框架,避免了冗余计算。最后,我们通过从实体类型生成实体来降低成本,同时保留模型推理能力。实验结果表明,我们的方法在三个数据集上的表现优于其他基线方法。
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Knowledge-Enriched Prompt for Low-Resource Named Entity Recognition

Named Entity Recognition (NER) in low-resource settings aims to identify and categorize entities in a sentence with limited labeled data. Although prompt-based methods have succeeded in low-resource perspectives, challenges persist in effectively harnessing information and optimizing computational efficiency. In this work, we present a novel prompt-based method to enhance low-resource NER without exhaustive template tuning. First, we construct knowledge-enriched prompts by integrating representative entities and background information to provide informative supervision tailored to each entity type. Then, we introduce an efficient reverse generative framework inspired by QA, which avoids redundant computations. Finally, We reduce costs by generating entities from their types while retaining model reasoning capacity. Experiment results demonstrate that our method outperforms other baselines on three datasets under few-shot settings.

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来源期刊
CiteScore
3.60
自引率
15.00%
发文量
241
期刊介绍: The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.
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