pwc-archive/papers-with-abstracts huggingface dataset:
Macquarie University at BioASQ 5b -- Query-based Summarisation Techniques for Selecting the Ideal Answers
Authors: Diego Moll{\'a}
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Question Answering, regression

Macquarie University{'}s contribution to the BioASQ challenge (Task 5b Phase B) focused on the use of query-based extractive summarisation techniques for the generation of the ideal answers. Four runs were submitted, with approaches ranging from a trivial system that selected the first $n$ snippets, to the use of deep learning approaches under a regression framework. Our experiments and the ROUGE results of the five test batches of BioASQ indicate surprisingly good results for the trivial approach. Overall, most of our runs on the first three test batches achieved the best ROUGE-SU4 results in the challenge.
End-to-End System for Bacteria Habitat Extraction
Authors: Farrokh Mehryary, Kai Hakala, Suwisa Kaewphan, Jari Bj{\"o}rne, Tapio Salakoski, Filip Ginter
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Named Entity Recognition (NER), Relation, Relation Extraction

We introduce an end-to-end system capable of named-entity detection, normalization and relation extraction for extracting information about bacteria and their habitats from biomedical literature. Our system is based on deep learning, CRF classifiers and vector space models. We train and evaluate the system on the BioNLP 2016 Shared Task Bacteria Biotope data. The official evaluation shows that the joint performance of our entity detection and relation extraction models outperforms the winning team of the Shared Task by 19pp on F1-score, establishing a new top score for the task. We also achieve state-of-the-art results in the normalization task. Our system is open source and freely available at \url{https://github.com/TurkuNLP/BHE}.
Creation and evaluation of a dictionary-based tagger for virus species and proteins
Authors: Helen Cook, R{\=u}dolfs B{\=e}rzi{\c{n}}{\v{s}}, Cristina Leal Rodr{\i}guez, Juan Miguel Cejuela, Lars Juhl Jensen
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

ext mining automatically extracts information from the literature with the goal of making it available for further analysis, for example by incorporating it into biomedical databases. A key first step towards this goal is to identify and normalize the named entities, such as proteins and species, which are mentioned in text. Despite the large detrimental impact that viruses have on human and agricultural health, very little previous text-mining work has focused on identifying virus species and proteins in the literature. Here, we present an improved dictionary-based system for viral species and the first dictionary for viral proteins, which we benchmark on a new corpus of 300 manually annotated abstracts. We achieve 81.0{\%} precision and 72.7{\%} recall at the task of recognizing and normalizing viral species and 76.2{\%} precision and 34.9{\%} recall on viral proteins. These results are achieved despite the many challenges involved with the names of viral species and, especially, proteins. This work provides a foundation that can be used to extract more complicated relations about viruses from the literature.
Representation of complex terms in a vector space structured by an ontology for a normalization task
Authors: Arnaud Ferr{\'e}, Pierre Zweigenbaum, Claire N{\'e}dellec
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

We propose in this paper a semi-supervised method for labeling terms of texts with concepts of a domain ontology. The method generates continuous vector representations of complex terms in a semantic space structured by the ontology. The proposed method relies on a distributional semantics approach, which generates initial vectors for each of the extracted terms. Then these vectors are embedded in the vector space constructed from the structure of the ontology. This embedding is carried out by training a linear model. Finally, we apply a distance calculation to determine the proximity between vectors of terms and vectors of concepts and thus to assign ontology labels to terms. We have evaluated the quality of these representations for a normalization task by using the concepts of an ontology as semantic labels. Normalization of terms is an important step to extract a part of the information containing in texts, but the vector space generated might find other applications. The performance of this method is comparable to that of the state of the art for this task of standardization, opening up encouraging prospects.
Proactive Learning for Named Entity Recognition
Authors: Maolin Li, Nhung Nguyen, Sophia Ananiadou
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Active Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), text-classification, Text Classification

The goal of active learning is to minimise the cost of producing an annotated dataset, in which annotators are assumed to be perfect, i.e., they always choose the correct labels. However, in practice, annotators are not infallible, and they are likely to assign incorrect labels to some instances. Proactive learning is a generalisation of active learning that can model different kinds of annotators. Although proactive learning has been applied to certain labelling tasks, such as text classification, there is little work on its application to named entity (NE) tagging. In this paper, we propose a proactive learning method for producing NE annotated corpora, using two annotators with different levels of expertise, and who charge different amounts based on their levels of experience. To optimise both cost and annotation quality, we also propose a mechanism to present multiple sentences to annotators at each iteration. Experimental results for several corpora show that our method facilitates the construction of high-quality NE labelled datasets at minimal cost.
Biomedical Event Extraction using Abstract Meaning Representation
Authors: Sudha Rao, Daniel Marcu, Kevin Knight, Hal Daum{\'e} III
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Abstract Meaning Representation, Event Extraction

We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset and show promising results.
Detecting Personal Medication Intake in Twitter: An Annotated Corpus and Baseline Classification System
Authors: Ari Klein, Abeed Sarker, Masoud Rouhizadeh, Karen O{'}Connor, Graciela Gonzalez
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Epidemiology, General Classification

Social media sites (e.g., Twitter) have been used for surveillance of drug safety at the population level, but studies that focus on the effects of medications on specific sets of individuals have had to rely on other sources of data. Mining social media data for this in-formation would require the ability to distinguish indications of personal medication in-take in this media. Towards that end, this paper presents an annotated corpus that can be used to train machine learning systems to determine whether a tweet that mentions a medication indicates that the individual posting has taken that medication at a specific time. To demonstrate the utility of the corpus as a training set, we present baseline results of supervised classification.
Unsupervised Context-Sensitive Spelling Correction of Clinical Free-Text with Word and Character N-Gram Embeddings
Authors: Pieter Fivez, Simon {\v{S}}uster, Walter Daelemans
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Spelling Correction

We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling replacement candidates and ranks them according to their semantic fit, by calculating a weighted cosine similarity between the vectorized representation of a candidate and the misspelling context. We greatly outperform two baseline off-the-shelf spelling correction tools on a manually annotated MIMIC-III test set, and counter the frequency bias of an optimized noisy channel model, showing that neural embeddings can be successfully exploited to include context-awareness in a spelling correction model.
Characterization of Divergence in Impaired Speech of ALS Patients
Authors: Archna Bhatia, Bonnie Dorr, Kristy Hollingshead, Samuel L. Phillips, Barbara McKenzie
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Approximately 80{\%} to 95{\%} of patients with Amyotrophic Lateral Sclerosis (ALS) eventually develop speech impairments, such as defective articulation, slow laborious speech and hypernasality. The relationship between impaired speech and asymptomatic speech may be seen as a divergence from a baseline. This relationship can be characterized in terms of measurable combinations of phonological characteristics that are indicative of the degree to which the two diverge. We demonstrate that divergence measurements based on phonological characteristics of speech correlate with physiological assessments of ALS. Speech-based assessments offer benefits over commonly-used physiological assessments in that they are inexpensive, non-intrusive, and do not require trained clinical personnel for administering and interpreting the results.
Deep Learning for Punctuation Restoration in Medical Reports
Authors: Wael Salloum, Greg Finley, Erik Edwards, Mark Miller, David Suendermann-Oeft
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Deep Learning, Punctuation Restoration, Speech Recognition

In clinical dictation, speakers try to be as concise as possible to save time, often resulting in utterances without explicit punctuation commands. Since the end product of a dictated report, e.g. an out-patient letter, does require correct orthography, including exact punctuation, the latter need to be restored, preferably by automated means. This paper describes a method for punctuation restoration based on a state-of-the-art stack of NLP and machine learning techniques including B-RNNs with an attention mechanism and late fusion, as well as a feature extraction technique tailored to the processing of medical terminology using a novel vocabulary reduction model. To the best of our knowledge, the resulting performance is superior to that reported in prior art on similar tasks.
Unsupervised Domain Adaptation for Clinical Negation Detection
Authors: Timothy Miller, Steven Bethard, Hadi Amiri, Guergana Savova
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Domain Adaptation, Negation, Negation Detection, Unsupervised Domain Adaptation

Detecting negated concepts in clinical texts is an important part of NLP information extraction systems. However, generalizability of negation systems is lacking, as cross-domain experiments suffer dramatic performance losses. We examine the performance of multiple unsupervised domain adaptation algorithms on clinical negation detection, finding only modest gains that fall well short of in-domain performance.
BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations
Authors: Rezarta Islamaj Do{\u{g}}an, Andrew Chatr-aryamontri, Sun Kim, Chih-Hsuan Wei, Yifan Peng, Donald Comeau, Zhiyong Lu
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Articles, Relation Extraction

The Precision Medicine Track in BioCre-ative VI aims to bring together the Bi-oNLP community for a novel challenge focused on mining the biomedical litera-ture in search of mutations and protein-protein interactions (PPI). In order to support this track with an effective train-ing dataset with limited curator time, the track organizers carefully reviewed Pub-Med articles from two different sources: curated public PPI databases, and the re-sults of state-of-the-art public text mining tools. We detail here the data collection, manual review and annotation process and describe this training corpus charac-teristics. We also describe a corpus per-formance baseline. This analysis will provide useful information to developers and researchers for comparing and devel-oping innovative text mining approaches for the BioCreative VI challenge and other Precision Medicine related applica-tions.
Painless Relation Extraction with Kindred
Authors: Jake Lever, Steven Jones
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Named Entity Recognition (NER), Relation, Relation Extraction

Relation extraction methods are essential for creating robust text mining tools to help researchers find useful knowledge in the vast published literature. Easy-to-use and generalizable methods are needed to encourage an ecosystem in which researchers can easily use shared resources and build upon each others{'} methods. We present the Kindred Python package for relation extraction. It builds upon methods from the most successful tools in the recent BioNLP Shared Task to predict high-quality predictions with low computational cost. It also integrates with PubAnnotation, PubTator, and BioNLP Shared Task data in order to allow easy development and application of relation extraction models.
Noise Reduction Methods for Distantly Supervised Biomedical Relation Extraction
Authors: Gang Li, Cathy Wu, K. Vijay-Shanker
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Relation, Relation Extraction

Distant supervision has been applied to automatically generate labeled data for biomedical relation extraction. Noise exists in both positively and negatively-labeled data and affects the performance of supervised machine learning methods. In this paper, we propose three novel heuristics based on the notion of proximity, trigger word and confidence of patterns to leverage lexical and syntactic information to reduce the level of noise in the distantly labeled data. Experiments on three different tasks, extraction of protein-protein-interaction, miRNA-gene regulation relation and protein-localization event, show that the proposed methods can improve the F-score over the baseline by 6, 10 and 14 points for the three tasks, respectively. We also show that when the models are configured to output high-confidence results, high precisions can be obtained using the proposed methods, making them promising for facilitating manual curation for databases.
Role-Preserving Redaction of Medical Records to Enable Ontology-Driven Processing
Authors: Seth Polsley, Atif Tahir, Muppala Raju, Akintayo Akinleye, Duane Steward
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Electronic medical records (EMR) have largely replaced hand-written patient files in healthcare. The growing pool of EMR data presents a significant resource in medical research, but the U.S. Health Insurance Portability and Accountability Act (HIPAA) mandates redacting medical records before performing any analysis on the same. This process complicates obtaining medical data and can remove much useful information from the record. As part of a larger project involving ontology-driven medical processing, we employ a method of recognizing protected health information (PHI) that maps to ontological terms. We then use the relationships defined in the ontology to redact medical texts so that roles and semantics of terms are retained without compromising anonymity. The method is evaluated by clinical experts on several hundred medical documents, achieving up to a 98.8{\%} f-score, and has already shown promise for retaining semantic information in later processing.
Annotation of pain and anesthesia events for surgery-related processes and outcomes extraction
Authors: Wen-wai Yim, Dario Tedesco, Catherine Curtin, Hern, Tina ez-Boussard
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Management

Pain and anesthesia information are crucial elements to identifying surgery-related processes and outcomes. However pain is not consistently recorded in the electronic medical record. Even when recorded, the rich complex granularity of the pain experience may be lost. Similarly, anesthesia information is recorded using local electronic collection systems; though the accuracy and completeness of the information is unknown. We propose an annotation schema to capture pain, pain management, and anesthesia event information.
Identifying Comparative Structures in Biomedical Text
Authors: Samir Gupta, A.S.M. Ashique Mahmood, Karen Ross, Cathy Wu, K. Vijay-Shanker
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Sentence

Comparison sentences are very commonly used by authors in biomedical literature to report results of experiments. In such comparisons, authors typically make observations under two different scenarios. In this paper, we present a system to automatically identify such comparative sentences and their components i.e. the compared entities, the scale of the comparison and the aspect on which the entities are being compared. Our methodology is based on dependencies obtained by applying a parser to extract a wide range of comparison structures. We evaluated our system for its effectiveness in identifying comparisons and their components. The system achieved a F-score of 0.87 for comparison sentence identification and 0.77-0.81 for identifying its components.
Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models
Authors: Subhradeep Kayal, Zubair Afzal, George Tsatsaronis, Sophia Katrenko, Pascal Coupet, Marius Doornenbal, Michelle Gregory
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Articles, Document Summarization, Information Retrieval, Multi-Document Summarization, Text Classification

In this paper we present a solution for tagging funding bodies and grants in scientific articles using a combination of trained sequential learning models, namely conditional random fields (CRF), hidden markov models (HMM) and maximum entropy models (MaxEnt), on a benchmark set created in-house. We apply the trained models to address the BioASQ challenge 5c, which is a newly introduced task that aims to solve the problem of funding information extraction from scientific articles. Results in the dry-run data set of BioASQ task 5c show that the suggested approach can achieve a micro-recall of more than 85{\%} in tagging both funding bodies and grants.
Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs
Authors: Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Biomedical Information Retrieval, Information Retrieval, regression, Retrieval, Text Matching

We describe a Deep Learning approach to modeling the relevance of a document{'}s text to a query, applied to biomedical literature. Instead of mapping each document and query to a common semantic space, we compute a variable-length difference vector between the query and document which is then passed through a deep convolution stage followed by a deep regression network to produce the estimated probability of the document{'}s relevance to the query. Despite the small amount of training data, this approach produces a more robust predictor than computing similarities between semantic vector representations of the query and document, and also results in significant improvements over traditional IR text factors. In the future, we plan to explore its application in improving PubMed search.
Detecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia
Authors: Vaden Masrani, Gabriel Murray, Thalia Field, Giuseppe Carenini
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: BIG-bench Machine Learning

We investigate if writers with dementia can be automatically distinguished from those without by analyzing linguistic markers in written text, in the form of blog posts. We have built a corpus of several thousand blog posts, some by people with dementia and others by people with loved ones with dementia. We use this dataset to train and test several machine learning methods, and achieve prediction performance at a level far above the baseline.
Protein Word Detection using Text Segmentation Techniques
Authors: Devi Ganesan, Ashish V. Tendulkar, Sutanu Chakraborti
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Decipherment, General Classification, Segmentation, Text Segmentation

Literature in Molecular Biology is abundant with linguistic metaphors. There have been works in the past that attempt to draw parallels between linguistics and biology, driven by the fundamental premise that proteins have a language of their own. Since word detection is crucial to the decipherment of any unknown language, we attempt to establish a problem mapping from natural language text to protein sequences at the level of words. Towards this end, we explore the use of an unsupervised text segmentation algorithm to the task of extracting {``}biological words{''} from protein sequences. In particular, we demonstrate the effectiveness of using domain knowledge to complement data driven approaches in the text segmentation task, as well as in its biological counterpart. We also propose a novel extrinsic evaluation measure for protein words through protein family classification.
Enhancing Automatic ICD-9-CM Code Assignment for Medical Texts with PubMed
Authors: Danchen Zhang, Daqing He, Sanqiang Zhao, Lei LI
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8

Assigning a standard ICD-9-CM code to disease symptoms in medical texts is an important task in the medical domain. Automating this process could greatly reduce the costs. However, the effectiveness of an automatic ICD-9-CM code classifier faces a serious problem, which can be triggered by unbalanced training data. Frequent diseases often have more training data, which helps its classification to perform better than that of an infrequent disease. However, a disease{'}s frequency does not necessarily reflect its importance. To resolve this training data shortage problem, we propose to strategically draw data from PubMed to enrich the training data when there is such need. We validate our method on the CMC dataset, and the evaluation results indicate that our method can significantly improve the code assignment classifiers{'} performance at the macro-averaging level.
Evaluating Feature Extraction Methods for Knowledge-based Biomedical Word Sense Disambiguation
Authors: Sam Henry, Clint Cuffy, Bridget McInnes
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Dimensionality Reduction, Information Retrieval, Question Answering, Word Embeddings, Word Sense Disambiguation

In this paper, we present an analysis of feature extraction methods via dimensionality reduction for the task of biomedical Word Sense Disambiguation (WSD). We modify the vector representations in the 2-MRD WSD algorithm, and evaluate four dimensionality reduction methods: Word Embeddings using Continuous Bag of Words and Skip Gram, Singular Value Decomposition (SVD), and Principal Component Analysis (PCA). We also evaluate the effects of vector size on the performance of each of these methods. Results are evaluated on five standard evaluation datasets (Abbrev.100, Abbrev.200, Abbrev.300, NLM-WSD, and MSH-WSD). We find that vector sizes of 100 are sufficient for all techniques except SVD, for which a vector size of 1500 is referred. We also show that SVD performs on par with Word Embeddings for all but one dataset.