64,044 research outputs found
Same but Different: Distant Supervision for Predicting and Understanding Entity Linking Difficulty
Entity Linking (EL) is the task of automatically identifying entity mentions
in a piece of text and resolving them to a corresponding entity in a reference
knowledge base like Wikipedia. There is a large number of EL tools available
for different types of documents and domains, yet EL remains a challenging task
where the lack of precision on particularly ambiguous mentions often spoils the
usefulness of automated disambiguation results in real applications. A priori
approximations of the difficulty to link a particular entity mention can
facilitate flagging of critical cases as part of semi-automated EL systems,
while detecting latent factors that affect the EL performance, like
corpus-specific features, can provide insights on how to improve a system based
on the special characteristics of the underlying corpus. In this paper, we
first introduce a consensus-based method to generate difficulty labels for
entity mentions on arbitrary corpora. The difficulty labels are then exploited
as training data for a supervised classification task able to predict the EL
difficulty of entity mentions using a variety of features. Experiments over a
corpus of news articles show that EL difficulty can be estimated with high
accuracy, revealing also latent features that affect EL performance. Finally,
evaluation results demonstrate the effectiveness of the proposed method to
inform semi-automated EL pipelines.Comment: Preprint of paper accepted for publication in the 34th ACM/SIGAPP
Symposium On Applied Computing (SAC 2019
Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition
Traditional language models are unable to efficiently model entity names
observed in text. All but the most popular named entities appear infrequently
in text providing insufficient context. Recent efforts have recognized that
context can be generalized between entity names that share the same type (e.g.,
\emph{person} or \emph{location}) and have equipped language models with access
to an external knowledge base (KB). Our Knowledge-Augmented Language Model
(KALM) continues this line of work by augmenting a traditional model with a KB.
Unlike previous methods, however, we train with an end-to-end predictive
objective optimizing the perplexity of text. We do not require any additional
information such as named entity tags. In addition to improving language
modeling performance, KALM learns to recognize named entities in an entirely
unsupervised way by using entity type information latent in the model. On a
Named Entity Recognition (NER) task, KALM achieves performance comparable with
state-of-the-art supervised models. Our work demonstrates that named entities
(and possibly other types of world knowledge) can be modeled successfully using
predictive learning and training on large corpora of text without any
additional information.Comment: NAACL 2019; updated to cite Zhou et al. (2018) EMNLP as a piece of
related wor
Large-scale event extraction from literature with multi-level gene normalization
Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons -Attribution - Share Alike (CC BY-SA) license
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