pwc-archive/papers-with-abstracts huggingface dataset:
Hierarchical Nested Named Entity Recognition
Authors: Zita Marinho, Afonso Mendes, Mir, Sebasti{\~a}o a, David Nogueira
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Nested Named Entity Recognition

In the medical domain and other scientific areas, it is often important to recognize different levels of hierarchy in mentions, such as those related to specific symptoms or diseases associated with different anatomical regions. Unlike previous approaches, we build a transition-based parser that explicitly models an arbitrary number of hierarchical and nested mentions, and propose a loss that encourages correct predictions of higher-level mentions. We further introduce a set of modifier classes which introduces certain concepts that change the meaning of an entity, such as absence, or uncertainty about a given disease. Our proposed model achieves state-of-the-art results in medical entity recognition datasets, using both nested and hierarchical mentions.
A Novel System for Extractive Clinical Note Summarization using EHR Data
Authors: Jennifer Liang, Ching-Huei Tsou, Ananya Poddar
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Extractive Text Summarization, Management, Sentence, Text Summarization

While much data within a patient{'}s electronic health record (EHR) is coded, crucial information concerning the patient{'}s care and management remain buried in unstructured clinical notes, making it difficult and time-consuming for physicians to review during their usual clinical workflow. In this paper, we present our clinical note processing pipeline, which extends beyond basic medical natural language processing (NLP) with concept recognition and relation detection to also include components specific to EHR data, such as structured data associated with the encounter, sentence-level clinical aspects, and structures of the clinical notes. We report on the use of this pipeline in a disease-specific extractive text summarization task on clinical notes, focusing primarily on progress notes by physicians and nurse practitioners. We show how the addition of EHR-specific components to the pipeline resulted in an improvement in our overall system performance and discuss the potential impact of EHR-specific components on other higher-level clinical NLP tasks.
Study of lexical aspect in the French medical language. Development of a lexical resource
Authors: Agathe Pierson, C{\'e}drick Fairon
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Temporal Information Extraction

This paper details the development of a linguistic resource designed to improve temporal information extraction systems and to integrate aspectual values. After a brief review of recent works in temporal information extraction for the medical area, we discuss the linguistic notion of aspect and how it got a place in the NLP field. Then, we present our clinical data and describe the five-step approach adopted in this study. Finally, we represent the linguistic resource itself and explain how we elaborated it and which properties were selected for the creation of the tables.
A BERT-based Universal Model for Both Within- and Cross-sentence Clinical Temporal Relation Extraction
Authors: Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana Savova
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Relation, Relation Extraction, Sentence, Temporal Relation Extraction

Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated {``}silver{''} instances.
A General-Purpose Annotation Model for Knowledge Discovery: Case Study in Spanish Clinical Text
Authors: Alej Piad-Morffis, ro, Yoan Guit{\'e}rrez, Suilan Estevez-Velarde, Rafael Mu{\~n}oz
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Relation, Relation Extraction

Knowledge discovery from text in natural language is a task usually aided by the manual construction of annotated corpora. Specifically in the clinical domain, several annotation models are used depending on the characteristics of the task to solve (e.g., named entity recognition, relation extraction, etc.). However, few general-purpose annotation models exist, that can support a broad range of knowledge extraction tasks. This paper presents an annotation model designed to capture a large portion of the semantics of natural language text. The structure of the annotation model is presented, with examples of annotated sentences and a brief description of each semantic role and relation defined. This research focuses on an application to clinical texts in the Spanish language. Nevertheless, the presented annotation model is extensible to other domains and languages. An example of annotated sentences, guidelines, and suitable configuration files for an annotation tool are also provided for the research community.
Predicting ICU transfers using text messages between nurses and doctors
Authors: Faiza Khan Khattak, Chlo{\'e} Pou-Prom, Robert Wu, Frank Rudzicz
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

We explore the use of real-time clinical information, i.e., text messages sent between nurses and doctors regarding patient conditions in order to predict transfer to the intensive care unit(ICU). Preliminary results, in data from five hospitals, indicate that, despite being short and full of noise, text messages can augment other visit information to improve the performance of ICU transfer prediction.
Annotating and Characterizing Clinical Sentences with Explicit Why-QA Cues
Authors: Jungwei Fan
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Diversity, Question Answering

Many clinical information needs can be stated as why-questions. The answers to them represent important clinical reasoning and justification. Clinical notes are a rich source for such why-question answering (why-QA). However, there are few dedicated corpora, and little is known about the characteristics of clinical why-QA narratives. To address this gap, the study performed manual annotation of 277 sentences containing explicit why-QA cues and summarized their quantitative and qualitative properties. The contributions are: 1) sharing a seed corpus that can be used for various QA-related training purposes, 2) adding to our knowledge about the diversity and distribution of clinical why-QA contents.
Medical Word Embeddings for Spanish: Development and Evaluation
Authors: Felipe Soares, Marta Villegas, Aitor Gonzalez-Agirre, Martin Krallinger, Jordi Armengol-Estap{\'e}
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

Word embeddings are representations of words in a dense vector space. Although they are not recent phenomena in Natural Language Processing (NLP), they have gained momentum after the recent developments of neural methods and Word2Vec. Regarding their applications in medical and clinical NLP, they are invaluable resources when training in-domain named entity recognition systems, classifiers or taggers, for instance. Thus, the development of tailored word embeddings for medical NLP is of great interest. However, we identified a gap in the literature which we aim to fill in this paper: the availability of embeddings for medical NLP in Spanish, as well as a standardized form of intrinsic evaluation. Since most work has been done for English, some established datasets for intrinsic evaluation are already available. In this paper, we show the steps we employed to adapt such datasets for the first time to Spanish, of particular relevance due to the considerable volume of EHRs in this language, as well as the creation of in-domain medical word embeddings for the Spanish using the state-of-the-art FastText model. We performed intrinsic evaluation with our adapted datasets, as well as extrinsic evaluation with a named entity recognition systems using a baseline embedding of general-domain. Both experiments proved that our embeddings are suitable for use in medical NLP in the Spanish language, and are more accurate than general-domain ones.
Attention Neural Model for Temporal Relation Extraction
Authors: Sijia Liu, Li-Wei Wang, Vipin Chaudhary, Hongfang Liu
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: model, Relation, Relation Extraction, Sentence, Temporal Relation Extraction

Neural network models have shown promise in the temporal relation extraction task. In this paper, we present the attention based neural network model to extract the containment relations within sentences from clinical narratives. The attention mechanism used on top of GRU model outperforms the existing state-of-the-art neural network models on THYME corpus in intra-sentence temporal relation extraction.
Automatically Generating Psychiatric Case Notes From Digital Transcripts of Doctor-Patient Conversations
Authors: Nazmul Kazi, Kah, Indika a
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

Electronic health records (EHRs) are notorious for reducing the face-to-face time with patients while increasing the screen-time for clinicians leading to burnout. This is especially problematic for psychiatry care in which maintaining consistent eye-contact and non-verbal cues are just as important as the spoken words. In this ongoing work, we explore the feasibility of automatically generating psychiatric EHR case notes from digital transcripts of doctor-patient conversation using a two-step approach: (1) predicting semantic topics for segments of transcripts using supervised machine learning, and (2) generating formal text of those segments using natural language processing. Through a series of preliminary experimental results obtained through a collection of synthetic and real-life transcripts, we demonstrate the viability of this approach.
Clinical Data Classification using Conditional Random Fields and Neural Parsing for Morphologically Rich Languages
Authors: Razieh Ehsani, Tyko Niemi, Gaurav Khullar, Tiina Leivo
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: General Classification

Past prescriptions constitute a central element in patient records. These are often written in an unstructured and brief form. Extracting information from such prescriptions enables the development of automated processes in the medical data mining field. This paper presents a Conditional Random Fields (CRFs) based approach to extract relevant information from prescriptions. We focus on Finnish language prescriptions and make use of Finnish language specific features. Our labeling accuracy is 95{\%}, which compares favorably to the current state-of-the-art in English language prescriptions. This, to the best of our knowledge, is the first such work for the Finnish language.
How Well Do Embedding Models Capture Non-compositionality? A View from Multiword Expressions
Authors: N, Navnita akumar, Timothy Baldwin, Bahar Salehi
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

In this paper, we apply various embedding methods on multiword expressions to study how well they capture the nuances of non-compositional data. Our results from a pool of word-, character-, and document-level embbedings suggest that Word2vec performs the best, followed by FastText and Infersent. Moreover, we find that recently-proposed contextualised embedding models such as Bert and ELMo are not adept at handling non-compositionality in multiword expressions.
SWOW-8500: Word Association task for Intrinsic Evaluation of Word Embeddings
Authors: Avijit Thawani, Biplav Srivastava, Anil Singh
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: General Classification, Natural Language Inference, text-classification, Text Classification, Word Embeddings, Word Similarity

Downstream evaluation of pretrained word embeddings is expensive, more so for tasks where current state of the art models are very large architectures. Intrinsic evaluation using word similarity or analogy datasets, on the other hand, suffers from several disadvantages. We propose a novel intrinsic evaluation task employing large word association datasets (particularly the Small World of Words dataset). We observe correlations not just between performances on SWOW-8500 and previously proposed intrinsic tasks of word similarity prediction, but also with downstream tasks (eg. Text Classification and Natural Language Inference). Most importantly, we report better confidence intervals for scores on our word association task, with no fall in correlation with downstream performance.
Classification of Semantic Paraphasias: Optimization of a Word Embedding Model
Authors: Katy McKinney-Bock, Steven Bedrick
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Classification, General Classification, Semantic Similarity, Semantic Textual Similarity

In clinical assessment of people with aphasia, impairment in the ability to recall and produce words for objects (anomia) is assessed using a confrontation naming task, where a target stimulus is viewed and a corresponding label is spoken by the participant. Vector space word embedding models have had inital results in assessing semantic similarity of target-production pairs in order to automate scoring of this task; however, the resulting models are also highly dependent upon training parameters. To select an optimal family of models, we fit a beta regression model to the distribution of performance metrics on a set of 2,880 grid search models and evaluate the resultant first- and second-order effects to explore how parameterization affects model performance. Comparing to SimLex-999, we show that clinical data can be used in an evaluation task with comparable optimal parameter settings as standard NLP evaluation datasets.
CODAH: An Adversarially-Authored Question Answering Dataset for Common Sense
Authors: Michael Chen, Mike D{'}Arcy, Alisa Liu, Fern, Jared ez, Doug Downey
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Common Sense Reasoning, Question Answering, Sentence, Sentence Completion

Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on large pre-trained models of language, have already achieved near-human-level performance on commonsense knowledge benchmarks. These systems do not possess human-level common sense, but are able to exploit limitations of the datasets to achieve human-level scores. We introduce the CODAH dataset, an adversarially-constructed evaluation dataset for testing common sense. CODAH forms a challenging extension to the recently-proposed SWAG dataset, which tests commonsense knowledge using sentence-completion questions that describe situations observed in video. To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems. Workers are rewarded for submissions that models fail to answer correctly both before and after fine-tuning (in cross-validation). We create 2.8k questions via this procedure and evaluate the performance of multiple state-of-the-art question answering systems on our dataset. We observe a significant gap between human performance, which is 95.3{\%}, and the performance of the best baseline accuracy of 65.3{\%} by the OpenAI GPT model.
Dyr Bul Shchyl. Proxying Sound Symbolism With Word Embeddings
Authors: Ivan Yamshchikov, Viascheslav Shibaev, Alexey Tikhonov
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Word Embeddings

This paper explores modern word embeddings in the context of sound symbolism. Using basic properties of the representations space one can construct semantic axes. A method is proposed to measure if the presence of individual sounds in a given word shifts its semantics of that word along a specific axis. It is shown that, in accordance with several experimental and statistical results, word embeddings capture symbolism for certain sounds.
Identification, Interpretability, and Bayesian Word Embeddings
Authors: Adam Lauretig
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: regression, Word Embeddings

Social scientists have recently turned to analyzing text using tools from natural language processing like word embeddings to measure concepts like ideology, bias, and affinity. However, word embeddings are difficult to use in the regression framework familiar to social scientists: embeddings are are neither identified, nor directly interpretable. I offer two advances on standard embedding models to remedy these problems. First, I develop Bayesian Word Embeddings with Automatic Relevance Determination priors, relaxing the assumption that all embedding dimensions have equal weight. Second, I apply work identifying latent variable models to anchor embeddings, identifying them, and making them interpretable and usable in a regression. I then apply this model and anchoring approach to two cases, the shift in internationalist rhetoric in the American presidents{'} inaugural addresses, and the relationship between bellicosity in American foreign policy decision-makers{'} deliberations. I find that inaugural addresses became less internationalist after 1945, which goes against the conventional wisdom, and that an increase in bellicosity is associated with an increase in hostile actions by the United States, showing that elite deliberations are not cheap talk, and helping confirm the validity of the model.
Tweet Classification without the Tweet: An Empirical Examination of User versus Document Attributes
Authors: Veronica Lynn, Salvatore Giorgi, Niranjan Balasubramanian, H. Andrew Schwartz
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: General Classification, Stance Detection

NLP naturally puts a primary focus on leveraging document language, occasionally considering user attributes as supplemental. However, as we tackle more social scientific tasks, it is possible user attributes might be of primary importance and the document supplemental. Here, we systematically investigate the predictive power of user-level features alone versus document-level features for document-level tasks. We first show user attributes can sometimes carry more task-related information than the document itself. For example, a tweet-level stance detection model using only 13 user-level attributes (i.e. features that did not depend on the specific tweet) was able to obtain a higher F1 than the top-performing SemEval participant. We then consider multiple tasks and a wider range of user attributes, showing the performance of strong document-only models can often be improved (as in stance, sentiment, and sarcasm) with user attributes, particularly benefiting tasks with stable {``}trait-like{''} outcomes (e.g. stance) most relative to frequently changing {``}state-like{''} outcomes (e.g. sentiment). These results not only support the growing work on integrating user factors into predictive systems, but that some of our NLP tasks might be better cast primarily as user-level (or human) tasks.
Neural Network Prediction of Censorable Language
Authors: Kei Yin Ng, Anna Feldman, Jing Peng, Chris Leberknight
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Prediction

Internet censorship imposes restrictions on what information can be publicized or viewed on the Internet. According to Freedom House{'}s annual Freedom on the Net report, more than half the world{'}s Internet users now live in a place where the Internet is censored or restricted. China has built the world{'}s most extensive and sophisticated online censorship system. In this paper, we describe a new corpus of censored and uncensored social media tweets from a Chinese microblogging website, Sina Weibo, collected by tracking posts that mention {`}sensitive{'} topics or authored by {`}sensitive{'} users. We use this corpus to build a neural network classifier to predict censorship. Our model performs with a 88.50{\%} accuracy using only linguistic features. We discuss these features in detail and hypothesize that they could potentially be used for censorship circumvention.
Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate
Authors: Josephine Lukito, Prathusha K Sarma, Jordan Foley, Aman Abhishek
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Domain Adaptation, Time Series, Time Series Analysis, Word Embeddings

This paper proposes a method for identifying and studying viral moments or highlights during a political debate. Using a combined strategy of time series analysis and domain adapted word embeddings, this study provides an in-depth analysis of several key moments during the 2016 U.S. Presidential election. First, a time series outlier analysis is used to identify key moments during the debate. These moments had to result in a long-term shift in attention towards either Hillary Clinton or Donald Trump (i.e., a transient change outlier or an intervention, resulting in a permanent change in the time series). To assess whether these moments also resulted in a discursive shift, two corpora are produced for each potential viral moment (a pre-viral corpus and post-viral corpus). A domain adaptation layer learns weights to combine a generic and domain-specific (DS) word embedding into a domain adapted (DA) embedding. Words are then classified using a generic encoder+ classifier framework that relies on these word embeddings as inputs. Results suggest that both Clinton and Trump were able to induce discourse-shifting viral moments, though the former is much better at producing a topically-specific discursive shift.
Stance Classification, Outcome Prediction, and Impact Assessment: NLP Tasks for Studying Group Decision-Making
Authors: Elijah Mayfield, Alan Black
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6
Tasks: Decision Making, General Classification, Stance Classification, Word Embeddings

In group decision-making, the nuanced process of conflict and resolution that leads to consensus formation is closely tied to the quality of decisions made. Behavioral scientists rarely have rich access to process variables, though, as unstructured discussion transcripts are difficult to analyze. Here, we define ways for NLP researchers to contribute to the study of groups and teams. We introduce three tasks alongside a large new corpus of over 400,000 group debates on Wikipedia. We describe the tasks and their importance, then provide baselines showing that BERT contextualized word embeddings consistently outperform other language representations.
A Sociolinguistic Study of Online Echo Chambers on Twitter
Authors: Nikita Duseja, Harsh Jhamtani
Date: 2019-06-01 00:00:00
Proceeding: WS 2019 6

Online social media platforms such as Facebook and Twitter are increasingly facing criticism for polarization of users. One particular aspect which has caught the attention of various critics is presence of users in echo chambers - a situation wherein users are exposed mostly to the opinions which are in sync with their own views. In this paper, we perform a sociolinguistic study by comparing the tweets of users in echo chambers with the tweets of users not in echo chambers with similar levels of polarity on a broad topic. Specifically, we carry out a comparative analysis of tweet structure, lexical choices, and focus issues, and provide possible explanations for the results.