pwc-archive/papers-with-abstracts huggingface dataset:
Thumbs Up and Down: Sentiment Analysis of Medical Online Forums
Authors: Victoria Bobicev, Marina Sokolova
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: BIG-bench Machine Learning, General Classification, Sentence, Sentence Classification, Sentiment Analysis

In the current study, we apply multi-class and multi-label sentence classification to sentiment analysis of online medical forums. We aim to identify major health issues discussed in online social media and the types of sentiments those issues evoke. We use ontology of personal health information for Information Extraction and apply Machine Learning methods in automated recognition of the expressed sentiments.
Identification of Emergency Blood Donation Request on Twitter
Authors: Puneet Mathur, Meghna Ayyar, Sahil Chopra, Simra Shahid, Laiba Mehnaz, Rajiv Shah
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

Social media-based text mining in healthcare has received special attention in recent times due to the enhanced accessibility of social media sites like Twitter. The increasing trend of spreading important information in distress can help patients reach out to prospective blood donors in a time bound manner. However such manual efforts are mostly inefficient due to the limited network of a user. In a novel step to solve this problem, we present an annotated Emergency Blood Donation Request (EBDR) dataset to classify tweets referring to the necessity of urgent blood donation requirement. Additionally, we also present an automated feature-based SVM classification technique that can help selective EBDR tweets reach relevant personals as well as medical authorities. Our experiments also present a quantitative evidence that linguistic along with handcrafted heuristics can act as the most representative set of signals this task with an accuracy of 97.89{\%}.
Dealing with Medication Non-Adherence Expressions in Twitter
Authors: Takeshi Onishi, Davy Weissenbacher, Ari Klein, Karen O{'}Connor, Gonzalez-Hern, Graciela ez
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

Through a semi-automatic analysis of tweets, we show that Twitter users not only express Medication Non-Adherence (MNA) in social media but also their reasons for not complying; further research is necessary to fully extract automatically and analyze this information, in order to facilitate the use of this data in epidemiological studies.
Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention
Authors: Chuhan Wu, Fangzhao Wu, Junxin Liu, Sixing Wu, Yongfeng Huang, Xing Xie
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: HTR

This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions. In our system we propose a neural approach with hierarchical tweet representation and multi-head self-attention (HTR-MSA) for both tasks. Our system achieved the first place in both the first and third shared tasks of SMM4H with an F-score of 91.83{\%} and 52.20{\%} respectively.
Classification of Medication-Related Tweets Using Stacked Bidirectional LSTMs with Context-Aware Attention
Authors: Orest Xherija
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: General Classification, Text Classification, Word Embeddings

This paper describes the system that team UChicagoCompLx developed for the 2018 Social Media Mining for Health Applications (SMM4H) Shared Task. We use a variant of the Message-level Sentiment Analysis (MSA) model of (Baziotis et al., 2017), a word-level stacked bidirectional Long Short-Term Memory (LSTM) network equipped with attention, to classify medication-related tweets in the four subtasks of the SMM4H Shared Task. Without any subtask-specific tuning, the model is able to achieve competitive results across all subtasks. We make the datasets, model weights, and code publicly available.
Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection
Authors: Aditya Joshi, Xiang Dai, Sarvnaz Karimi, Ross Sparks, C{\'e}cile Paris, C Raina MacIntyre
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: General Classification, text-classification, Text Classification, Word Embeddings

Vaccination behaviour detection deals with predicting whether or not a person received/was about to receive a vaccine. We present our submission for vaccination behaviour detection shared task at the SMM4H workshop. Our findings are based on three prevalent text classification approaches: rule-based, statistical and deep learning-based. Our final submissions are: (1) an ensemble of statistical classifiers with task-specific features derived using lexicons, language processing tools and word embeddings; and, (2) a LSTM classifier with pre-trained language models.
IRISA at SMM4H 2018: Neural Network and Bagging for Tweet Classification
Authors: Anne-Lyse Minard, Christian Raymond, Vincent Claveau
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: General Classification, Word Embeddings

This paper describes the systems developed by IRISA to participate to the four tasks of the SMM4H 2018 challenge. For these tweet classification tasks, we adopt a common approach based on recurrent neural networks (BiLSTM). Our main contributions are the use of certain features, the use of Bagging in order to deal with unbalanced datasets, and on the automatic selection of difficult examples. These techniques allow us to reach 91.4, 46.5, 47.8, 85.0 as F1-scores for Tasks 1 to 4.
Drug-Use Identification from Tweets with Word and Character N-Grams
Authors: {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Taraka Rama
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Text Classification

This paper describes our systems in social media mining for health applications (SMM4H) shared task. We participated in all four tracks of the shared task using linear models with a combination of character and word n-gram features. We did not use any external data or domain specific information. The resulting systems achieved above-average scores among other participating systems, with F1-scores of 91.22, 46.8, 42.4, and 85.53 on tasks 1, 2, 3, and 4 respectively.
Automatic Identification of Drugs and Adverse Drug Reaction Related Tweets
Authors: Segun Taofeek Aroyehun, Alex Gelbukh, er
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Deep Learning

We describe our submissions to the Third Social Media Mining for Health Applications Shared Task. We participated in two tasks (tasks 1 and 3). For both tasks, we experimented with a traditional machine learning model (Naive Bayes Support Vector Machine (NBSVM)), deep learning models (Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM)), and the combination of deep learning model with SVM. We observed that the NBSVM reaches superior performance on both tasks on our development split of the training data sets. Official result for task 1 based on the blind evaluation data shows that the predictions of the NBSVM achieved our team{'}s best F-score of 0.910 which is above the average score received by all submissions to the task. On task 3, the combination of of BiLSTM and SVM gives our best F-score for the positive class of 0.394.
UZH@SMM4H: System Descriptions
Authors: Tilia Ellendorff, Joseph Cornelius, Heath Gordon, Nicola Colic, Fabio Rinaldi
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Document Classification, General Classification, Lemmatization, Part-Of-Speech Tagging, regression, text-classification, Text Classification

Our team at the University of Z{\"u}rich participated in the first 3 of the 4 sub-tasks at the Social Media Mining for Health Applications (SMM4H) shared task. We experimented with different approaches for text classification, namely traditional feature-based classifiers (Logistic Regression and Support Vector Machines), shallow neural networks, RCNNs, and CNNs. This system description paper provides details regarding the different system architectures and the achieved results.
Deep Learning for Social Media Health Text Classification
Authors: Santosh Tokala, Vaibhav Gambhir, Animesh Mukherjee
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Binary Classification, Classification, Deep Learning, General Classification, Speech Recognition, text-classification, Text Classification, Word Embeddings

This paper describes the systems developed for 1st and 2nd tasks of the 3rd Social Media Mining for Health Applications Shared Task at EMNLP 2018. The first task focuses on automatic detection of posts mentioning a drug name or dietary supplement, a binary classification. The second task is about distinguishing the tweets that present personal medication intake, possible medication intake and non-intake. We performed extensive experiments with various classifiers like Logistic Regression, Random Forest, SVMs, Gradient Boosted Decision Trees (GBDT) and deep learning architectures such as Long Short-Term Memory Networks (LSTM), jointed Convolutional Neural Networks (CNN) and LSTM architecture, and attention based LSTM architecture both at word and character level. We have also explored using various pre-trained embeddings like Global Vectors for Word Representation (GloVe), Word2Vec and task-specific embeddings learned using CNN-LSTM and LSTMs.
Using PPM for Health Related Text Detection
Authors: Victoria Bobicev, Victoria Lazu, Daniela Istrati
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10
Tasks: Language Modeling, Language Modelling, Text Detection

This paper describes the participation of the LILU team in SMM4H challenge on social media mining for health related events description such as drug intakes or vaccinations.
Leveraging Web Based Evidence Gathering for Drug Information Identification from Tweets
Authors: Rupsa Saha, Abir Naskar, Tirthankar Dasgupta, Lipika Dey
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

In this paper, we have explored web-based evidence gathering and different linguistic features to automatically extract drug names from tweets and further classify such tweets into Adverse Drug Events or not. We have evaluated our proposed models with the dataset as released by the SMM4H workshop shared Task-1 and Task-3 respectively. Our evaluation results shows that the proposed model achieved good results, with Precision, Recall and F-scores of 78.5{\%}, 88{\%} and 82.9{\%} respectively for Task1 and 33.2{\%}, 54.7{\%} and 41.3{\%} for Task3.
CLaC at SMM4H Task 1, 2, and 4
Authors: Parsa Bagherzadeh, Nadia Sheikh, Sabine Bergler
Date: 2018-10-01 00:00:00
Proceeding: WS 2018 10

CLaC Labs participated in Tasks 1, 2, and 4 using the same base architecture for all tasks with various parameter variations. This was our first exploration of this data and the SMM4H Tasks, thus a unified system was useful to compare the behavior of our architecture over the different datasets and how they interact with different linguistic features.
Assessing the Impact of Incremental Error Detection and Correction. A Case Study on the Italian Universal Dependency Treebank
Authors: Chiara Alzetta, Felice Dell{'}Orletta, Simonetta Montemagni, Maria Simi, Giulia Venturi
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Dependency Parsing

Detection and correction of errors and inconsistencies in {``}gold treebanks{''} are becoming more and more central topics of corpus annotation. The paper illustrates a new incremental method for enhancing treebanks, with particular emphasis on the extension of error patterns across different textual genres and registers. Impact and role of corrections have been assessed in a dependency parsing experiment carried out with four different parsers, whose results are promising. For both evaluation datasets, the performance of parsers increases, in terms of the standard LAS and UAS measures and of a more focused measure taking into account only relations involved in error patterns, and at the level of individual dependencies.
Using Universal Dependencies in cross-linguistic complexity research
Authors: Aleks Berdicevskis, rs, {\c{C}}a{\u{g}}r{\i} {\c{C}}{\"o}ltekin, Katharina Ehret, Kilu von Prince, Daniel Ross, Bill Thompson, Chunxiao Yan, Vera Demberg, Gary Lupyan, Taraka Rama, Christian Bentz
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11

We evaluate corpus-based measures of linguistic complexity obtained using Universal Dependencies (UD) treebanks. We propose a method of estimating robustness of the complexity values obtained using a given measure and a given treebank. The results indicate that measures of syntactic complexity might be on average less robust than those of morphological complexity. We also estimate the validity of complexity measures by comparing the results for very similar languages and checking for unexpected differences. We show that some of those differences that arise can be diminished by using parallel treebanks and, more importantly from the practical point of view, by harmonizing the language-specific solutions in the UD annotation.
Expletives in Universal Dependency Treebanks
Authors: Gosse Bouma, Jan Hajic, Dag Haug, Joakim Nivre, Per Erik Solberg, Lilja {\O}vrelid
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Coreference Resolution, Question Answering

Although treebanks annotated according to the guidelines of Universal Dependencies (UD) now exist for many languages, the goal of annotating the same phenomena in a cross-linguistically consistent fashion is not always met. In this paper, we investigate one phenomenon where we believe such consistency is lacking, namely expletive elements. Such elements occupy a position that is structurally associated with a core argument (or sometimes an oblique dependent), yet are non-referential and semantically void. Many UD treebanks identify at least some elements as expletive, but the range of phenomena differs between treebanks, even for closely related languages, and sometimes even for different treebanks for the same language. In this paper, we present criteria for identifying expletives that are applicable across languages and compatible with the goals of UD, give an overview of expletives as found in current UD treebanks, and present recommendations for the annotation of expletives so that more consistent annotation can be achieved in future releases.
Challenges in Converting the Index Thomisticus Treebank into Universal Dependencies
Authors: Flavio Massimiliano Cecchini, Marco Passarotti, Paola Marongiu, Daniel Zeman
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Dependency Parsing, POS, POS Tagging

This paper describes the changes applied to the original process used to convert the \textit{Index Thomisticus} Treebank, a corpus including texts in Medieval Latin by Thomas Aquinas, into the annotation style of Universal Dependencies. The changes are made both to harmonise the Universal Dependencies version of the \textit{Index Thomisticus} Treebank with the two other available Latin treebanks and to fix errors and inconsistencies resulting from the original process. The paper details the treatment of different issues in PoS tagging, lemmatisation and assignment of dependency relations. Finally, it assesses the quality of the new conversion process by providing an evaluation against a gold standard.
Er ... well, it matters, right? On the role of data representations in spoken language dependency parsing
Authors: Kaja Dobrovoljc, Matej Martinc
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Dependency Parsing, Language Modelling

Despite the significant improvement of data-driven dependency parsing systems in recent years, they still achieve a considerably lower performance in parsing spoken language data in comparison to written data. On the example of Spoken Slovenian Treebank, the first spoken data treebank using the UD annotation scheme, we investigate which speech-specific phenomena undermine parsing performance, through a series of training data and treebank modification experiments using two distinct state-of-the-art parsing systems. Our results show that utterance segmentation is the most prominent cause of low parsing performance, both in parsing raw and pre-segmented transcriptions. In addition to shorter utterances, both parsers perform better on normalized transcriptions including basic markers of prosody and excluding disfluencies, discourse markers and fillers. On the other hand, the effects of written training data addition and speech-specific dependency representations largely depend on the parsing system selected.
Mind the Gap: Data Enrichment in Dependency Parsing of Elliptical Constructions
Authors: Kira Droganova, Filip Ginter, Jenna Kanerva, Daniel Zeman
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Dependency Parsing

In this paper, we focus on parsing rare and non-trivial constructions, in particular ellipsis. We report on several experiments in enrichment of training data for this specific construction, evaluated on five languages: Czech, English, Finnish, Russian and Slovak. These data enrichment methods draw upon self-training and tri-training, combined with a stratified sampling method mimicking the structural complexity of the original treebank. In addition, using these same methods, we also demonstrate small improvements over the CoNLL-17 parsing shared task winning system for four of the five languages, not only restricted to the elliptical constructions.
Integration complexity and the order of cosisters
Authors: William Dyer
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11

The cost of integrating dependent constituents to their heads is thought to involve the distance between dependent and head and the complexity of the integration (Gibson, 1998). The former has been convincingly addressed by Dependency Distance Minimization (DDM) (cf. Liu et al., 2017). The current study addresses the latter by proposing a novel theory of integration complexity derived from the entropy of the probability distribution of a dependent{'}s heads. An analysis of Universal Dependency corpora provides empirical evidence regarding the preferred order of isomorphic cosisters{---}sister constituents of the same syntactic form on the same side of their head{---}such as the adjectives in \textit{pretty blue fish}. Integration complexity, alongside DDM, allows for a general theory of constituent order based on integration cost.
SUD or Surface-Syntactic Universal Dependencies: An annotation scheme near-isomorphic to UD
Authors: Kim Gerdes, Bruno Guillaume, Sylvain Kahane, Guy Perrier
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11

This article proposes a surface-syntactic annotation scheme called SUD that is near-isomorphic to the Universal Dependencies (UD) annotation scheme while following distributional criteria for defining the dependency tree structure and the naming of the syntactic functions. Rule-based graph transformation grammars allow for a bi-directional transformation of UD into SUD. The back-and-forth transformation can serve as an error-mining tool to assure the intra-language and inter-language coherence of the UD treebanks.
Coordinate Structures in Universal Dependencies for Head-final Languages
Authors: Hiroshi Kanayama, Na-Rae Han, Masayuki Asahara, Jena D. Hwang, Yusuke Miyao, Jinho D. Choi, Yuji Matsumoto
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11

This paper discusses the representation of coordinate structures in the Universal Dependencies framework for two head-final languages, Japanese and Korean. UD applies a strict principle that makes the head of coordination the left-most conjunct. However, the guideline may produce syntactic trees which are difficult to accept in head-final languages. This paper describes the status in the current Japanese and Korean corpora and proposes alternative designs suitable for these languages.
Investigating NP-Chunking with Universal Dependencies for English
Authors: Oph{\'e}lie Lacroix
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Chunking, Constituency Parsing, Dependency Parsing, Multi-Task Learning, Part-Of-Speech Tagging, POS, POS Tagging

Chunking is a pre-processing task generally dedicated to improving constituency parsing. In this paper, we want to show that universal dependency (UD) parsing can also leverage the information provided by the task of chunking even though annotated chunks are not provided with universal dependency trees. In particular, we introduce the possibility of deducing noun-phrase (NP) chunks from universal dependencies, focusing on English as a first example. We then demonstrate how the task of NP-chunking can benefit PoS-tagging in a multi-task learning setting {--} comparing two different strategies {--} and how it can be used as a feature for dependency parsing in order to learn enriched models.
Enhancing Universal Dependency Treebanks: A Case Study
Authors: Joakim Nivre, Paola Marongiu, Filip Ginter, Jenna Kanerva, Simonetta Montemagni, Sebastian Schuster, Maria Simi
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11

We evaluate two cross-lingual techniques for adding enhanced dependencies to existing treebanks in Universal Dependencies. We apply a rule-based system developed for English and a data-driven system trained on Finnish to Swedish and Italian. We find that both systems are accurate enough to bootstrap enhanced dependencies in existing UD treebanks. In the case of Italian, results are even on par with those of a prototype language-specific system.
Enhancing Universal Dependencies for Korean
Authors: Youngbin Noh, Jiyoon Han, Tae Hwan Oh, Hansaem Kim
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: POS, Relation

In this paper, for the purpose of enhancing Universal Dependencies for the Korean language, we propose a modified method for mapping Korean Part-of-Speech(POS) tagset in relation to Universal Part-of-Speech (UPOS) tagset in order to enhance the Universal Dependencies for the Korean Language. Previous studies suggest that UPOS reflects several issues that influence dependency annotation by using the POS of Korean predicates, particularly the distinctiveness in using verb, adjective, and copula.
UD-Japanese BCCWJ: Universal Dependencies Annotation for the Balanced Corpus of Contemporary Written Japanese
Authors: Mai Omura, Masayuki Asahara
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11

In this paper, we describe a corpus UD Japanese-BCCWJ that was created by converting the Balanced Corpus of Contemporary Written Japanese (BCCWJ), a Japanese language corpus, to adhere to the UD annotation schema. The BCCWJ already assigns dependency information at the level of the bunsetsu (a Japanese syntactic unit comparable to the phrase). We developed a program to convert the BCCWJ to UD based on this dependency structure, and this corpus is the result of completely automatic conversion using the program. UD Japanese-BCCWJ is the largest-scale UD Japanese corpus and the second-largest of all UD corpora, including 1,980 documents, 57,109 sentences, and 1,273k words across six distinct domains.
The Hebrew Universal Dependency Treebank: Past Present and Future
Authors: Shoval Sade, Amit Seker, Reut Tsarfaty
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Dependency Parsing

The Hebrew treebank (HTB), consisting of 6221 morpho-syntactically annotated newspaper sentences, has been the only resource for training and validating statistical parsers and taggers for Hebrew, for almost two decades now. During these decades, the HTB has gone through a trajectory of automatic and semi-automatic conversions, until arriving at its UDv2 form. In this work we manually validate the UDv2 version of the HTB, and, according to our findings, we apply scheme changes that bring the UD HTB to the same theoretical grounds as the rest of UD. Our experimental parsing results with UDv2New confirm that improving the coherence and internal consistency of the UD HTB indeed leads to improved parsing performance. At the same time, our analysis demonstrates that there is more to be done at the point of intersection of UD with other linguistic processing layers, in particular, at the points where UD interfaces external morphological and lexical resources.