pwc-archive/papers-with-abstracts huggingface dataset:
Stylistic variation over 200 years of court proceedings according to gender and social class
Authors: Stefania Degaetano-Ortlieb
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6

We present an approach to detect stylistic variation across social variables (here: gender and social class), considering also diachronic change in language use. For detection of stylistic variation, we use relative entropy, measuring the difference between probability distributions at different linguistic levels (here: lexis and grammar). In addition, by relative entropy, we can determine which linguistic units are related to stylistic variation.
Cross-corpus Native Language Identification via Statistical Embedding
Authors: Francisco Rangel, Paolo Rosso, Julian Brooke, Alex Uitdenbogerd, ra
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Cross-corpus, Language Identification, Native Language Identification

In this paper, we approach the task of native language identification in a realistic cross-corpus scenario where a model is trained with available data and has to predict the native language from data of a different corpus. The motivation behind this study is to investigate native language identification in the Australian academic scenario where a majority of students come from China, Indonesia, and Arabic-speaking nations. We have proposed a statistical embedding representation reporting a significant improvement over common single-layer approaches of the state of the art, identifying Chinese, Arabic, and Indonesian in a cross-corpus scenario. The proposed approach was shown to be competitive even when the data is scarce and imbalanced.
Scientific Discovery as Link Prediction in Influence and Citation Graphs
Authors: Fan Luo, Marco A. Valenzuela-Esc{\'a}rcega, Gus Hahn-Powell, Mihai Surdeanu
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Link Prediction, Reading Comprehension, scientific discovery

We introduce a machine learning approach for the identification of {``}white spaces{''} in scientific knowledge. Our approach addresses this task as link prediction over a graph that contains over 2M influence statements such as {``}CTCF activates FOXA1{''}, which were automatically extracted using open-domain machine reading. We model this prediction task using graph-based features extracted from the above influence graph, as well as from a citation graph that captures scientific communities. We evaluated the proposed approach through backtesting. Although the data is heavily unbalanced (50 times more negative examples than positives), our approach predicts which influence links will be discovered in the {``}near future{''} with a F1 score of 27 points, and a mean average precision of 68{\%}.
Efficient Generation and Processing of Word Co-occurrence Networks Using corpus2graph
Authors: Zheng Zhang, Pierre Zweigenbaum, Ruiqing Yin
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Keyword Extraction

Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus. It not only contains different built-in methods to preprocess words, analyze sentences, extract word pairs and define edge weights, but also supports user-customized functions. By using parallelization techniques, it can generate a large word co-occurrence network of the whole English Wikipedia data within hours. And thanks to its nodes-edges-weight three-level progressive calculation design, rebuilding networks with different configurations is even faster as it does not need to start all over again. This tool also works with other graph libraries such as igraph, NetworkX and graph-tool as a front end providing data to boost network generation speed.
Multi-Sentence Compression with Word Vertex-Labeled Graphs and Integer Linear Programming
Authors: Elvys Linhares Pontes, St{\'e}phane Huet, Thiago Gouveia da Silva, Andr{\'e}a Carneiro Linhares, Juan-Manuel Torres-Moreno
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Informativeness, Question Answering, Sentence, Sentence Compression, Text Summarization

Multi-Sentence Compression (MSC) aims to generate a short sentence with key information from a cluster of closely related sentences. MSC enables summarization and question-answering systems to generate outputs combining fully formed sentences from one or several documents. This paper describes a new Integer Linear Programming method for MSC using a vertex-labeled graph to select different keywords, and novel 3-gram scores to generate more informative sentences while maintaining their grammaticality. Our system is of good quality and outperforms the state-of-the-art for evaluations led on news dataset. We led both automatic and manual evaluations to determine the informativeness and the grammaticality of compressions for each dataset. Additional tests, which take advantage of the fact that the length of compressions can be modulated, still improve ROUGE scores with shorter output sentences.
Large-scale spectral clustering using diffusion coordinates on landmark-based bipartite graphs
Authors: Khiem Pham, Guangliang Chen
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Clustering, Image/Document Clustering, Semantic Segmentation

Spectral clustering has received a lot of attention due to its ability to separate nonconvex, non-intersecting manifolds, but its high computational complexity has significantly limited its applicability. Motivated by the document-term co-clustering framework by Dhillon (2001), we propose a landmark-based scalable spectral clustering approach in which we first use the selected landmark set and the given data to form a bipartite graph and then run a diffusion process on it to obtain a family of diffusion coordinates for clustering. We show that our proposed algorithm can be implemented based on very efficient operations on the affinity matrix between the given data and selected landmarks, thus capable of handling large data. Finally, we demonstrate the excellent performance of our method by comparing with the state-of-the-art scalable algorithms on several benchmark data sets.
Fusing Document, Collection and Label Graph-based Representations with Word Embeddings for Text Classification
Authors: Konstantinos Skianis, Fragkiskos Malliaros, Michalis Vazirgiannis
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: General Classification, Sentiment Analysis, Text Categorization, text-classification, Text Classification, Word Embeddings

Contrary to the traditional Bag-of-Words approach, we consider the Graph-of-Words(GoW) model in which each document is represented by a graph that encodes relationships between the different terms. Based on this formulation, the importance of a term is determined by weighting the corresponding node in the document, collection and label graphs, using node centrality criteria. We also introduce novel graph-based weighting schemes by enriching graphs with word-embedding similarities, in order to reward or penalize semantic relationships. Our methods produce more discriminative feature weights for text categorization, outperforming existing frequency-based criteria.
Multi-task learning for interpretable cause of death classification using key phrase prediction
Authors: Serena Jeblee, Mireille Gomes, Graeme Hirst
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: General Classification, Multi-Task Learning, Text Classification

We introduce a multi-task learning model for cause-of-death classification of verbal autopsy narratives that jointly learns to output interpretable key phrases. Adding these key phrases outperforms the baseline model and topic modeling features.
Identifying Risk Factors For Heart Disease in Electronic Medical Records: A Deep Learning Approach
Authors: Thanat Chokwijitkul, Anthony Nguyen, Hamed Hassanzadeh, Siegfried Perez
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Clinical Concept Extraction, Deep Learning, Feature Engineering, text-classification, Text Classification

Automatic identification of heart disease risk factors in clinical narratives can expedite disease progression modelling and support clinical decisions. Existing practical solutions for cardiovascular risk detection are mostly hybrid systems entailing the integration of knowledge-driven and data-driven methods, relying on dictionaries, rules and machine learning methods that require a substantial amount of human effort. This paper proposes a comparative analysis on the applicability of deep learning, a re-emerged data-driven technique, in the context of clinical text classification. Various deep learning architectures were devised and evaluated for extracting heart disease risk factors from clinical documents. The data provided for the 2014 i2b2/UTHealth shared task focusing on identifying risk factors for heart disease was used for system development and evaluation. Results have shown that a relatively simple deep learning model can achieve a high micro-averaged F-measure of 0.9081, which is comparable to the best systems from the shared task. This is highly encouraging given the simplicity of the deep learning approach compared to the heavily feature-engineered hybrid approaches that were required to achieve state-of-the-art performances.
Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks
Authors: Ilham Fathy Saputra, Rahmad Mahendra, Alfan Farizki Wicaksono
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Question Answering, Text Classification, Text Summarization, Word Embeddings

We propose keyphrases extraction technique to extract important terms from the healthcare user-generated contents. We employ deep learning architecture, i.e. Long Short-Term Memory, and leverage word embeddings, medical concepts from a knowledge base, and linguistic components as our features. The proposed model achieves 61.37{\%} F-1 score. Experimental results indicate that our proposed approach outperforms the baseline methods, i.e. RAKE and CRF, on the task of extracting keyphrases from Indonesian health forum posts.
Identifying Key Sentences for Precision Oncology Using Semi-Supervised Learning
Authors: Jurica {\v{S}}eva, Martin Wackerbauer, Ulf Leser
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Articles, BIG-bench Machine Learning, Transductive Learning

We present a machine learning pipeline that identifies key sentences in abstracts of oncological articles to aid evidence-based medicine. This problem is characterized by the lack of gold standard datasets, data imbalance and thematic differences between available silver standard corpora. Additionally, available training and target data differs with regard to their domain (professional summaries vs. sentences in abstracts). This makes supervised machine learning inapplicable. We propose the use of two semi-supervised machine learning approaches: To mitigate difficulties arising from heterogeneous data sources, overcome data imbalance and create reliable training data we propose using transductive learning from positive and unlabelled data (PU Learning). For obtaining a realistic classification model, we propose the use of abstracts summarised in relevant sentences as unlabelled examples through Self-Training. The best model achieves 84{\%} accuracy and 0.84 F1 score on our dataset
Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimization
Authors: Dieter Galea, Ivan Laponogov, Kirill Veselkov
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Feature Engineering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

Word2vec embeddings are limited to computing vectors for in-vocabulary terms and do not take into account sub-word information. Character-based representations, such as fastText, mitigate such limitations. We optimize and compare these representations for the biomedical domain. fastText was found to consistently outperform word2vec in named entity recognition tasks for entities such as chemicals and genes. This is likely due to gained information from computed out-of-vocabulary term vectors, as well as the word compositionality of such entities. Contrastingly, performance varied on intrinsic datasets. Optimal hyper-parameters were intrinsic dataset-dependent, likely due to differences in term types distributions. This indicates embeddings should be chosen based on the task at hand. We therefore provide a number of optimized hyper-parameter sets and pre-trained word2vec and fastText models, available on \url{https://github.com/dterg/bionlp-embed}.
PICO Element Detection in Medical Text via Long Short-Term Memory Neural Networks
Authors: Di Jin, Peter Szolovits
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Benchmarking, Decision Making, General Classification, PICO

Successful evidence-based medicine (EBM) applications rely on answering clinical questions by analyzing large medical literature databases. In order to formulate a well-defined, focused clinical question, a framework called PICO is widely used, which identifies the sentences in a given medical text that belong to the four components: Participants/Problem (P), Intervention (I), Comparison (C) and Outcome (O). In this work, we present a Long Short-Term Memory (LSTM) neural network based model to automatically detect PICO elements. By jointly classifying subsequent sentences in the given text, we achieve state-of-the-art results on PICO element classification compared to several strong baseline models. We also make our curated data public as a benchmarking dataset so that the community can benefit from it.
Coding Structures and Actions with the COSTA Scheme in Medical Conversations
Authors: Nan Wang, Yan Song, Fei Xia
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Decision Making

This paper describes the COSTA scheme for coding structures and actions in conversation. Informed by Conversation Analysis, the scheme introduces an innovative method for marking multi-layer structural organization of conversation and a structure-informed taxonomy of actions. In addition, we create a corpus of naturally occurring medical conversations, containing 318 video-recorded and manually transcribed pediatric consultations. Based on the annotated corpus, we investigate 1) treatment decision-making process in medical conversations, and 2) effects of physician-caregiver communication behaviors on antibiotic over-prescribing. Although the COSTA annotation scheme is developed based on data from the task-specific domain of pediatric consultations, it can be easily extended to apply to more general domains and other languages.
A Neural Autoencoder Approach for Document Ranking and Query Refinement in Pharmacogenomic Information Retrieval
Authors: Jonas Pfeiffer, Samuel Broscheit, Rainer Gemulla, Mathias G{\"o}schl
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Decision Making, Document Ranking, Feature Engineering, Information Retrieval, Learning-To-Rank, Retrieval

In this study, we investigate learning-to-rank and query refinement approaches for information retrieval in the pharmacogenomic domain. The goal is to improve the information retrieval process of biomedical curators, who manually build knowledge bases for personalized medicine. We study how to exploit the relationships between genes, variants, drugs, diseases and outcomes as features for document ranking and query refinement. For a supervised approach, we are faced with a small amount of annotated data and a large amount of unannotated data. Therefore, we explore ways to use a neural document auto-encoder in a semi-supervised approach. We show that a combination of established algorithms, feature-engineering and a neural auto-encoder model yield promising results in this setting.
Biomedical Event Extraction Using Convolutional Neural Networks and Dependency Parsing
Authors: Jari Bj{\"o}rne, Tapio Salakoski
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Dependency Parsing, Diversity, Event Extraction, NER, Relation, Relation Extraction, Semantic Parsing, Word Embeddings

Event and relation extraction are central tasks in biomedical text mining. Where relation extraction concerns the detection of semantic connections between pairs of entities, event extraction expands this concept with the addition of trigger words, multiple arguments and nested events, in order to more accurately model the diversity of natural language. In this work we develop a convolutional neural network that can be used for both event and relation extraction. We use a linear representation of the input text, where information is encoded with various vector space embeddings. Most notably, we encode the parse graph into this linear space using dependency path embeddings. We integrate our neural network into the open source Turku Event Extraction System (TEES) framework. Using this system, our machine learning model can be easily applied to a large set of corpora from e.g. the BioNLP, DDI Extraction and BioCreative shared tasks. We evaluate our system on 12 different event, relation and NER corpora, showing good generalizability to many tasks and achieving improved performance on several corpora.
BioAMA: Towards an End to End BioMedical Question Answering System
Authors: Vasu  Sharma, Nitish Kulkarni, Srividya Pranavi, Gabriel Bayomi, Eric Nyberg, Teruko Mitamura
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Natural Language Inference, NER, Question Answering, Retrieval, Transfer Learning, Word Embeddings

In this paper, we present a novel Biomedical Question Answering system, BioAMA: {``}Biomedical Ask Me Anything{''} on task 5b of the annual BioASQ challenge. In this work, we focus on a wide variety of question types including factoid, list based, summary and yes/no type questions that generate both exact and well-formed {`}ideal{'} answers. For summary-type questions, we combine effective IR-based techniques for retrieval and diversification of relevant snippets for a question to create an end-to-end system which achieves a ROUGE-2 score of 0.72 and a ROUGE-SU4 score of 0.71 on ideal answer questions (7{\%} improvement over the previous best model). Additionally, we propose a novel NLI-based framework to answer the yes/no questions. To train the NLI model, we also devise a transfer-learning technique by cross-domain projection of word embeddings. Finally, we present a two-stage approach to address the factoid and list type questions by first generating a candidate set using NER taggers and ranking them using both supervised or unsupervised techniques.
Phrase2VecGLM: Neural generalized language model--based semantic tagging for complex query reformulation in medical IR
Authors: Manirupa Das, Eric Fosler-Lussier, Simon Lin, Soheil Moosavinasab, David Chen, Steve Rust, Yungui Huang, Rajiv Ramnath
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Document Ranking, Information Retrieval, Knowledge Graphs, Language Modeling, Language Modelling, Question Answering, Retrieval

In this work, we develop a novel, completely unsupervised, neural language model-based document ranking approach to semantic tagging of documents, using the document to be tagged as a query into the GLM to retrieve candidate phrases from top-ranked related documents, thus associating every document with novel related concepts extracted from the text. For this we extend the word embedding-based general language model due to Ganguly et al 2015, to employ phrasal embeddings, and use the semantic tags thus obtained for downstream query expansion, both directly and in feedback loop settings. Our method, evaluated using the TREC 2016 clinical decision support challenge dataset, shows statistically significant improvement not only over various baselines that use standard MeSH terms and UMLS concepts for query expansion, but also over baselines using human expert{--}assigned concept tags for the queries, run on top of a standard Okapi BM25{--}based document retrieval system.
Domain Adaptation for Disease Phrase Matching with Adversarial Networks
Authors: Miaofeng Liu, Jialong Han, Haisong Zhang, Yan Song
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Domain Adaptation, Entity Linking, Management, Natural Language Inference

With the development of medical information management, numerous medical data are being classified, indexed, and searched in various systems. Disease phrase matching, i.e., deciding whether two given disease phrases interpret each other, is a basic but crucial preprocessing step for the above tasks. Being capable of relieving the scarceness of annotations, domain adaptation is generally considered useful in medical systems. However, efforts on applying it to phrase matching remain limited. This paper presents a domain-adaptive matching network for disease phrases. Our network achieves domain adaptation by adversarial training, i.e., preferring features indicating whether the two phrases match, rather than which domain they come from. Experiments suggest that our model has the best performance among the very few non-adaptive or adaptive methods that can benefit from out-of-domain annotations.
Predicting Discharge Disposition Using Patient Complaint Notes in Electronic Medical Records
Authors: Mohamad Salimi, Alla Rozovskaya
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

Overcrowding in emergency rooms is a major challenge faced by hospitals across the United States. Overcrowding can result in longer wait times, which, in turn, has been shown to adversely affect patient satisfaction, clinical outcomes, and procedure reimbursements. This paper presents research that aims to automatically predict discharge disposition of patients who received medical treatment in an emergency department. We make use of a corpus that consists of notes containing patient complaints, diagnosis information, and disposition, entered by health care providers. We use this corpus to develop a model that uses the complaint and diagnosis information to predict patient disposition. We show that the proposed model substantially outperforms the baseline of predicting the most common disposition type. The long-term goal of this research is to build a model that can be implemented as a real-time service in an application to predict disposition as patients arrive.
Bacteria and Biotope Entity Recognition Using A Dictionary-Enhanced Neural Network Model
Authors: Qiuyue Wang, Xiaofeng Meng
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Feature Engineering

Automatic recognition of biomedical entities in text is the crucial initial step in biomedical text mining. In this pa-per, we investigate employing modern neural network models for recognizing biomedical entities. To compensate for the small amount of training data in biomedical domain, we propose to integrate dictionaries into the neural model. Our experiments on BB3 data sets demonstrate that state-of-the-art neural network model is promising in recognizing biomedical entities even with very little training data. When integrated with dictionaries, its performance could be greatly improved, achieving the competitive performance compared with the best dictionary-based system on the entities with specific terminology, and much higher performance on the entities with more general terminology.
SingleCite: Towards an improved Single Citation Search in PubMed
Authors: Lana Yeganova, Donald C. Comeau, Won Kim, W. John Wilbur, Zhiyong Lu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

A search that is targeted at finding a specific document in databases is called a Single Citation search. Single citation searches are particularly important for scholarly databases, such as PubMed, because users are frequently searching for a specific publication. In this work we describe SingleCite, a single citation matching system designed to facilitate user{'}s search for a specific document. We report on the progress that has been achieved towards building that functionality.