1,974 research outputs found
Biomedical Entity Recognition by Detection and Matching
Biomedical named entity recognition (BNER) serves as the foundation for
numerous biomedical text mining tasks. Unlike general NER, BNER require a
comprehensive grasp of the domain, and incorporating external knowledge beyond
training data poses a significant challenge. In this study, we propose a novel
BNER framework called DMNER. By leveraging existing entity representation
models SAPBERT, we tackle BNER as a two-step process: entity boundary detection
and biomedical entity matching. DMNER exhibits applicability across multiple
NER scenarios: 1) In supervised NER, we observe that DMNER effectively
rectifies the output of baseline NER models, thereby further enhancing
performance. 2) In distantly supervised NER, combining MRC and AutoNER as span
boundary detectors enables DMNER to achieve satisfactory results. 3) For
training NER by merging multiple datasets, we adopt a framework similar to
DS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the
training. Through extensive experiments conducted on 10 benchmark datasets, we
demonstrate the versatility and effectiveness of DMNER.Comment: 9 pages content, 2 pages appendi
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach
Named Entity Recognition (NER) aims to extract and classify entity mentions
in the text into pre-defined types (e.g., organization or person name).
Recently, many works have been proposed to shape the NER as a machine reading
comprehension problem (also termed MRC-based NER), in which entity recognition
is achieved by answering the formulated questions related to pre-defined entity
types through MRC, based on the contexts. However, these works ignore the label
dependencies among entity types, which are critical for precisely recognizing
named entities. In this paper, we propose to incorporate the label dependencies
among entity types into a multi-task learning framework for better MRC-based
NER. We decompose MRC-based NER into multiple tasks and use a self-attention
module to capture label dependencies. Comprehensive experiments on both nested
NER and flat NER datasets are conducted to validate the effectiveness of the
proposed Multi-NER. Experimental results show that Multi-NER can achieve better
performance on all datasets
MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding
Multimodal named entity recognition (MNER) is a critical step in information
extraction, which aims to detect entity spans and classify them to
corresponding entity types given a sentence-image pair. Existing methods either
(1) obtain named entities with coarse-grained visual clues from attention
mechanisms, or (2) first detect fine-grained visual regions with toolkits and
then recognize named entities. However, they suffer from improper alignment
between entity types and visual regions or error propagation in the two-stage
manner, which finally imports irrelevant visual information into texts. In this
paper, we propose a novel end-to-end framework named MNER-QG that can
simultaneously perform MRC-based multimodal named entity recognition and query
grounding. Specifically, with the assistance of queries, MNER-QG can provide
prior knowledge of entity types and visual regions, and further enhance
representations of both texts and images. To conduct the query grounding task,
we provide manual annotations and weak supervisions that are obtained via
training a highly flexible visual grounding model with transfer learning. We
conduct extensive experiments on two public MNER datasets, Twitter2015 and
Twitter2017. Experimental results show that MNER-QG outperforms the current
state-of-the-art models on the MNER task, and also improves the query grounding
performance.Comment: 13 pages, 6 figures, published to AAA
Mirror: A Universal Framework for Various Information Extraction Tasks
Sharing knowledge between information extraction tasks has always been a
challenge due to the diverse data formats and task variations. Meanwhile, this
divergence leads to information waste and increases difficulties in building
complex applications in real scenarios. Recent studies often formulate IE tasks
as a triplet extraction problem. However, such a paradigm does not support
multi-span and n-ary extraction, leading to weak versatility. To this end, we
reorganize IE problems into unified multi-slot tuples and propose a universal
framework for various IE tasks, namely Mirror. Specifically, we recast existing
IE tasks as a multi-span cyclic graph extraction problem and devise a
non-autoregressive graph decoding algorithm to extract all spans in a single
step. It is worth noting that this graph structure is incredibly versatile, and
it supports not only complex IE tasks, but also machine reading comprehension
and classification tasks. We manually construct a corpus containing 57 datasets
for model pretraining, and conduct experiments on 30 datasets across 8
downstream tasks. The experimental results demonstrate that our model has
decent compatibility and outperforms or reaches competitive performance with
SOTA systems under few-shot and zero-shot settings. The code, model weights,
and pretraining corpus are available at https://github.com/Spico197/Mirror .Comment: Accepted to EMNLP23 main conferenc
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
Most NER methods rely on extensive labeled data for model training, which
struggles in the low-resource scenarios with limited training data. Existing
dominant approaches usually suffer from the challenge that the target domain
has different label sets compared with a resource-rich source domain, which can
be concluded as class transfer and domain transfer. In this paper, we propose a
lightweight tuning paradigm for low-resource NER via pluggable prompting
(LightNER). Specifically, we construct the unified learnable verbalizer of
entity categories to generate the entity span sequence and entity categories
without any label-specific classifiers, thus addressing the class transfer
issue. We further propose a pluggable guidance module by incorporating
learnable parameters into the self-attention layer as guidance, which can
re-modulate the attention and adapt pre-trained weights. Note that we only tune
those inserted module with the whole parameter of the pre-trained language
model fixed, thus, making our approach lightweight and flexible for
low-resource scenarios and can better transfer knowledge across domains.
Experimental results show that LightNER can obtain comparable performance in
the standard supervised setting and outperform strong baselines in low-resource
settings. Code is in
https://github.com/zjunlp/DeepKE/tree/main/example/ner/few-shot.Comment: Accepted by COLING 202
From Clozing to Comprehending: Retrofitting Pre-trained Language Model to Pre-trained Machine Reader
We present Pre-trained Machine Reader (PMR), a novel method to retrofit
Pre-trained Language Models (PLMs) into Machine Reading Comprehension (MRC)
models without acquiring labeled data. PMR is capable of resolving the
discrepancy between model pre-training and downstream fine-tuning of existing
PLMs, and provides a unified solver for tackling various extraction tasks. To
achieve this, we construct a large volume of general-purpose and high-quality
MRC-style training data with the help of Wikipedia hyperlinks and design a Wiki
Anchor Extraction task to guide the MRC-style pre-training process. Although
conceptually simple, PMR is particularly effective in solving extraction tasks
including Extractive Question Answering and Named Entity Recognition, where it
shows tremendous improvements over previous approaches especially under
low-resource settings. Moreover, viewing sequence classification task as a
special case of extraction task in our MRC formulation, PMR is even capable to
extract high-quality rationales to explain the classification process,
providing more explainability of the predictions
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