Entity linking models have achieved significant success via utilizing pretrained language models to capture semantic features.However, the NIL prediction problem, which aims to identify mentions without a corresponding entity in the knowledge base, has received insufficient attention.We categorize mentions linking to NIL into Missing Entity and Non-Entity Phrase, and propose an entity linking dataset NEL that focuses on the NIL prediction problem.NEL takes ambiguous entities as seeds, collects relevant mention context in the Wikipedia corpus, and ensures the presence of mentions linking to NIL by human annotation and entity masking.We conduct a series of experiments with the widely used bi-encoder and cross-encoder entity linking models, results show that both types of NIL mentions in training data have a significant influence on the accuracy of NIL prediction.Our code and dataset can be accessed at \url{https://github.com/solitaryzero/NIL_EL}.

<iframe src="https://app.sli.do/event/eX4PwSmcCc1EvCqjxcYdKB/embed/polls/6df3e623-d797-44a1-a4b8-6acf3fbd05bc" width="300" height="400"></iframe>

Learn to Not Link: Exploring NIL Prediction in Entity Linking

We investigate the usage of entity linking (EL) in downstream tasks and present the first modularized EL toolkit for easy task adaptation. Different from the existing EL methods that dealwith all the features simultaneously, we modularize the whole model into separate parts witheach feature. This decoupled design enables flexibly adding new features without retraining the whole model as well as flow visualization with better interpretability of the EL result. We release the corresponding toolkit, HOSMEL, for Chinese, with three flexible usage modes, a live demo, and a demonstration video. Experiments on two benchmarks for the question answering task demonstrate that HOSMEL achieves much less time and space consumption as well as significantly better accuracy performance compared with existing SOTA EL methods. We hope the release of HOSMEL will call for more attention to study EL for downstream tasks in non-English languages.

HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese

Entity Matching (EM) aims at recognizing entity records that denote the same real-world object. Neural EM models learn vector representation of entity descriptions and match entities end-to-end. Though robust, these methods require many annotated resources for training, and lack of interpretability. In this paper, we propose a novel EM framework that consists of Heterogeneous Information Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouple feature representation from matching decision. Using self-supervised learning and mask mechanism in pre-trained language modeling, HIF learns the embeddings of noisy attribute values by inter-attribute attention with unlabeled data. Using a set of comparison features and a limited amount of annotated data, KAT Induction learns an efficient decision tree that can be interpreted by generating entity matching rules whose structure is advocated by domain experts. Experiments on 6 public datasets and 3 industrial datasets show that our method is highly efficient and outperforms SOTA EM models in most cases. We will release the codes upon acceptance.

Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making

Findings papers without presentation slot

poster

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Posters: Demos

demo

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8A: Information Extraction #4

technical paper

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Jifan Yu

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Presentations

Learn to Not Link: Exploring NIL Prediction in Entity Linking

HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese

Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making

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