pwc-archive/papers-with-abstracts huggingface dataset:
Detecting and Extracting of Adverse Drug Reaction Mentioning Tweets with Multi-Head Self Attention
Authors: Suyu Ge, Tao Qi, Chuhan Wu, Yongfeng Huang
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Language Modeling, Language Modelling, Task 2, Transfer Learning

This paper describes our system for the first and second shared tasks of the fourth Social Media Mining for Health Applications (SMM4H) workshop. We enhance tweet representation with a language model and distinguish the importance of different words with Multi-Head Self-Attention. In addition, transfer learning is exploited to make up for the data shortage. Our system achieved competitive results on both tasks with an F1-score of 0.5718 for task 1 and 0.653 (overlap) / 0.357 (strict) for task 2.
Deep Learning for Identification of Adverse Effect Mentions In Twitter Data
Authors: Paul Barry, Ozlem Uzuner
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Social Media Mining for Health Applications (SMM4H) Adverse Effect Mentions Shared Task challenges participants to accurately identify spans of text within a tweet that correspond to Adverse Effects (AEs) resulting from medication usage (Weissenbacher et al., 2019). This task features a training data set of 2,367 tweets, in addition to a 1,000 tweet evaluation data set. The solution presented here features a bidirectional Long Short-term Memory Network (bi-LSTM) for the generation of character-level embeddings. It uses a second bi-LSTM trained on both character and token level embeddings to feed a Conditional Random Field (CRF) which provides the final classification. This paper further discusses the deep learning algorithms used in our solution.
Using Machine Learning and Deep Learning Methods to Find Mentions of Adverse Drug Reactions in Social Media
Authors: Pilar L{\'o}pez {\'U}beda, Manuel Carlos D{\'\i}az Galiano, Maite Martin, L. Alfonso Urena Lopez
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: BIG-bench Machine Learning

Over time the use of social networks is becoming very popular platforms for sharing health related information. Social Media Mining for Health Applications (SMM4H) provides tasks such as those described in this document to help manage information in the health domain. This document shows the first participation of the SINAI group. We study approaches based on machine learning and deep learning to extract adverse drug reaction mentions from Twitter. The results obtained in the tasks are encouraging, we are close to the average of all participants and even above in some cases.
Towards Text Processing Pipelines to Identify Adverse Drug Events-related Tweets: University of Michigan @ SMM4H 2019 Task 1
Authors: V.G.Vinod Vydiswaran, Grace Ganzel, Bryan Romas, Deahan Yu, Amy Austin, Neha Bhomia, Socheatha Chan, Stephanie Hall, Van Le, Aaron Miller, Olawunmi Oduyebo, Aulia Song, Radhika Sondhi, Danny Teng, Hao Tseng, Kim Vuong, Stephanie Zimmerman
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: BIG-bench Machine Learning

We participated in Task 1 of the Social Media Mining for Health Applications (SMM4H) 2019 Shared Tasks on detecting mentions of adverse drug events (ADEs) in tweets. Our approach relied on a text processing pipeline for tweets, and training traditional machine learning and deep learning models. Our submitted runs performed above average for the task.
Neural Network to Identify Personal Health Experience Mention in Tweets Using BioBERT Embeddings
Authors: Shubham Gondane
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This paper describes the system developed by team ASU-NLP for the Social Media Mining for Health Applications(SMM4H) shared task 4. We extract feature embeddings from the BioBERT (Lee et al., 2019) model which has been fine-tuned on the training dataset and use that as inputs to a dense fully connected neural network. We achieve above average scores among the participant systems with the overall F1-score, accuracy, precision, recall as 0.8036, 0.8456, 0.9783, 0.6818 respectively.
Give It a Shot: Few-shot Learning to Normalize ADR Mentions in Social Media Posts
Authors: Emmanouil Manousogiannis, Sepideh Mesbah, Aless Bozzon, ro, Selene Baez, Robert Jan Sips
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Entity Linking, Few-Shot Learning, Text Normalization, Word Embeddings

This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes. For the first two steps, we reuse a state-of-the art approach, focusing our contribution on the final entity-linking step. For that we propose a simple Few-Shot learning approach, based on pre-trained word embeddings and data from the UMLS, combined with the provided training data. Our system (relaxed F1: 0.337-0.345) outperforms the average (relaxed F1 0.2972) of the participants in this task, demonstrating the potential feasibility of few-shot learning in the context of medical text normalization.
BIGODM System in the Social Media Mining for Health Applications Shared Task 2019
Authors: Chen-Kai Wang, Hong-Jie Dai, Bo-Hung Wang
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Negation

In this study, we describe our methods to automatically classify Twitter posts conveying events of adverse drug reaction (ADR). Based on our previous experience in tackling the ADR classification task, we empirically applied the vote-based under-sampling ensemble approach along with linear support vector machine (SVM) to develop our classifiers as part of our participation in ACL 2019 Social Media Mining for Health Applications (SMM4H) shared task 1. The best-performed model on the test sets were trained on a merged corpus consisting of the datasets released by SMM4H 2017 and 2019. By using VUE, the corpus was randomly under-sampled with 2:1 ratio between the negative and positive classes to create an ensemble using the linear kernel trained with features including bag-of-word, domain knowledge, negation and word embedding. The best performing model achieved an F-measure of 0.551 which is about 5{\%} higher than the average F-scores of 16 teams.
Detection of Adverse Drug Reaction Mentions in Tweets Using ELMo
Authors: Sarah Sarabadani
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Task 2

This paper describes the models used by our team in SMM4H 2019 shared task. We submitted results for subtasks 1 and 2. For task 1 which aims to detect tweets with Adverse Drug Reaction (ADR) mentions we used ELMo embeddings which is a deep contextualized word representation able to capture both syntactic and semantic characteristics. For task 2, which focuses on extraction of ADR mentions, first the same architecture as task 1 was used to identify whether or not a tweet contains ADR. Then, for tweets positively classified as mentioning ADR, the relevant text span was identified by similarity matching with 3 different lexicon sets.
Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4
Authors: Parsa Bagherzadeh, Nadia Sheikh, Sabine Bergler
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

CLaC labs participated in Task 1 and 4 of SMM4H 2019. We pursed two main objectives in our submission. First we tried to use some textual features in a deep net framework, and second, the potential use of more than one word embedding was tested. The results seem positively affected by the proposed architectures.
Detection of Adverse Drug Reaction in Tweets Using a Combination of Heterogeneous Word Embeddings
Authors: Segun Taofeek Aroyehun, Alex Gelbukh, er
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Word Embeddings

This paper details our approach to the task of detecting reportage of adverse drug reaction in tweets as part of the 2019 social media mining for healthcare applications shared task. We employed a combination of three types of word representations as input to a LSTM model. With this approach, we achieved an F1 score of 0.5209.
Identification of Adverse Drug Reaction Mentions in Tweets -- SMM4H Shared Task 2019
Authors: Samarth Rawal, Siddharth Rawal, Saadat Anwar, Chitta Baral
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Pharmacovigilance

Analyzing social media posts can offer insights into a wide range of topics that are commonly discussed online, providing valuable information for studying various health-related phenomena reported online. The outcome of this work can offer insights into pharmacovigilance research to monitor the adverse effects of medications. This research specifically looks into mentions of adverse drug reactions (ADRs) in Twitter data through the Social Media Mining for Health Applications (SMM4H) Shared Task 2019. Adverse drug reactions are undesired harmful effects which can arise from medication or other methods of treatment. The goal of this research is to build accurate models using natural language processing techniques to detect reports of adverse drug reactions in Twitter data and extract these words or phrases.
Cross-Linguistic Semantic Annotation: Reconciling the Language-Specific and the Universal
Authors: Jens E. L. Van Gysel, Meagan Vigus, Pavlina Kalm, Sook-kyung Lee, Michael Regan, William Croft
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Position

Developers of cross-linguistic semantic annotation schemes face a number of issues not encountered in monolingual annotation. This paper discusses four such issues, related to the establishment of annotation labels, and the treatment of languages with more fine-grained, more coarse-grained, and cross-cutting categories. We propose that a lattice-like architecture of the annotation categories can adequately handle all four issues, and at the same time remain both intuitive for annotators and faithful to typological insights. This position is supported by a brief annotation experiment.
Modeling Quantification and Scope in Abstract Meaning Representations
Authors: James Pustejovsky, Ken Lai, Nianwen Xue
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Negation

In this paper, we propose an extension to Abstract Meaning Representations (AMRs) to encode scope information of quantifiers and negation, in a way that overcomes the semantic gaps of the schema while maintaining its cognitive simplicity. Specifically, we address three phenomena not previously part of the AMR specification: quantification, negation (generally), and modality. The resulting representation, which we call {``}Uniform Meaning Representation{''} (UMR), adopts the predicative core of AMR and embeds it under a {``}scope{''} graph when appropriate. UMR representations differ from other treatments of quantification and modal scope phenomena in two ways: (a) they are more transparent; and (b) they specify default scope when possible.{`}
Parsing Meaning Representations: Is Easier Always Better?
Authors: Zi Lin, Nianwen Xue
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abstract Meaning Representation

The parsing accuracy varies a great deal for different meaning representations. In this paper, we compare the parsing performances between Abstract Meaning Representation (AMR) and Minimal Recursion Semantics (MRS), and provide an in-depth analysis of what factors contributed to the discrepancy in their parsing accuracy. By crystalizing the trade-off between representation expressiveness and ease of automatic parsing, we hope our results can help inform the design of the next-generation meaning representations.
GKR: Bridging the Gap between Symbolic/structural and Distributional Meaning Representations
Authors: Aikaterini-Lida Kalouli, Richard Crouch, Valeria de Paiva
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Three broad approaches have been attempted to combine distributional and structural/symbolic aspects to construct meaning representations: a) injecting linguistic features into distributional representations, b) injecting distributional features into symbolic representations or c) combining structural and distributional features in the final representation. This work focuses on an example of the third and less studied approach: it extends the Graphical Knowledge Representation (GKR) to include distributional features and proposes a division of semantic labour between the distributional and structural/symbolic features. We propose two extensions of GKR that clearly show this division and empirically test one of the proposals on an NLI dataset with hard compositional pairs.
Generating Discourse Inferences from Unscoped Episodic Logical Formulas
Authors: Gene Kim, Benjamin Kane, Viet Duong, Muskaan Mendiratta, Graeme McGuire, Sophie Sackstein, Georgiy Platonov, Lenhart Schubert
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Abstract Unscoped episodic logical form (ULF) is a semantic representation capturing the predicate-argument structure of English within the episodic logic formalism in relation to the syntactic structure, while leaving scope, word sense, and anaphora unresolved. We describe how ULF can be used to generate natural language inferences that are grounded in the semantic and syntactic structure through a small set of rules defined over interpretable predicates and transformations on ULFs. The semantic restrictions placed by ULF semantic types enables us to ensure that the inferred structures are semantically coherent while the nearness to syntax enables accurate mapping to English. We demonstrate these inferences on four classes of conversationally-oriented inferences in a mixed genre dataset with 68.5{\%} precision from human judgments.
A Plea for Information Structure as a Part of Meaning Representation
Authors: Eva Hajicova
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Negation

The view that the representation of information structure (IS) should be a part of (any type of) representation of meaning is based on the fact that IS is a semantically relevant phenomenon. In the contribution, three arguments supporting this view are briefly summarized, namely, the relation of IS to the interpretation of negation and presupposition, the relevance of IS to the understanding of discourse connectivity and for the establishment and interpretation of coreference relations. Afterwards, possible integration of the description of the main ingredient of IS into a meaning representation is illustrated.
TCL - a Lexicon of Turkish Discourse Connectives
Authors: Deniz Zeyrek, Kezban Ba{\c{s}}{\i}b{\"u}y{\"u}k
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

It is known that discourse connectives are the most salient indicators of discourse relations. State-of-the-art parsers being developed to predict explicit discourse connectives exploit annotated discourse corpora but a lexicon of discourse connectives is also needed to enable further research in discourse structure and support the development of language technologies that use these structures for text understanding. This paper presents a lexicon of Turkish discourse connectives built by automatic means. The lexicon has the format of the German connective lexicon, DiMLex, where for each discourse connective, information about the connective{`}s orthographic variants, syntactic category and senses are provided along with sample relations. In this paper, we describe the data sources we used and the development steps of the lexicon.
Meta-Semantic Representation for Early Detection of Alzheimer's Disease
Authors: Jinho D. Choi, Mengmei Li, Felicia Goldstein, Ihab Hajjar
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Knowledge Graphs

This paper presents a new task-oriented meaning representation called meta-semantics, that is designed to detect patients with early symptoms of Alzheimer{'}s disease by analyzing their language beyond a syntactic or semantic level. Meta-semantic representation consists of three parts, entities, predicate argument structures, and discourse attributes, that derive rich knowledge graphs. For this study, 50 controls and 50 patients with mild cognitive impairment (MCI) are selected, and meta-semantic representation is annotated on their speeches transcribed in text. Inter-annotator agreement scores of 88{\%}, 82{\%}, and 89{\%} are achieved for the three types of annotation, respectively. Five analyses are made using this annotation, depicting clear distinctions between the control and MCI groups. Finally, a neural model is trained on features extracted from those analyses to classify MCI patients from normal controls, showing a high accuracy of 82{\%} that is very promising.
Ellipsis in Chinese AMR Corpus
Authors: Yihuan Liu, Bin Li, Peiyi Yan, Li Song, Weiguang Qu
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abstract Meaning Representation, Sentence

Ellipsis is very common in language. It{'}s necessary for natural language processing to restore the elided elements in a sentence. However, there{'}s only a few corpora annotating the ellipsis, which draws back the automatic detection and recovery of the ellipsis. This paper introduces the annotation of ellipsis in Chinese sentences, using a novel graph-based representation Abstract Meaning Representation (AMR), which has a good mechanism to restore the elided elements manually. We annotate 5,000 sentences selected from Chinese TreeBank (CTB). We find that 54.98{\%} of sentences have ellipses. 92{\%} of the ellipses are restored by copying the antecedents{'} concepts. and 12.9{\%} of them are the new added concepts. In addition, we find that the elided element is a word or phrase in most cases, but sometimes only the head of a phrase or parts of a phrase, which is rather hard for the automatic recovery of ellipsis.
Event Structure Representation: Between Verbs and Argument Structure Constructions
Authors: Pavlina Kalm, Michael Regan, William Croft
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This paper proposes a novel representation of event structure by separating verbal semantics and the meaning of argument structure constructions that verbs occur in. Our model demonstrates how the two meaning representations interact. Our model thus effectively deals with various verb construals in different argument structure constructions, unlike purely verb-based approaches. However, unlike many constructionally-based approaches, we also provide a richer representation of the event structure evoked by the verb meaning.
Distributional Semantics Meets Construction Grammar. towards a Unified Usage-Based Model of Grammar and Meaning
Authors: Giulia Rambelli, Emmanuele Chersoni, Philippe Blache, Chu-Ren Huang, Aless Lenci, ro
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

In this paper, we propose a new type of semantic representation of Construction Grammar that combines constructions with the vector representations used in Distributional Semantics. We introduce a new framework, Distributional Construction Grammar, where grammar and meaning are systematically modeled from language use, and finally, we discuss the kind of contributions that distributional models can provide to CxG representation from a linguistic and cognitive perspective.
Meaning Representation of Null Instantiated Semantic Roles in FrameNet
Authors: Miriam R. L. Petruck
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Natural Language Understanding

Humans have the unique ability to infer information about participants in a scene, even if they are not mentioned in a text about that scene. Computer systems cannot do so without explicit information about those participants. This paper addresses the linguistic phenomenon of null-instantiated frame elements, i.e., implicit semantic roles, and their representation in FrameNet (FN). It motivates FN{'}s annotation practice, and illustrates three types of null-instantiated arguments that FrameNet tracks, noting that other lexical resources do not record such semantic-pragmatic information, despite its need in natural language understanding (NLU), and the elaborate efforts to create new datasets. It challenges the community to appeal to FN data to develop more sophisticated techniques for recognizing implicit semantic roles, and creating needed datasets. Although the annotation of null-instantiated roles was lexicographically motivated, FN provides useful information for text processing, and therefore must be considered in the design of any meaning representation for natural language understanding.
Copula and Case-Stacking Annotations for Korean AMR
Authors: Hyonsu Choe, Jiyoon Han, Hyejin Park, Hansaem Kim
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abstract Meaning Representation, Negation

This paper concerns the application of Abstract Meaning Representation (AMR) to Korean. In this regard, it focuses on the copula construction and its negation and the case-stacking phenomenon thereof. To illustrate this clearly, we reviewed the :domain annotation scheme from various perspectives. In this process, the existing annotation guidelines were improved to devise annotation schemes for each issue under the principle of pursuing consistency and efficiency of annotation without distorting the characteristics of Korean.
ClearTAC: Verb Tense, Aspect, and Form Classification Using Neural Nets
Authors: Skatje Myers, Martha Palmer
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Form, General Classification

This paper proposes using a Bidirectional LSTM-CRF model in order to identify the tense and aspect of verbs. The information that this classifier outputs can be useful for ordering events and can provide a pre-processing step to improve efficiency of annotating this type of information. This neural network architecture has been successfully employed for other sequential labeling tasks, and we show that it significantly outperforms the rule-based tool TMV-annotator on the Propbank I dataset.
Preparing SNACS for Subjects and Objects
Authors: Adi Shalev, Jena D. Hwang, Nathan Schneider, Vivek Srikumar, Omri Abend, Ari Rappoport
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens. Importantly, that work has argued for a principled separation of the semantic role in a scene from the function coded by morphosyntax. Here, we ask whether this approach can be generalized beyond adpositions and possessives to cover all scene participants{---}including subjects and objects{---}directly, without reference to a frame lexicon. We present new guidelines for English and the results of an interannotator agreement study.
A Case Study on Meaning Representation for Vietnamese
Authors: Ha Linh, Huyen Nguyen
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abstract Meaning Representation

This paper presents a case study on meaning representation for Vietnamese. Having introduced several existing semantic representation schemes for different languages, we select as basis for our work on Vietnamese AMR (Abstract Meaning Representation). From it, we define a meaning representation label set by adapting the English schema and taking into account the specific characteristics of Vietnamese.
VerbNet Representations: Subevent Semantics for Transfer Verbs
Authors: Susan Windisch Brown, Julia Bonn, James Gung, Annie Zaenen, James Pustejovsky, Martha Palmer
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

This paper announces the release of a new version of the English lexical resource VerbNet with substantially revised semantic representations designed to facilitate computer planning and reasoning based on human language. We use the transfer of possession and transfer of information event representations to illustrate both the general framework of the representations and the types of nuances the new representations can capture. These representations use a Generative Lexicon-inspired subevent structure to track attributes of event participants across time, highlighting oppositions and temporal and causal relations among the subevents.