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Semeval-2013 task 9: Extraction of drug-drug interactions from biomedical texts (ddiextraction 2013).
- Proceedings of Semeval,
, 2013
"... Abstract The DDIExtraction 2013 task concerns the recognition of drugs and extraction of drugdrug interactions that appear in biomedical literature. We propose two subtasks for the DDIExtraction 2013 Shared Task challenge: 1) the recognition and classification of drug names and 2) the extraction an ..."
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Abstract The DDIExtraction 2013 task concerns the recognition of drugs and extraction of drugdrug interactions that appear in biomedical literature. We propose two subtasks for the DDIExtraction 2013 Shared Task challenge: 1) the recognition and classification of drug names and 2) the extraction and classification of their interactions. Both subtasks have been very successful in participation and results. There were 14 teams who submitted a total of 38 runs. The best result reported for the first subtask was F1 of 71.5% and 65.1% for the second one.
UWM-TRIADS: Classifying Drug-Drug Interactions with Two-Stage SVM and Post-Processing
"... We describe our system for the DDIExtraction-2013 shared task of classifying Drug-Drug interactions (DDIs) given labeled drug mentions. The challenge called for a five-way classification of all drug pairs in each sentence: a drug pair is either non-interacting, or interacting as one of four types. O ..."
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We describe our system for the DDIExtraction-2013 shared task of classifying Drug-Drug interactions (DDIs) given labeled drug mentions. The challenge called for a five-way classification of all drug pairs in each sentence: a drug pair is either non-interacting, or interacting as one of four types. Our approach begins with the use of a two-stage weighted SVM classifier to handle the highly unbalanced class distribution: the first stage for a binary classification of drug pairs as interacting or non-interacting, and the second stage for further classification of interacting pairs from the first stage into one of the four interacting types. Our SVM features exploit stemmed words, lemmas, bigrams, part of speech tags, verb lists, and similarity measures, among others. For each stage, we also developed a set of post-processing rules based on observations in the training data. Our best system achieved 0.472 F-measure. 1
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"... Data and text mining A novel feature-based approach to extract drug-drug interactions from biomedical text ..."
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Data and text mining A novel feature-based approach to extract drug-drug interactions from biomedical text
BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btu557 Data and text mining Advance Access publication August 20, 2014
, 2014
"... A novel feature-based approach to extract drug–drug interactions from biomedical text ..."
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A novel feature-based approach to extract drug–drug interactions from biomedical text
Database tool Egas: a collaborative and interactive document curation platform
"... With the overwhelming amount of biomedical textual information being produced, sev-eral manual curation efforts have been set up to extract and store concepts and their rela-tionships into structured resources. As manual annotation is a demanding and expensive task, computerized solutions were devel ..."
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With the overwhelming amount of biomedical textual information being produced, sev-eral manual curation efforts have been set up to extract and store concepts and their rela-tionships into structured resources. As manual annotation is a demanding and expensive task, computerized solutions were developed to perform such tasks automatically. However, high-end information extraction techniques are still not widely used by bio-medical research communities, mainly because of the lack of standards and limitations in usability. Interactive annotation tools intend to fill this gap, taking advantage of auto-matic techniques and existing knowledge bases to assist expert curators in their daily tasks. This article presents Egas, a web-based platform for biomedical text mining and assisted curation with highly usable interfaces for manual and automatic in-line annota-tion of concepts and relations. A comprehensive set of de facto standard knowledge bases are integrated and indexed to provide straightforward concept normalization fea-tures. Real-time collaboration and conversation functionalities allow discussing details of the annotation task as well as providing instant feedback of curator’s interactions. Egas
Biomedical entity extraction using machine-learning based approaches
"... Abstract In this paper, we present the experiments we made to process entities from the biomedical domain. Depending on the task to process, we used two distinct supervised machine-learning techniques: Conditional Random Fields to perform both named entity identification and classification, and Max ..."
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Abstract In this paper, we present the experiments we made to process entities from the biomedical domain. Depending on the task to process, we used two distinct supervised machine-learning techniques: Conditional Random Fields to perform both named entity identification and classification, and Maximum Entropy to classify given entities. Machine-learning approaches outperformed knowledge-based techniques on categories where sufficient annotated data was available. We showed that the use of external features (unsupervised clusters, information from ontology and taxonomy) improved the results significantly.
Large-scale information extraction for assisted curation of the biomedical literature
"... Abstract. PubMed, the main literature repository for the life sciences, contains more than 23 million publication references. In average nearly two publications per minute are added. There is a wealth of knowledge hidden in unstructed format in these publications that needs to be structured, linked ..."
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Abstract. PubMed, the main literature repository for the life sciences, contains more than 23 million publication references. In average nearly two publications per minute are added. There is a wealth of knowledge hidden in unstructed format in these publications that needs to be structured, linked, and semantically annotated so that it becomes actionable knowledge. We present an approach towards large-scale processing of biomedical literature in order to extract domain entities and semantic relationships among them. We describe some practical applications of the resulting knowledge base.
WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting
"... This work describes the participation of the WBI-DDI team on the SemEval 2013 – Task 9.2 DDI extraction challenge. The task consisted of extracting interactions between pairs of drugs from two collections of documents (DrugBank and MEDLINE) and their classification into four subtypes: advise, effect ..."
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This work describes the participation of the WBI-DDI team on the SemEval 2013 – Task 9.2 DDI extraction challenge. The task consisted of extracting interactions between pairs of drugs from two collections of documents (DrugBank and MEDLINE) and their classification into four subtypes: advise, effect, mechanism, and int. We developed a two-step approach in which pairs are initially extracted using ensembles of up to five different classifiers and then relabeled to one of the four categories. Our approach achieved the second rank in the DDI competition. For interaction detection we achieved F1 measures ranging from 73 % to almost 76 % depending on the run. These results are on par or even higher than the performance estimation on the training dataset. When considering the four interaction subtypes we achieved an F1 measure of 60.9 %. 1
Committee-based Selection of Weakly Labeled Instances for Learning Relation Extraction
"... Abstract. Manual annotation is a tedious and time consuming process, usually needed for generating training corpora to be used in a machine learning scenario. The distant supervision paradigm aims at automatically generating such corpora from structured data. The active learning paradigm aims at red ..."
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Abstract. Manual annotation is a tedious and time consuming process, usually needed for generating training corpora to be used in a machine learning scenario. The distant supervision paradigm aims at automatically generating such corpora from structured data. The active learning paradigm aims at reducing the effort needed for manual annotation. We explore active and distant learning approaches jointly to limit the amount of automatically generated data needed for the use case of relation extraction by increasing the quality of the annotations. The main idea of using distantly labeled corpora is that they can simplify and speed-up the generation of models, e. g. for extracting relationships between enti-ties of interest, while the selection of instances is typically performed randomly. We propose the use of query-by-committee to select instances instead. This ap-proach is similar to the active learning paradigm, with a difference that unlabeled instances are weakly annotated, rather than by human experts. Different strategies using low or high confidence are compared to random selection. Experiments on publicly available data sets for detection of protein-protein interactions show a statistically significant improvement in F1 measure when adding instances with a high agreement of the committee. 1
