Sieve-Based Entity Linking for the Biomedical Domain

D’Souza, Jennifer; Ng, Vincent

doi:10.3115/v1/p15-2049

Cited by 71 publications

(56 citation statements)

References 9 publications

(9 reference statements)

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“…), coreference researches in this domain have received comparatively less attentions ( 19 ). Previous approaches had applied several methods, ranging from heuristics-based ( 20 , 21 ) to machine learning ( 22 , 23 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…In this regard, our proposed system employed the coreference module that was based on a multi-pass sieve model ( 21 ). It has been evaluated as a simple yet effective mean for disorder mention normalization ( 21 ). We first processed each abstract by noun phrase (NP) chunking (using Genia tagger; http://www.nactem.ac.uk/GENIA/tagger/ ) and then created a set of NPs pairs for each abstract.…”

Section: Methodsmentioning

confidence: 99%

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Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction

Le¹,

Tran²,

Dang³

et al. 2016

Database

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The BioCreative V chemical-disease relation (CDR) track was proposed to accelerate the progress of text mining in facilitating integrative understanding of chemicals, diseases and their relations. In this article, we describe an extension of our system (namely UET-CAM) that participated in the BioCreative V CDR. The original UET-CAM system’s performance was ranked fourth among 18 participating systems by the BioCreative CDR track committee. In the Disease Named Entity Recognition and Normalization (DNER) phase, our system employed joint inference (decoding) with a perceptron-based named entity recognizer (NER) and a back-off model with Semantic Supervised Indexing and Skip-gram for named entity normalization. In the chemical-induced disease (CID) relation extraction phase, we proposed a pipeline that includes a coreference resolution module and a Support Vector Machine relation extraction model. The former module utilized a multi-pass sieve to extend entity recall. In this article, the UET-CAM system was improved by adding a ‘silver’ CID corpus to train the prediction model. This silver standard corpus of more than 50 thousand sentences was automatically built based on the Comparative Toxicogenomics Database (CTD) database. We evaluated our method on the CDR test set. Results showed that our system could reach the state of the art performance with F1 of 82.44 for the DNER task and 58.90 for the CID task. Analysis demonstrated substantial benefits of both the multi-pass sieve coreference resolution method (F1 + 4.13%) and the silver CID corpus (F1 +7.3%).Database URL: SilverCID–The silver-standard corpus for CID relation extraction is freely online available at: https://zenodo.org/record/34530 (doi:10.5281/zenodo.34530).

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction

Le¹,

Tran²,

Dang³

et al. 2016

Database

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“…It was first proposed for coreference resolution [30] and then applied to other tasks such as temporal relations and quote attribution [31,32]. It has also been employed for various tasks in the biomedical domains, such as entity linking, coreference resolution, relation extraction, and concept normalization [33][34][35][36]. Our system can be seen as consisting of four sieves that capture different types of information: (1) context triggers, (2) short-range intra-sentence (i.e., intraclause) events, (3) long-range intra-sentence (i.e., crossclause) events, and (4) cross-sentence events.…”

Section: Multi-pass Sieve Architecturementioning

confidence: 99%

“…This means that it is also easy to assess the contribution of each sieve to the overall performance. This is particularly useful when supervised learning does not work well, as discussed in the section on related work [30][31][32][33][34][35][36].…”

Section: Multi-pass Sieve Architecturementioning

confidence: 99%

Unsupervised inference of implicit biomedical events using context triggers

2020

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Background: Event extraction from the biomedical literature is one of the most actively researched areas in biomedical text mining and natural language processing. However, most approaches have focused on events within single sentence boundaries, and have thus paid much less attention to events spanning multiple sentences. The Bacteria-Biotope event (BB-event) subtask presented in BioNLP Shared Task 2016 is one such example; a significant amount of relations between bacteria and biotope span more than one sentence, but existing systems have treated them as false negatives because labeled data is not sufficiently large enough to model a complex reasoning process using supervised learning frameworks. Results: We present an unsupervised method for inferring cross-sentence events by propagating intra-sentence information to adjacent sentences using context trigger expressions that strongly signal the implicit presence of entities of interest. Such expressions can be collected from a large amount of unlabeled plain text based on simple syntactic constraints, helping to overcome the limitation of relying only on a small number of training examples available. The experimental results demonstrate that our unsupervised system extracts cross-sentence events quite well and outperforms all the state-of-the-art supervised systems when combined with existing methods for intra-sentence event extraction. Moreover, our system is also found effective at detecting long-distance intra-sentence events, compared favorably with existing high-dimensional models such as deep neural networks, without any supervised learning techniques. Conclusions: Our linguistically motivated inference model is shown to be effective at detecting implicit events that have not been covered by previous work, without relying on training data or curated knowledge bases. Moreover, it also helps to boost the performance of existing systems by allowing them to detect additional cross-sentence events. We believe that the proposed model offers an effective way to infer implicit information beyond sentence boundaries, especially when human-annotated data is not sufficient enough to train a robust supervised system.

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Pattern-Based Acquisition of Scientific Entities from Scholarly Article Titles

D’Souza¹,

Auer²

2021

Lecture Notes in Computer Science

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Sieve-Based Entity Linking for the Biomedical Domain

Cited by 71 publications

References 9 publications

Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction

Sieve-based coreference resolution enhances semi-supervised learning model for chemical-induced disease relation extraction

Unsupervised inference of implicit biomedical events using context triggers

Pattern-Based Acquisition of Scientific Entities from Scholarly Article Titles

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