Entity linking is an important step towards constructing knowledge graphs
that facilitate advanced question answering over scientific documents,
including the retrieval of relevant information included in tables within these
documents. This paper introduces a general-purpose system for linking entities
to items in the Wikidata knowledge base. It describes how we adapt this system
for linking domain-specific entities, especially for those entities embedded
within tables drawn from COVID-19-related scientific literature. We describe
the setup of an efficient offline instance of the system that enables our
entity-linking approach to be more feasible in practice. As part of a broader
approach to infer the semantic meaning of scientific tables, we leverage the
structural and semantic characteristics of the tables to improve overall entity
linking performance.

本文介绍了一个通用的实体链接系统，说明了如何将此系统适应域特定的实体，特别是那些内嵌在 COVID-19 相关科学文献中的实体。通过利用表格的结构和语义特征来提高整体实体链接性能，进一步推断科学表格的语义含义。

科学文献表格实体链接实用系统

A Practical Entity Linking System for Tables in Scientific Literature

Entity linking (EL) is the task of linking a textual mention to its
corresponding entry in a knowledge base, and is critical for many
knowledge-intensive NLP applications. When applied to tables in scientific
papers, EL is a step toward large-scale scientific knowledge bases that could
enable advanced scientific question answering and analytics. We present the
first dataset for EL in scientific tables. EL for scientific tables is
especially challenging because scientific knowledge bases can be very
incomplete, and disambiguating table mentions typically requires understanding
the papers's tet in addition to the table. Our dataset, S2abEL, focuses on EL
in machine learning results tables and includes hand-labeled cell types,
attributed sources, and entity links from the PaperswithCode taxonomy for 8,429
cells from 732 tables. We introduce a neural baseline method designed for EL on
scientific tables containing many out-of-knowledge-base mentions, and show that
it significantly outperforms a state-of-the-art generic table EL method. The
best baselines fall below human performance, and our analysis highlights
avenues for improvement.

本研究介绍了一个用于科学表格中实体链接的数据集 S2abEL，利用神经网络方法对在科学表格中许多基于知识库之外的提及进行实体链接，并在机器学习结果表格中的 8,429 个单元格上展示了显著优于最先进的通用表 EL 方法的表现。

S2abEL：科学表格中的实体链接数据集

S2abEL: A Dataset for Entity Linking from Scientific Tables

In this paper, we introduce SciGen, a new challenge dataset for the task of
reasoning-aware data-to-text generation consisting of tables from scientific
articles and their corresponding descriptions. Describing scientific tables
goes beyond the surface realization of the table content and requires reasoning
over table values. The unique properties of SciGen are that (1) tables mostly
contain numerical values, and (2) the corresponding descriptions require
arithmetic reasoning. SciGen is therefore the first dataset that assesses the
arithmetic reasoning capabilities of generation models on complex input
structures, i.e., tables from scientific articles. We study the effectiveness
of state-of-the-art data-to-text generation models on SciGen and evaluate the
results using common metrics as well as human evaluation. Our results and
analyses show that (a) while humans like to reason for describing scientific
tables, the ability of state-of-the-art models is severely limited on this
task, (b) while adding more training data improves the results, it is not the
solution for reasoning-aware text generation, and (c) one of the main
bottlenecks for this task is the lack of proper automatic evaluation metrics.
The data, code, and annotations for human evaluation will be available at
this https URL SciGen opens new avenues for future research
in reasoning-aware text generation and evaluation.

本研究引入了 SciGen，一个考虑推理的数据到文本生成任务的数据集。SciGen 是第一个评估生成模型在复杂输入结构下算术推理能力的数据集，并表明表格描述需要考虑表格值的推理。在 SciGen 上进行实验表明，目前数据到文本生成模型的推理能力受限，缺乏适当的自动评估度量。