pwc-archive/papers-with-abstracts huggingface dataset:
The impact of simple feature engineering in multilingual medical NER
Authors: Rebecka Weegar, Arantza Casillas, Arantza Diaz de Ilarraza, Maite Oronoz, Alicia P{\'e}rez, Koldo Gojenola
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Feature Engineering, Lemmatization, Named Entity Recognition (NER), NER, POS, Relation Extraction

The goal of this paper is to examine the impact of simple feature engineering mechanisms before applying more sophisticated techniques to the task of medical NER. Sometimes papers using scientifically sound techniques present raw baselines that could be improved adding simple and cheap features. This work focuses on entity recognition for the clinical domain for three languages: English, Swedish and Spanish. The task is tackled using simple features, starting from the window size, capitalization, prefixes, and moving to POS and semantic tags. This work demonstrates that a simple initial step of feature engineering can improve the baseline results significantly. Hence, the contributions of this paper are: first, a short list of guidelines well supported with experimental results on three languages and, second, a detailed description of the relevance of these features for medical NER.
MedNLPDoc: Japanese Shared Task for Clinical NLP
Authors: Eiji Aramaki, Yoshinobu Kano, Tomoko Ohkuma, Mizuki Morita
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Information Retrieval

Due to the recent replacements of physical documents with electronic medical records (EMR), the importance of information processing in medical fields has been increased. We have been organizing the MedNLP task series in NTCIR-10 and 11. These workshops were the first shared tasks which attempt to evaluate technologies that retrieve important information from medical reports written in Japanese. In this report, we describe the NTCIR-12 MedNLPDoc task which is designed for more advanced and practical use for the medical fields. This task is considered as a multi-labeling task to a patient record. This report presents results of the shared task, discusses and illustrates remained issues in the medical natural language processing field.
Semi-supervised Clustering of Medical Text
Authors: Pracheta Sahoo, Asif Ekbal, Sriparna Saha, Diego Moll{\'a}, N, Kaushik an
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Clustering

Semi-supervised clustering is an attractive alternative for traditional (unsupervised) clustering in targeted applications. By using the information of a small annotated dataset, semi-supervised clustering can produce clusters that are customized to the application domain. In this paper, we present a semi-supervised clustering technique based on a multi-objective evolutionary algorithm (NSGA-II-clus). We apply this technique to the task of clustering medical publications for Evidence Based Medicine (EBM) and observe an improvement of the results against unsupervised and other semi-supervised clustering techniques.
Deep Learning Architecture for Patient Data De-identification in Clinical Records
Authors: Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Decision Making, De-identification, Named Entity Recognition (NER)

Rapid growth in Electronic Medical Records (EMR) has emerged to an expansion of data in the clinical domain. The majority of the available health care information is sealed in the form of narrative documents which form the rich source of clinical information. Text mining of such clinical records has gained huge attention in various medical applications like treatment and decision making. However, medical records enclose patient Private Health Information (PHI) which can reveal the identities of the patients. In order to retain the privacy of patients, it is mandatory to remove all the PHI information prior to making it publicly available. The aim is to de-identify or encrypt the PHI from the patient medical records. In this paper, we propose an algorithm based on deep learning architecture to solve this problem. We perform de-identification of seven PHI terms from the clinical records. Experiments on benchmark datasets show that our proposed approach achieves encouraging performance, which is better than the baseline model developed with Conditional Random Field.
Neural Clinical Paraphrase Generation with Attention
Authors: Sadid A. Hasan, Bo Liu, Joey Liu, Ashequl Qadir, Kathy Lee, Vivek Datla, Aaditya Prakash, Oladimeji Farri
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Document Summarization, Information Retrieval, Machine Translation, NMT, Paraphrase Generation, Question Answering, Sentence Compression, Text Generation

Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact. Clinical paraphrase generation is especially vital in building patient-centric clinical decision support (CDS) applications where users are able to understand complex clinical jargons via easily comprehensible alternative paraphrases. This paper presents Neural Clinical Paraphrase Generation (NCPG), a novel approach that casts the task as a monolingual neural machine translation (NMT) problem. We propose an end-to-end neural network built on an attention-based bidirectional Recurrent Neural Network (RNN) architecture with an encoder-decoder framework to perform the task. Conventional bilingual NMT models mostly rely on word-level modeling and are often limited by out-of-vocabulary (OOV) issues. In contrast, we represent the source and target paraphrase pairs as character sequences to address this limitation. To the best of our knowledge, this is the first work that uses attention-based RNNs for clinical paraphrase generation and also proposes an end-to-end character-level modeling for this task. Extensive experiments on a large curated clinical paraphrase corpus show that the attention-based NCPG models achieve improvements of up to 5.2 BLEU points and 0.5 METEOR points over a non-attention based strong baseline for word-level modeling, whereas further gains of up to 6.1 BLEU points and 1.3 METEOR points are obtained by the character-level NCPG models over their word-level counterparts. Overall, our models demonstrate comparable performance relative to the state-of-the-art phrase-based non-neural models.
Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP
Authors: Kirk Roberts
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Deep Learning, Word Embeddings

The proliferation of deep learning methods in natural language processing (NLP) and the large amounts of data they often require stands in stark contrast to the relatively data-poor clinical NLP domain. In particular, large text corpora are necessary to build high-quality word embeddings, yet often large corpora that are suitably representative of the target clinical data are unavailable. This forces a choice between building embeddings from small clinical corpora and less representative, larger corpora. This paper explores this trade-off, as well as intermediate compromise solutions. Two standard clinical NLP tasks (the i2b2 2010 concept and assertion tasks) are evaluated with commonly used deep learning models (recurrent neural networks and convolutional neural networks) using a set of six corpora ranging from the target i2b2 data to large open-domain datasets. While combinations of corpora are generally found to work best, the single-best corpus is generally task-dependent.
Inference of ICD Codes from Japanese Medical Records by Searching Disease Names
Authors: Masahito Sakishita, Yoshinobu Kano
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Information Retrieval

Importance of utilizing medical information is getting increased as electronic health records (EHRs) are widely used nowadays. We aim to assign international standardized disease codes, ICD-10, to Japanese textual information in EHRs for users to reuse the information accurately. In this paper, we propose methods to automatically extract diagnosis and to assign ICD codes to Japanese medical records. Due to the lack of available training data, we dare employed rule-based methods rather than machine learning. We observed characteristics of medical records carefully, writing rules to make effective methods by hand. We applied our system to the NTCIR-12 MedNLPDoc shared task data where participants are required to assign ICD-10 codes of possible diagnosis in given EHRs. In this shared task, our system achieved the highest F-measure score among all participants in the most severe evaluation criteria. Through comparison with other approaches, we show that our approach could be a useful milestone for the future development of Japanese medical record processing.
A fine-grained corpus annotation schema of German nephrology records
Authors: Rol Roller, Hans Uszkoreit, Feiyu Xu, Laura Seiffe, Michael Mikhailov, Oliver Staeck, Klemens Budde, Fabian Halleck, Danilo Schmidt
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: BIG-bench Machine Learning, Named Entity Recognition (NER)

In this work we present a fine-grained annotation schema to detect named entities in German clinical data of chronically ill patients with kidney diseases. The annotation schema is driven by the needs of our clinical partners and the linguistic aspects of German language. In order to generate annotations within a short period, the work also presents a semi-automatic annotation which uses additional sources of knowledge such as UMLS, to pre-annotate concepts in advance. The presented schema will be used to apply novel techniques from natural language processing and machine learning to support doctors treating their patients by improved information access from unstructured German texts.
Detecting Japanese Patients with Alzheimer's Disease based on Word Category Frequencies
Authors: Daisaku Shibata, Shoko Wakamiya, Ayae Kinoshita, Eiji Aramaki
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

In recent years, detecting Alzheimer disease (AD) in early stages based on natural language processing (NLP) has drawn much attention. To date, vocabulary size, grammatical complexity, and fluency have been studied using NLP metrics. However, the content analysis of AD narratives is still unreachable for NLP. This study investigates features of the words that AD patients use in their spoken language. After recruiting 18 examinees of 53{--}90 years old (mean: 76.89), they were divided into two groups based on MMSE scores. The AD group comprised 9 examinees with scores of 21 or lower. The healthy control group comprised 9 examinees with a score of 22 or higher. Linguistic Inquiry and Word Count (LIWC) classified words were used to categorize the words that the examinees used. The word frequency was found from observation. Significant differences were confirmed for the usage of impersonal pronouns in the AD group. This result demonstrated the basic feasibility of the proposed NLP-based detection approach.
Prediction of Key Patient Outcome from Sentence and Word of Medical Text Records
Authors: Takanori Yamashita, Yoshifumi Wakata, Hidehisa Soejima, Naoki Nakashima, Sachio Hirokawa
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Sentence

The number of unstructured medical records kept in hospital information systems is increasing. The conditions of patients are formulated as outcomes in clinical pathway. A variance of an outcome describes deviations from standards of care like a patient{'}s bad condition. The present paper applied text mining to extract feature words and phrases of the variance from admission records. We report the cases the variances of {``}pain control{''} and {``}no neuropathy worsening{''} in cerebral infarction.
Unsupervised Abbreviation Detection in Clinical Narratives
Authors: Markus Kreuzthaler, Michel Oleynik, Alex Avian, er, Stefan Schulz
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Feature Engineering, Sentence

Clinical narratives in electronic health record systems are a rich resource of patient-based information. They constitute an ongoing challenge for natural language processing, due to their high compactness and abundance of short forms. German medical texts exhibit numerous ad-hoc abbreviations that terminate with a period character. The disambiguation of period characters is therefore an important task for sentence and abbreviation detection. This task is addressed by a combination of co-occurrence information of word types with trailing period characters, a large domain dictionary, and a simple rule engine, thus merging statistical and dictionary-based disambiguation strategies. An F-measure of 0.95 could be reached by using the unsupervised approach presented in this paper. The results are promising for a domain-independent abbreviation detection strategy, because our approach avoids retraining of models or use case specific feature engineering efforts required for supervised machine learning approaches.
Automated Anonymization as Spelling Variant Detection
Authors: Steven Kester Yuwono, Hwee Tou Ng, Kee Yuan Ngiam
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Sentence

The issue of privacy has always been a concern when clinical texts are used for research purposes. Personal health information (PHI) (such as name and identification number) needs to be removed so that patients cannot be identified. Manual anonymization is not feasible due to the large number of clinical texts to be anonymized. In this paper, we tackle the task of anonymizing clinical texts written in sentence fragments and which frequently contain symbols, abbreviations, and misspelled words. Our clinical texts therefore differ from those in the i2b2 shared tasks which are in prose form with complete sentences. Our clinical texts are also part of a structured database which contains patient name and identification number in structured fields. As such, we formulate our anonymization task as spelling variant detection, exploiting patients{'} personal information in the structured fields to detect their spelling variants in clinical texts. We successfully anonymized clinical texts consisting of more than 200 million words, using minimum edit distance and regular expression patterns.
Zooming in on Gender Differences in Social Media
Authors: Aparna Garimella, Rada Mihalcea
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: General Classification, Sociology, text-classification, Text Classification, Word Sense Disambiguation

Men are from Mars and women are from Venus - or so the genre of relationship literature would have us believe. But there is some truth in this idea, and researchers in fields as diverse as psychology, sociology, and linguistics have explored ways to better understand the differences between genders. In this paper, we take another look at the problem of gender discrimination and attempt to move beyond the typical surface-level text classification approach, by (1) identifying semantic and psycholinguistic word classes that reflect systematic differences between men and women and (2) finding differences between genders in the ways they use the same words. We describe several experiments and report results on a large collection of blogs authored by men and women.
The Effect of Gender and Age Differences on the Recognition of Emotions from Facial Expressions
Authors: Daniela Schneevogt, Patrizia Paggio
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Cultural Vocal Bursts Intensity Prediction, Emotion Recognition, Sentiment Analysis

Recent studies have demonstrated gender and cultural differences in the recognition of emotions in facial expressions. However, most studies were conducted on American subjects. In this paper, we explore the generalizability of several findings to a non-American culture in the form of Danish subjects. We conduct an emotion recognition task followed by two stereotype questionnaires with different genders and age groups. While recent findings (Krems et al., 2015) suggest that women are biased to see anger in neutral facial expressions posed by females, in our sample both genders assign higher ratings of anger to all emotions expressed by females. Furthermore, we demonstrate an effect of gender on the fear-surprise-confusion observed by Tomkins and McCarter (1964); females overpredict fear, while males overpredict surprise.
A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts
Authors: Fei Liu, Julien Perez, Scott Nowson
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Feature Engineering, Part-Of-Speech Tagging, Personality Trait Recognition, regression, Sentence, Sentiment Analysis

Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits compared with prior work. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.
A graphical framework to detect and categorize diverse opinions from online news
Authors: Ankan Mullick, Pawan Goyal, Niloy Ganguly
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Diversity, General Classification, Opinion Mining

This paper proposes a graphical framework to extract opinionated sentences which highlight different contexts within a given news article by introducing the concept of diversity in a graphical model for opinion detection.We conduct extensive evaluations and find that the proposed modification leads to impressive improvement in performance and makes the final results of the model much more usable. The proposed method (OP-D) not only performs much better than the other techniques used for opinion detection as well as introducing diversity, but is also able to select opinions from different categories (Asher et al. 2009). By developing a classification model which categorizes the identified sentences into various opinion categories, we find that OP-D is able to push opinions from different categories uniformly among the top opinions.
Active learning for detection of stance components
Authors: Maria Skeppstedt, Magnus Sahlgren, Carita Paradis, Andreas Kerren
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Active Learning, Opinion Mining, Sentiment Analysis, Stance Detection

Automatic detection of five language components, which are all relevant for expressing opinions and for stance taking, was studied: positive sentiment, negative sentiment, speculation, contrast and condition. A resource-aware approach was taken, which included manual annotation of 500 training samples and the use of limited lexical resources. Active learning was compared to random selection of training data, as well as to a lexicon-based method. Active learning was successful for the categories speculation, contrast and condition, but not for the two sentiment categories, for which results achieved when using active learning were similar to those achieved when applying a random selection of training data. This difference is likely due to a larger variation in how sentiment is expressed than in how speakers express the other three categories. This larger variation was also shown by the lower recall results achieved by the lexicon-based approach for sentiment than for the categories speculation, contrast and condition.
Detecting Opinion Polarities using Kernel Methods
Authors: Rasoul Kaljahi, Jennifer Foster
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

We investigate the application of kernel methods to representing both structural and lexical knowledge for predicting polarity of opinions in consumer product review. We introduce any-gram kernels which model lexical information in a significantly faster way than the traditional n-gram features, while capturing all possible orders of n-grams n in a sequence without the need to explicitly present a pre-specified set of such orders. We also present an effective format to represent constituency and dependency structure together with aspect terms and sentiment polarity scores. Furthermore, we modify the traditional tree kernel function to compute the similarity based on word embedding vectors instead of exact string match and present experiments using the new models.
Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games
Authors: Andrew Cattle, Xiaojuan Ma
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Acquisition, Sentiment Analysis

This paper explores humour recognition for Twitter-based hashtag games. Given their popularity, frequency, and relatively formulaic nature, these games make a good target for computational humour research and can leverage Twitter likes and retweets as humour judgments. In this work, we use pair-wise relative humour judgments to examine several measures of semantic relatedness between setups and punchlines on a hashtag game corpus we collected and annotated. Results show that perplexity, Normalized Google Distance, and free-word association-based features are all useful in identifying {``}funnier{''} hashtag game responses. In fact, we provide empirical evidence that funnier punchlines tend to be more obscure, although more obscure punchlines are not necessarily rated funnier. Furthermore, the asymmetric nature of free-word association features allows us to see that while punchlines should be harder to predict given a setup, they should also be relatively easy to understand in context.
Innovative Semi-Automatic Methodology to Annotate Emotional Corpora
Authors: Lea Canales, Carlo Strapparava, Ester Boldrini, Patricio Mart{\'\i}nez-Barco
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Detecting depression or personality traits, tutoring and student behaviour systems, or identifying cases of cyber-bulling are a few of the wide range of the applications, in which the automatic detection of emotion is a crucial element. Emotion detection has the potential of high impact by contributing the benefit of business, society, politics or education. Given this context, the main objective of our research is to contribute to the resolution of one of the most important challenges in textual emotion detection task: the problems of emotional corpora annotation. This will be tackled by proposing of a new semi-automatic methodology. Our innovative methodology consists in two main phases: (1) an automatic process to pre-annotate the unlabelled sentences with a reduced number of emotional categories; and (2) a refinement manual process where human annotators will determine which is the predominant emotion between the emotional categories selected in the phase 1. Our proposal in this paper is to show and evaluate the pre-annotation process to analyse the feasibility and the benefits by the methodology proposed. The results obtained are promising and allow obtaining a substantial improvement of annotation time and cost and confirm the usefulness of our pre-annotation process to improve the annotation task.