16,038 research outputs found
A survey on recent advances in named entity recognition
Named Entity Recognition seeks to extract substrings within a text that name
real-world objects and to determine their type (for example, whether they refer
to persons or organizations). In this survey, we first present an overview of
recent popular approaches, but we also look at graph- and transformer- based
methods including Large Language Models (LLMs) that have not had much coverage
in other surveys. Second, we focus on methods designed for datasets with scarce
annotations. Third, we evaluate the performance of the main NER implementations
on a variety of datasets with differing characteristics (as regards their
domain, their size, and their number of classes). We thus provide a deep
comparison of algorithms that are never considered together. Our experiments
shed some light on how the characteristics of datasets affect the behavior of
the methods that we compare.Comment: 30 page
Robust Subgraph Generation Improves Abstract Meaning Representation Parsing
The Abstract Meaning Representation (AMR) is a representation for open-domain
rich semantics, with potential use in fields like event extraction and machine
translation. Node generation, typically done using a simple dictionary lookup,
is currently an important limiting factor in AMR parsing. We propose a small
set of actions that derive AMR subgraphs by transformations on spans of text,
which allows for more robust learning of this stage. Our set of construction
actions generalize better than the previous approach, and can be learned with a
simple classifier. We improve on the previous state-of-the-art result for AMR
parsing, boosting end-to-end performance by 3 F on both the LDC2013E117 and
LDC2014T12 datasets.Comment: To appear in ACL 201
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