pwc-archive/papers-with-abstracts huggingface dataset:
Investigating the Documentation of Electronic Cigarette Use in the Veteran Affairs Electronic Health Record: A Pilot Study
Authors: Danielle Mowery, Brett South, Olga Patterson, Shu-Hong Zhu, Mike Conway
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Epidemiology

In this paper, we present pilot work on characterising the documentation of electronic cigarettes (e-cigarettes) in the United States Veterans Administration Electronic Health Record. The Veterans Health Administration is the largest health care system in the United States with 1,233 health care facilities nationwide, serving 8.9 million veterans per year. We identified a random sample of 2000 Veterans Administration patients, coded as current tobacco users, from 2008 to 2014. Using simple keyword matching techniques combined with qualitative analysis, we investigated the prevalence and distribution of e-cigarette terms in these clinical notes, discovering that for current smokers, 11.9{\%} of patient records contain an e-cigarette related term.
Automated Preamble Detection in Dictated 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: Speech Recognition, Word Embeddings

Dictated medical reports very often feature a preamble containing metainformation about the report such as patient and physician names, location and name of the clinic, date of procedure, and so on. In the medical transcription process, the preamble is usually omitted from the final report, as it contains information already available in the electronic medical record. We present a method which is able to automatically identify preambles in medical dictations. The method makes use of state-of-the-art NLP techniques including word embeddings and Bi-LSTMs and achieves preamble detection performance superior to humans.
A Biomedical Question Answering System in BioASQ 2017
Authors: Mourad Sarrouti, Said Ouatik El Alaoui
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Question Answering

Question answering, the identification of short accurate answers to users questions, is a longstanding challenge widely studied over the last decades in the open domain. However, it still requires further efforts in the biomedical domain. In this paper, we describe our participation in phase B of task 5b in the 2017 BioASQ challenge using our biomedical question answering system. Our system, dealing with four types of questions (i.e., yes/no, factoid, list, and summary), is based on (1) a dictionary-based approach for generating the exact answers of yes/no questions, (2) UMLS metathesaurus and term frequency metric for extracting the exact answers of factoid and list questions, and (3) the BM25 model and UMLS concepts for retrieving the ideal answers (i.e., paragraph-sized summaries). Preliminary results show that our system achieves good and competitive results in both exact and ideal answers extraction tasks as compared with the participating systems.
Adapting Pre-trained Word Embeddings For Use In Medical Coding
Authors: Kevin Patel, Divya Patel, Mansi Golakiya, Pushpak Bhattacharyya, Nilesh Birari
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Word Embeddings

Word embeddings are a crucial component in modern NLP. Pre-trained embeddings released by different groups have been a major reason for their popularity. However, they are trained on generic corpora, which limits their direct use for domain specific tasks. In this paper, we propose a method to add task specific information to pre-trained word embeddings. Such information can improve their utility. We add information from medical coding data, as well as the first level from the hierarchy of ICD-10 medical code set to different pre-trained word embeddings. We adapt CBOW algorithm from the word2vec package for our purpose. We evaluated our approach on five different pre-trained word embeddings. Both the original word embeddings, and their modified versions (the ones with added information) were used for automated review of medical coding. The modified word embeddings give an improvement in f-score by 1{\%} on the 5-fold evaluation on a private medical claims dataset. Our results show that adding extra information is possible and beneficial for the task at hand.
Initializing neural networks for hierarchical multi-label text classification
Authors: Simon Baker, Anna Korhonen
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Classification, General Classification, Multi-Label Classification, Multi Label Text Classification, Multi-Label Text Classification, Sentence, text-classification, Text Classification

Many tasks in the biomedical domain require the assignment of one or more predefined labels to input text, where the labels are a part of a hierarchical structure (such as a taxonomy). The conventional approach is to use a one-vs.-rest (OVR) classification setup, where a binary classifier is trained for each label in the taxonomy or ontology where all instances not belonging to the class are considered negative examples. The main drawbacks to this approach are that dependencies between classes are not leveraged in the training and classification process, and the additional computational cost of training parallel classifiers. In this paper, we apply a new method for hierarchical multi-label text classification that initializes a neural network model final hidden layer such that it leverages label co-occurrence relations such as hypernymy. This approach elegantly lends itself to hierarchical classification. We evaluated this approach using two hierarchical multi-label text classification tasks in the biomedical domain using both sentence- and document-level classification. Our evaluation shows promising results for this approach.
Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks
Authors: Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana Savova
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Relation, Relation Extraction, Semantic Parsing, Temporal Relation Extraction, Word Embeddings

Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing. However, they might not be an ideal representation for time expressions, which are long, highly varied, and semantically complex. We describe a method for representing time expressions with single pseudo-tokens for CNNs. With this method, we establish a new state-of-the-art result for a clinical temporal relation extraction task.
Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods
Authors: Sarvnaz Karimi, Xiang Dai, Hamed Hassanzadeh, Anthony Nguyen
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Decision Making, Deep Learning, Feature Engineering, General Classification, Multi-Label Classification, Text Classification

Diagnosis autocoding services and research intend to both improve the productivity of clinical coders and the accuracy of the coding. It is an important step in data analysis for funding and reimbursement, as well as health services planning and resource allocation. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters that could be used in setting up a convolutional neural network for autocoding with comparable results to that of conventional methods.
Automatic classification of doctor-patient questions for a virtual patient record query task
Authors: Leonardo Campillos Llanos, Sophie Rosset, Pierre Zweigenbaum
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: BIG-bench Machine Learning, Dialogue Management, General Classification, Information Retrieval, Named Entity Recognition (NER), Question Answering

We present the work-in-progress of automating the classification of doctor-patient questions in the context of a simulated consultation with a virtual patient. We classify questions according to the computational strategy (rule-based or other) needed for looking up data in the clinical record. We compare {`}traditional{'} machine learning methods (Gaussian and Multinomial Naive Bayes, and Support Vector Machines) and a neural network classifier (FastText). We obtained the best results with the SVM using semantic annotations, whereas the neural classifier achieved promising results without it.
Assessing the performance of Olelo, a real-time biomedical question answering application
Authors: Mariana Neves, Fabian Eckert, Hendrik Folkerts, Matthias Uflacker
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Document Summarization, Information Retrieval, Multi-Document Summarization, Passage Retrieval, Question Answering, Retrieval, Semantic Role Labeling

Question answering (QA) can support physicians and biomedical researchers to find answers to their questions in the scientific literature. Such systems process large collections of documents in real time and include many natural language processing (NLP) procedures. We recently developed Olelo, a QA system for biomedicine which includes various NLP components, such as question processing, document and passage retrieval, answer processing and multi-document summarization. In this work, we present an evaluation of our system on the the fifth BioASQ challenge. We participated with the current state of the application and with an extension based on semantic role labeling that we are currently investigating. In addition to the BioASQ evaluation, we compared our system to other on-line biomedical QA systems in terms of the response time and the quality of the answers.
Clinical Event Detection with Hybrid Neural Architecture
Authors: Adyasha Maharana, Meliha Yetisgen
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Event Detection, Feature Engineering, Word Embeddings

Event detection from clinical notes has been traditionally solved with rule based and statistical natural language processing (NLP) approaches that require extensive domain knowledge and feature engineering. In this paper, we have explored the feasibility of approaching this task with recurrent neural networks, clinical word embeddings and introduced a hybrid architecture to improve detection for entities with smaller representation in the dataset. A comparative analysis is also done which reveals the complementary behavior of neural networks and conditional random fields in clinical entity detection.
Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision
Authors: Aakanksha Naik, Chris Bogart, Carolyn Rose
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Event Detection

In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision. In recent years, several online support groups have been established which has led to a huge increase in the amount of patient-authored text available. Creating systems which can automatically extract important medical events and create disease progression timelines for users from such text can help in patient health monitoring as well as studying links between medical events and users{'} participation in support groups. Prior work in this domain has used manually constructed keyword sets to detect medical events. In this work, our aim is to perform medical event detection using minimal supervision in order to develop a more general timeline construction system. Our system achieves an accuracy of 55.17{\%}, which is 92{\%} of the performance achieved by a supervised baseline system.
Detecting mentions of pain and acute confusion in Finnish clinical text
Authors: Hans Moen, Kai Hakala, Farrokh Mehryary, Laura-Maria Peltonen, Tapio Salakoski, Filip Ginter, Sanna Salanter{\"a}
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: General Classification, Information Retrieval, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Sentence

We study and compare two different approaches to the task of automatic assignment of predefined classes to clinical free-text narratives. In the first approach this is treated as a traditional mention-level named-entity recognition task, while the second approach treats it as a sentence-level multi-label classification task. Performance comparison across these two approaches is conducted in the form of sentence-level evaluation and state-of-the-art methods for both approaches are evaluated. The experiments are done on two data sets consisting of Finnish clinical text, manually annotated with respect to the topics pain and acute confusion. Our results suggest that the mention-level named-entity recognition approach outperforms sentence-level classification overall, but the latter approach still manages to achieve the best prediction scores on several annotation classes.
A Multi-strategy Query Processing Approach for Biomedical Question Answering: USTB\_PRIR at BioASQ 2017 Task 5B
Authors: Zan-Xia Jin, Bo-Wen Zhang, Fan Fang, Le-Le Zhang, Xu-Cheng Yin
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Articles, Information Retrieval, Question Answering, Retrieval

This paper describes the participation of USTB{\_}PRIR team in the 2017 BioASQ 5B on question answering, including document retrieval, snippet retrieval, and concept retrieval task. We introduce different multimodal query processing strategies to enrich query terms and assign different weights to them. Specifically, sequential dependence model (SDM), pseudo-relevance feedback (PRF), fielded sequential dependence model (FSDM) and Divergence from Randomness model (DFRM) are respectively performed on different fields of PubMed articles, sentences extracted from relevant articles, the five terminologies or ontologies (MeSH, GO, Jochem, Uniprot and DO) to achieve better search performances. Preliminary results show that our systems outperform others in the document and snippet retrieval task in the first two batches.
On the ``Calligraphy'' of Books
Authors: Vanessa Queiroz Marinho, Henrique Ferraz de Arruda, Thales Sinelli, Luciano da Fontoura Costa, Diego Raphael Amancio
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Authorship Attribution, Machine Translation, Sentiment Analysis

Authorship attribution is a natural language processing task that has been widely studied, often by considering small order statistics. In this paper, we explore a complex network approach to assign the authorship of texts based on their mesoscopic representation, in an attempt to capture the flow of the narrative. Indeed, as reported in this work, such an approach allowed the identification of the dominant narrative structure of the studied authors. This has been achieved due to the ability of the mesoscopic approach to take into account relationships between different, not necessarily adjacent, parts of the text, which is able to capture the story flow. The potential of the proposed approach has been illustrated through principal component analysis, a comparison with the chance baseline method, and network visualization. Such visualizations reveal individual characteristics of the authors, which can be understood as a kind of calligraphy.
Merging knowledge bases in different languages
Authors: Jer{\'o}nimo Hern{\'a}ndez-Gonz{\'a}lez, Estevam R. Hruschka Jr., Tom M. Mitchell
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Cultural Vocal Bursts Intensity Prediction

Recently, different systems which learn to populate and extend a knowledge base (KB) from the web in different languages have been presented. Although a large set of concepts should be learnt independently from the language used to read, there are facts which are expected to be more easily gathered in local language (e.g., culture or geography). A system that merges KBs learnt in different languages will benefit from the complementary information as long as common beliefs are identified, as well as from redundancy present in web pages written in different languages. In this paper, we deal with the problem of identifying equivalent beliefs (or concepts) across language specific KBs, assuming that they share the same ontology of categories and relations. In a case study with two KBs independently learnt from different inputs, namely web pages written in English and web pages written in Portuguese respectively, we report on the results of two methodologies: an approach based on personalized PageRank and an inference technique to find out common relevant paths through the KBs. The proposed inference technique efficiently identifies relevant paths, outperforming the baseline (a dictionary-based classifier) in the vast majority of tested categories.
Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings
Authors: Thomas Alex Trost, er, Dietrich Klakow
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Clustering, Dimensionality Reduction, Word Embeddings

Word embeddings are high-dimensional vector representations of words and are thus difficult to interpret. In order to deal with this, we introduce an unsupervised parameter free method for creating a hierarchical graphical clustering of the full ensemble of word vectors and show that this structure is a geometrically meaningful representation of the original relations between the words. This newly obtained representation can be used for better understanding and thus improving the embedding algorithm and exhibits semantic meaning, so it can also be utilized in a variety of language processing tasks like categorization or measuring similarity.
Spectral Graph-Based Method of Multimodal Word Embedding
Authors: Kazuki Fukui, Takamasa Oshikiri, Hidetoshi Shimodaira
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Graph Embedding, Image Retrieval, Machine Translation, Part-Of-Speech Tagging, Question Answering, Text Classification, Visual Question Answering (VQA), Word Similarity

In this paper, we propose a novel method for multimodal word embedding, which exploit a generalized framework of multi-view spectral graph embedding to take into account visual appearances or scenes denoted by words in a corpus. We evaluated our method through word similarity tasks and a concept-to-image search task, having found that it provides word representations that reflect visual information, while somewhat trading-off the performance on the word similarity tasks. Moreover, we demonstrate that our method captures multimodal linguistic regularities, which enable recovering relational similarities between words and images by vector arithmetics.
Graph Methods for Multilingual FrameNets
Authors: Collin F. Baker, Michael Ellsworth
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Semantic Role Labeling, Sentence

This paper introduces a new, graph-based view of the data of the FrameNet project, which we hope will make it easier to understand the mixture of semantic and syntactic information contained in FrameNet annotation. We show how English FrameNet and other Frame Semantic resources can be represented as sets of interconnected graphs of frames, frame elements, semantic types, and annotated instances of them in text. We display examples of the new graphical representation based on the annotations, which combine Frame Semantics and Construction Grammar, thus capturing most of the syntax and semantics of each sentence. We consider how graph theory could help researchers to make better use of FrameNet data for tasks such as automatic Frame Semantic role labeling, paraphrasing, and translation. Finally, we describe the development of FrameNet-like lexical resources for other languages in the current Multilingual FrameNet project. which seeks to discover cross-lingual alignments, both in the lexicon (for frames and lexical units within frames) and across parallel or comparable texts. We conclude with an example showing graphically the semantic and syntactic similarities and differences between parallel sentences in English and Japanese. We will release software for displaying such graphs from the current data releases.
Work Hard, Play Hard: Email Classification on the Avocado and Enron Corpora
Authors: Sakhar Alkhereyf, Owen Rambow
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Classification, General Classification

In this paper, we present an empirical study of email classification into two main categories {``}Business{''} and {``}Personal{''}. We train on the Enron email corpus, and test on the Enron and Avocado email corpora. We show that information from the email exchange networks improves the performance of classification. We represent the email exchange networks as social networks with graph structures. For this classification task, we extract social networks features from the graphs in addition to lexical features from email content and we compare the performance of SVM and Extra-Trees classifiers using these features. Combining graph features with lexical features improves the performance on both classifiers. We also provide manually annotated sets of the Avocado and Enron email corpora as a supplementary contribution.
A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit
Authors: Amrith Krishna, Pavankumar Satuluri, Harshavardhan Ponnada, Muneeb Ahmed, Gulab Arora, Kaustubh Hiware, Pawan Goyal
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: Semantic Similarity, Semantic Textual Similarity

Derivational nouns are widely used in Sanskrit corpora and represent an important cornerstone of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. Our novelty comes in the design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 {\%} to that of the entire dataset.
Evaluating text coherence based on semantic similarity graph
Authors: Jan Wira Gotama Putra, Takenobu Tokunaga
Date: 2017-08-01 00:00:00
Proceeding: WS 2017 8
Tasks: graph construction, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Similarity

Coherence is a crucial feature of text because it is indispensable for conveying its communication purpose and meaning to its readers. In this paper, we propose an unsupervised text coherence scoring based on graph construction in which edges are established between semantically similar sentences represented by vertices. The sentence similarity is calculated based on the cosine similarity of semantic vectors representing sentences. We provide three graph construction methods establishing an edge from a given vertex to a preceding adjacent vertex, to a single similar vertex, or to multiple similar vertices. We evaluated our methods in the document discrimination task and the insertion task by comparing our proposed methods to the supervised (Entity Grid) and unsupervised (Entity Graph) baselines. In the document discrimination task, our method outperformed the unsupervised baseline but could not do the supervised baseline, while in the insertion task, our method outperformed both baselines.