pwc-archive/papers-with-abstracts huggingface dataset:
Annotating Temporal Information in Clinical Notes for Timeline Reconstruction: Towards the Definition of Calendar Expressions
Authors: Natalia Viani, Hegler Tissot, Ariane Bernardino, Sumithra Velupillai
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

To automatically analyse complex trajectory information enclosed in clinical text (e.g. timing of symptoms, duration of treatment), it is important to understand the related temporal aspects, anchoring each event on an absolute point in time. In the clinical domain, few temporally annotated corpora are currently available. Moreover, underlying annotation schemas - which mainly rely on the TimeML standard - are not necessarily easily applicable for applications such as patient timeline reconstruction. In this work, we investigated how temporal information is documented in clinical text by annotating a corpus of medical reports with time expressions (TIMEXes), based on TimeML. The developed corpus is available to the NLP community. Starting from our annotations, we analysed the suitability of the TimeML TIMEX schema for capturing timeline information, identifying challenges and possible solutions. As a result, we propose a novel annotation schema that could be useful for timeline reconstruction: CALendar EXpression (CALEX).
Leveraging Sublanguage Features for the Semantic Categorization of Clinical Terms
Authors: Leonie Gr{\"o}n, Ann Bertels, Kris Heylen
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: General Classification

The automatic processing of clinical documents, such as Electronic Health Records (EHRs), could benefit substantially from the enrichment of medical terminologies with terms encountered in clinical practice. To integrate such terms into existing knowledge sources, they must be linked to corresponding concepts. We present a method for the semantic categorization of clinical terms based on their surface form. We find that features based on sublanguage properties can provide valuable cues for the classification of term variants.
Contributions to Clinical Named Entity Recognition in Portuguese
Authors: F{\'a}bio Lopes, C{\'e}sar Teixeira, Hugo Gon{\c{c}}alo Oliveira
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

Having in mind that different languages might present different challenges, this paper presents the following contributions to the area of Information Extraction from clinical text, targeting the Portuguese language: a collection of 281 clinical texts in this language, with manually-annotated named entities; word embeddings trained in a larger collection of similar texts; results of using BiLSTM-CRF neural networks for named entity recognition on the annotated collection, including a comparison of using in-domain or out-of-domain word embeddings in this task. Although learned with much less data, performance is higher when using in-domain embeddings. When tested in 20 independent clinical texts, this model achieved better results than a model using larger out-of-domain embeddings.
Can Character Embeddings Improve Cause-of-Death Classification for Verbal Autopsy Narratives?
Authors: Zhaodong Yan, Serena Jeblee, Graeme Hirst
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Classification, General Classification

We present two models for combining word and character embeddings for cause-of-death classification of verbal autopsy reports using the text of the narratives. We find that for smaller datasets (500 to 1000 records), adding character information to the model improves classification, making character-based CNNs a promising method for automated verbal autopsy coding.
ChiMed: A Chinese Medical Corpus for Question Answering
Authors: Yuanhe Tian, Weicheng Ma, Fei Xia, Yan Song
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Question Answering

Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains. While models trained in the general domain can be adapted to a new target domain, their performance often degrades significantly due to domain mismatch. Alternatively, one can require a large amount of domain-specific QA data, but such data are rare, especially for the medical domain. In this study, we first collect a large-scale Chinese medical QA corpus called ChiMed; second we annotate a small fraction of the corpus to check the quality of the answers; third, we extract two datasets from the corpus and use them for the relevancy prediction task and the adoption prediction task. Several benchmark models are applied to the datasets, producing good results for both tasks.
Clinical Case Reports for NLP
Authors: Cyril Grouin, Natalia Grabar, Vincent Claveau, Thierry Hamon
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Textual data are useful for accessing expert information. Yet, since the texts are representative of distinct language uses, it is necessary to build specific corpora in order to be able to design suitable NLP tools. In some domains, such as medical domain, it may be complicated to access the representative textual data and their semantic annotations, while there exists a real need for providing efficient tools and methods. Our paper presents a corpus of clinical cases written in French, and their semantic annotations. Thus, we manually annotated a set of 717 files into four general categories (age, gender, outcome, and origin) for a total number of 2,835 annotations. The values of age, gender, and outcome are normalized. A subset with 70 files has been additionally manually annotated into 27 categories for a total number of 5,198 annotations.
Simplification-induced transformations: typology and some characteristics
Authors: Ana{\"\i}s Koptient, R{\'e}mi Cardon, Natalia Grabar
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Text Simplification

The purpose of automatic text simplification is to transform technical or difficult to understand texts into a more friendly version. The semantics must be preserved during this transformation. Automatic text simplification can be done at different levels (lexical, syntactic, semantic, stylistic...) and relies on the corresponding knowledge and resources (lexicon, rules...). Our objective is to propose methods and material for the creation of transformation rules from a small set of parallel sentences differentiated by their technicity. We also propose a typology of transformations and quantify them. We work with French-language data related to the medical domain, although we assume that the method can be exploited on texts in any language and from any domain.
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning
Authors: Hassan Alhuzali, Sophia Ananiadou
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: General Classification, Sentiment Analysis, Transfer Learning

The availability of large-scale and real-time data on social media has motivated research into adverse drug reactions (ADRs). ADR classification helps to identify negative effects of drugs, which can guide health professionals and pharmaceutical companies in making medications safer and advocating patients{'} safety. Based on the observation that in social media, negative sentiment is frequently expressed towards ADRs, this study presents a neural model that combines sentiment analysis with transfer learning techniques to improve ADR detection in social media postings. Our system is firstly trained to classify sentiment in tweets concerning current affairs, using the SemEval17-task4A corpus. We then apply transfer learning to adapt the model to the task of detecting ADRs in social media postings. We show that, in combination with rich representations of words and their contexts, transfer learning is beneficial, especially given the large degree of vocabulary overlap between the current affairs posts in the SemEval17-task4A corpus and posts about ADRs. We compare our results with previous approaches, and show that our model can outperform them by up to 3{\%} F-score.
Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations
Authors: Gaurav Vashisth, Jan-Niklas Voigt-Antons, Michael Mikhailov, Rol Roller
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

In research best practices can change over time as new discoveries are made and novel methods are implemented. Scientific publications reporting about the latest facts and current state-of-the-art can be possibly outdated after some years or even proved to be false. A publication usually sheds light only on the knowledge of the period it has been published. Thus, the aspect of time can play an essential role in the reliability of the presented information. In Natural Language Processing many methods focus on information extraction from text, such as detecting entities and their relationship to each other. Those methods mostly focus on the facts presented in the text itself and not on the aspects of knowledge which changes over time. This work instead examines the evolution in biomedical knowledge over time using scientific literature in terms of diachronic change. Mainly the usage of temporal and distributional concept representations are explored and evaluated by a proof-of-concept.
Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications
Authors: Anna Koroleva, Patrick Paroubek
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Two-sample testing

Randomized controlled trials assess the effects of an experimental intervention by comparing it to a control intervention with regard to some variables - trial outcomes. Statistical hypothesis testing is used to test if the experimental intervention is superior to the control. Statistical significance is typically reported for the measured outcomes and is an important characteristic of the results. We propose a machine learning approach to automatically extract reported outcomes, significance levels and the relation between them. We annotated a corpus of 663 sentences with 2,552 outcome - significance level relations (1,372 positive and 1,180 negative relations). We compared several classifiers, using a manually crafted feature set, and a number of deep learning models. The best performance (F-measure of 94{\%}) was shown by the BioBERT fine-tuned model.
Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering
Authors: Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Information Retrieval, Natural Language Inference, Question Answering, Retrieval

This paper presents the MEDIQA 2019 shared task organized at the ACL-BioNLP workshop. The shared task is motivated by a need to develop relevant methods, techniques and gold standards for inference and entailment in the medical domain, and their application to improve domain specific information retrieval and question answering systems. MEDIQA 2019 includes three tasks: Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and Question Answering (QA) in the medical domain. 72 teams participated in the challenge, achieving an accuracy of 98{\%} in the NLI task, 74.9{\%} in the RQE task, and 78.3{\%} in the QA task. In this paper, we describe the tasks, the datasets, and the participants{'} approaches and results. We hope that this shared task will attract further research efforts in textual inference, question entailment, and question answering in the medical domain.
PANLP at MEDIQA 2019: Pre-trained Language Models, Transfer Learning and Knowledge Distillation
Authors: Wei Zhu, Xiaofeng Zhou, Keqiang Wang, Xun Luo, Xiepeng Li, Yuan Ni, Guotong Xie
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Knowledge Distillation, Re-Ranking, Transfer Learning

This paper describes the models designated for the MEDIQA 2019 shared tasks by the team PANLP. We take advantages of the recent advances in pre-trained bidirectional transformer language models such as BERT (Devlin et al., 2018) and MT-DNN (Liu et al., 2019b). We find that pre-trained language models can significantly outperform traditional deep learning models. Transfer learning from the NLI task to the RQE task is also experimented, which proves to be useful in improving the results of fine-tuning MT-DNN large. A knowledge distillation process is implemented, to distill the knowledge contained in a set of models and transfer it into an single model, whose performance turns out to be comparable with that obtained by the ensemble of that set of models. Finally, for test submissions, model ensemble and a re-ranking process are implemented to boost the performances. Our models participated in all three tasks and ranked the 1st place for the RQE task, and the 2nd place for the NLI task, and also the 2nd place for the QA task.
WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference
Authors: Zhaofeng Wu, Yan Song, Sicong Huang, Yuanhe Tian, Fei Xia
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Natural Language Inference

Natural language inference (NLI) is challenging, especially when it is applied to technical domains such as biomedical settings. In this paper, we propose a hybrid approach to biomedical NLI where different types of information are exploited for this task. Our base model includes a pre-trained text encoder as the core component, and a syntax encoder and a feature encoder to capture syntactic and domain-specific information. Then we combine the output of different base models to form more powerful ensemble models. Finally, we design two conflict resolution strategies when the test data contain multiple (premise, hypothesis) pairs with the same premise. We train our models on the MedNLI dataset, yielding the best performance on the test set of the MEDIQA 2019 Task 1.
KU\_ai at MEDIQA 2019: Domain-specific Pre-training and Transfer Learning for Medical NLI
Authors: Cemil Cengiz, Ula{\c{s}} Sert, Deniz Yuret
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: De-identification, Language Modeling, Language Modelling, Natural Language Inference, Transfer Learning

In this paper, we describe our system and results submitted for the Natural Language Inference (NLI) track of the MEDIQA 2019 Shared Task. As KU{\_}ai team, we used BERT as our baseline model and pre-processed the MedNLI dataset to mitigate the negative impact of de-identification artifacts. Moreover, we investigated different pre-training and transfer learning approaches to improve the performance. We show that pre-training the language model on rich biomedical corpora has a significant effect in teaching the model domain-specific language. In addition, training the model on large NLI datasets such as MultiNLI and SNLI helps in learning task-specific reasoning. Finally, we ensembled our highest-performing models, and achieved 84.7{\%} accuracy on the unseen test dataset and ranked 10th out of 17 teams in the official results.
DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering
Authors: Huiwei Zhou, Xuefei Li, Weihong Yao, Chengkun Lang, Shixian Ning
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Medical Question Answering, Multi-Task Learning, Question Answering

In this paper, we propose a novel model called Adversarial Multi-Task Network (AMTN) for jointly modeling Recognizing Question Entailment (RQE) and medical Question Answering (QA) tasks. AMTN utilizes a pre-trained BioBERT model and an Interactive Transformer to learn the shared semantic representations across different task through parameter sharing mechanism. Meanwhile, an adversarial training strategy is introduced to separate the private features of each task from the shared representations. Experiments on BioNLP 2019 RQE and QA Shared Task datasets show that our model benefits from the shared representations of both tasks provided by multi-task learning and adversarial training, and obtains significant improvements upon the single-task models.
DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering
Authors: Huiwei Zhou, Bizun Lei, Zhe Liu, Zhuang Liu
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Question Answering, Relation

In medical domain, given a medical question, it is difficult to manually select the most relevant information from a large number of search results. BioNLP 2019 proposes Question Answering (QA) task, which encourages the use of text mining technology to automatically judge whether a search result is an answer to the medical question. The main challenge of QA task is how to mine the semantic relation between question and answer. We propose BioBERT Transformer model to tackle this challenge, which applies Transformers to extract semantic relation between different words in questions and answers. Furthermore, BioBERT is utilized to encode medical domain-specific contextualized word representations. Our method has reached the accuracy of 76.24{\%} and spearman of 17.12{\%} on the BioNLP 2019 QA task.
Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment
Authors: Sai Abishek Bhaskar, Rashi Rungta, James Route, Eric Nyberg, Teruko Mitamura
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Multi-Task Learning, Natural Language Inference, Question Similarity

This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH. We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations. Our final models for the NLI and RQE tasks achieve the 4th and 2nd rank on the shared-task leaderboard respectively.
IIT-KGP at MEDIQA 2019: Recognizing Question Entailment using Sci-BERT stacked with a Gradient Boosting Classifier
Authors: Prakhar Sharma, Sumegh Roychowdhury
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Question Answering, Retrieval

Official System Description paper of Team IIT-KGP ranked 1st in the Development phase and 3rd in Testing Phase in MEDIQA 2019 - Recognizing Question Entailment (RQE) Shared Task of BioNLP workshop - ACL 2019. The number of people turning to the Internet to search for a diverse range of health-related subjects continues to grow and with this multitude of information available, duplicate questions are becoming more frequent and finding the most appropriate answers becomes problematic. This issue is important for question answering platforms as it complicates the retrieval of all information relevant to the same topic, particularly when questions similar in essence are expressed differently, and answering a given medical question by retrieving similar questions that are already answered by human experts seems to be a promising solution. In this paper, we present our novel approach to detect question entailment by determining the type of question asked rather than focusing on the type of the ailment given. This unique methodology makes the approach robust towards examples which have different ailment names but are synonyms of each other. Also, it enables us to check entailment at a much more fine-grained level. QSpider is a staged system consisting of state-of-the-art model Sci-BERT used as a multi-class classifier aimed at capturing both question types and semantic relations stacked with a Gradient Boosting Classifier which checks for entailment. QSpider achieves an accuracy score of 68.4{\%} on the Test set which outperforms the baseline model (54.1{\%}) by an accuracy score of 14.3{\%}.
ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge
Authors: Vincent Nguyen, Sarvnaz Karimi, Zhenchang Xing
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Medical Question Answering, Natural Language Inference, Question Answering

We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA. Textual inference is the task of finding the semantic relationships between pairs of text. Question entailment involves identifying pairs of questions which have similar semantic content. To improve upon medical natural language inference and question entailment approaches to further medical question answering, we propose a system that incorporates open-domain and biomedical domain approaches to improve semantic understanding and ambiguity resolution. Our models achieve 80{\%} accuracy on medical natural language inference (6.5{\%} absolute improvement over the original baseline), 48.9{\%} accuracy on recognising medical question entailment, 0.248 Spearman{'}s rho for question answering ranking and 68.6{\%} accuracy for question answering classification.
MSIT\_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.
Authors: Sahil Chopra, Ankita Gupta, Anupama Kaushik
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Natural Language Inference, Transfer Learning

In this paper, we present Biomedical Multi-Task Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge. Bio-MTDNN utilizes {``}transfer learning{''} based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related. Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain. Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results.
UU\_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain
Authors: Noha Tawfik, Marco Spruit
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Natural Language Inference, Question Answering, Sentence

This article describes the participation of the UU{\_}TAILS team in the 2019 MEDIQA challenge intended to improve domain-specific models in medical and clinical NLP. The challenge consists of 3 tasks: medical language inference (NLI), recognizing textual entailment (RQE) and question answering (QA). Our team participated in tasks 1 and 2 and our best runs achieved a performance accuracy of 0.852 and 0.584 respectively for the test sets. The models proposed for task 1 relied on BERT embeddings and different ensemble techniques. For the RQE task, we trained a traditional multilayer perceptron network based on embeddings generated by the universal sentence encoder.
Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language Inference
Authors: Kamal raj Kanakarajan, Suriyadeepan Ramamoorthy, Vaidheeswaran Archana, Soham Chatterjee, Malaikannan Sankarasubbu
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Natural Language Inference

Natural Language inference is the task of identifying relation between two sentences as entailment, contradiction or neutrality. MedNLI is a biomedical flavour of NLI for clinical domain. This paper explores the use of Bidirectional Encoder Representation from Transformer (BERT) for solving MedNLI. The proposed model, BERT pre-trained on PMC, PubMed and fine-tuned on MIMICIII v1.4, achieves state of the art results on MedNLI (83.45{\%}) and an accuracy of 78.5{\%} in MEDIQA challenge. The authors present an analysis of the attention patterns that emerged as a result of training BERT on MedNLI using a visualization tool, bertviz.