Label Semantics for Few Shot Named Entity Recognition

Ma, Jing; Ballesteros, Miguel; Doss, Srikanth; Anubhai, Rishita; Mallya, Sunil; Al-Onaizan, Yaser; Roth, Dan

doi:10.18653/v1/2022.findings-acl.155

Cited by 46 publications

(39 citation statements)

References 6 publications

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“…(2). FSLS (Ma et al, 2022): The state-of-the-art extractive-based method for few-shot NER task. Anonymous (2022) also validate its competitive performance on few-shot ED tasks.…”

Section: Small Language Modelsmentioning

confidence: 99%

Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

Ma¹,

Cao²,

Hong³

et al. 2023

Preprint

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Large Language Models (LLMs) have made remarkable strides in various tasks. However, whether they are competitive few-shot solvers for information extraction (IE) tasks and surpass fine-tuned small Pre-trained Language Models (SLMs) remains an open problem. This paper aims to provide a thorough answer to this problem, and moreover, to explore an approach towards effective and economical IE systems that combine the strengths of LLMs and SLMs. Through extensive experiments on eight datasets across three IE tasks, we show that LLMs are not effective few-shot information extractors in general, given their unsatisfactory performance in most settings and the high latency and budget requirements. However, we demonstrate that LLMs can well complement SLMs and effectively solve hard samples that SLMs struggle with. Building on these findings, we propose an adaptive filterthen-rerank paradigm, in which SLMs act as filters and LLMs act as rerankers. By utilizing LLMs to rerank a small portion of difficult samples identified by SLMs, our preliminary system consistently achieves promising improvements (2.1% F1-gain on average) on various IE tasks, with acceptable cost of time and money.

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“…(2). FSLS (Ma et al, 2022): The state-of-the-art extractive-based method for few-shot NER task. Anonymous (2022) also validate its competitive performance on few-shot ED tasks.…”

Section: Small Language Modelsmentioning

confidence: 99%

Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

Ma¹,

Cao²,

Hong³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…LEBERT [26] is integrating lexicon information into the underlying BERT layer and achieved the optimal results for Chinese sequence annotation. Ma et al [27] proposed to use label information to match entities in text and achieved good results on lowresource Chinese datasets.…”

Section: Baselinementioning

confidence: 99%

A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

2023

Teh. vjesn.

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At present, Named Entity Recognition (NER) is one of the fundamental tasks for extracting knowledge from traditional Chinese medicine (TCM) texts. The variability of the length of TCM entities and the characteristics of the language of TCM texts lead to ambiguity of TCM entity boundaries. In addition, better extracting and exploiting local features of text can improve the accuracy of named entity recognition. In this paper, we proposed a TCM NER model with lexicon information and text local feature enhancement of text. In this model, a lexicon is introduced to encode the characters in the text to obtain the context-sensitive global semantic representation of the text. The convolutional neural network (CNN) and gate joined collaborative attention network are used to form a text local feature extraction module to capture the important semantic features of local text. Experiments were conducted on two TCM domain datasets and the F1 values are 91.13% and 90.21% respectively.

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“…IE is a key component in supporting knowledge acquisition and it impacts a wide spectrum of knowledge-driven AI applications. We will conclude the tutorial by presenting further challenges and potential research topics in identifying trustworthiness of extracted content (Zhang et al, , 2020b, IE with quantitative reasoning (Elazar et al, 2019;, cross-document IE (Caciularu et al, 2021), incorporating domainspecific knowledge Zhang et al, 2021c), extension to knowledge reasoning and prediction, modeling of label semantics Mueller et al, 2022;Ma et al, 2022;Chen et al, 2020a), and challenges for acquiring implicit but essential information from corpora that potentially involve reporting bias (Sap et al, 2020).…”

Section: Future Research Directions [30min]mentioning

confidence: 99%

New Frontiers of Information Extraction

Chen¹,

Huang²,

Li³

et al. 2022

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

Self Cite

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This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources. In particular, this tutorial will provide audience with a systematic introduction to recent advances in IE, by addressing several important research questions. These questions include (i) how to develop a robust IE system from a small amount of noisy training data, while ensuring the reliability of its prediction? (ii) how to foster the generalizability of IE through enhancing the system's cross-lingual, crossdomain, cross-task and cross-modal transferability? (iii) how to support extracting structural information with extremely fine-grained and diverse labels? (iv) how to further improve IE by leveraging indirect supervision from other NLP tasks, such as Natural Language Generation (NLG), Natural Language Inference (NLI), Question Answering (QA) or summarization, and pre-trained language models? (v) how to acquire knowledge to guide inference in IE systems? We will discuss several lines of frontier research that tackle those challenges, and will conclude the tutorial by outlining directions for further investigation.

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Label Semantics for Few Shot Named Entity Recognition

Cited by 46 publications

References 6 publications

Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!

A Named Entity Recognition Method Enhanced with Lexicon Information and Text Local Feature

New Frontiers of Information Extraction

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