pwc-archive/papers-with-abstracts huggingface dataset:
Can spontaneous spoken language disfluencies help describe syntactic dependencies? An empirical study
Authors: M. Zakaria Kurdi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Speech Recognition, Text Generation

This paper explores the correlations between key syntactic dependencies and the occurrence of simple spoken language disfluencies such as filled pauses and incomplete words. The working hypothesis here is that interruptions caused by these phenomena are more likely to happen between weakly connected words from a syntactic point of view than between strongly connected ones. The obtained results show significant patterns with the regard to key syntactic phenomena, like confirming the positive correlation between the frequency of disfluencies and multiples measures of syntactic complexity. In addition, they show that there is a stronger relationship between the verb and its subject than with its object, which confirms the idea of a hierarchical incrementality. Also, this work uncovered an interesting role played by a verb particle as a syntactic delimiter of some verb complements. Finally, the interruptions by disfluencies patterns show that verbs have a more privileged relationship with their preposition compared to the object Noun Phrase (NP).
Word-word Relations in Dementia and Typical Aging
Authors: Natalia Arias-Trejo, Aline Minto-Garc{\'\i}a, Diana I. Luna-Umanzor, Alma E. R{\'\i}os-Ponce, Balderas-Pliego Mariana, Gemma Bel-Enguix
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Retrieval

Older adults tend to suffer a decline in some of their cognitive capabilities, being language one of least affected processes. Word association norms (WAN) also known as free word associations reflect word-word relations, the participant reads or hears a word and is asked to write or say the first word that comes to mind. Free word associations show how the organization of semantic memory remains almost unchanged with age. We have performed a WAN task with very small samples of older adults with Alzheimer{'}s disease (AD), vascular dementia (VaD) and mixed dementia (MxD), and also with a control group of typical aging adults, matched by age, sex and education. All of them are native speakers of Mexican Spanish. The results show, as expected, that Alzheimer disease has a very important impact in lexical retrieval, unlike vascular and mixed dementia. This suggests that linguistic tests elaborated from WAN can be also used for detecting AD at early stages.
Part-of-Speech Annotation of English-Assamese code-mixed texts: Two Approaches
Authors: Ritesh Kumar, Manas Jyoti Bora
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

In this paper, we discuss the development of a part-of-speech tagger for English-Assamese code-mixed texts. We provide a comparison of 2 approaches to annotating code-mixed data {--} a) annotation of the texts from the two languages using monolingual resources from each language and b) annotation of the text through a different resource created specifically for code-mixed data. We present a comparative study of the efforts required in each approach and the final performance of the system. Based on this, we argue that it might be a better approach to develop new technologies using code-mixed data instead of monolingual, {`}clean{'} data, especially for those languages where we do not have significant tools and technologies available till now.
The effect of information controls on developers in China: An analysis of censorship in Chinese open source projects
Authors: Jeffrey Knockel, Masashi Crete-Nishihata, Lotus Ruan
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Censorship of Internet content in China is understood to operate through a system of intermediary liability whereby service providers are liable for the content on their platforms. Previous work studying censorship has found huge variability in the implementation of censorship across different products even within the same industry segment. In this work we explore the extent to which these censorship features are present in the open source projects of individual developers in China by collecting their blacklists and comparing their similarity. We collect files from a popular online code repository, extract lists of strings, and then classify whether each is a Chinese blacklist. Overall, we found over 1,000 Chinese blacklists comprising over 200,000 unique keywords, representing the largest dataset of Chinese blacklisted keywords to date. We found very little keyword overlap between lists, raising questions as to their origins, as the lists seem too large to have been individually curated, yet the lack of overlap suggests that they have no common source.
Creative Language Encoding under Censorship
Authors: Heng Ji, Kevin Knight
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Position

People often create obfuscated language for online communication to avoid Internet censorship, share sensitive information, express strong sentiment or emotion, plan for secret actions, trade illegal products, or simply hold interesting conversations. In this position paper we systematically categorize human-created obfuscated language on various levels, investigate their basic mechanisms, give an overview on automated techniques needed to simulate human encoding. These encoders have potential to frustrate and evade, co-evolve with dynamic human or automated decoders, and produce interesting and adoptable code words. We also summarize remaining challenges for future research on the interaction between Natural Language Processing (NLP) and encryption, and leveraging NLP techniques for encoding and decoding.
Every Object Tells a Story
Authors: James Pustejovsky, Nikhil Krishnaswamy
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Object

Most work within the computational event modeling community has tended to focus on the interpretation and ordering of events that are associated with verbs and event nominals in linguistic expressions. What is often overlooked in the construction of a global interpretation of a narrative is the role contributed by the objects participating in these structures, and the latent events and activities conventionally associated with them. Recently, the analysis of visual images has also enriched the scope of how events can be identified, by anchoring both linguistic expressions and ontological labels to segments, subregions, and properties of images. By semantically grounding event descriptions in their visualization, the importance of object-based attributes becomes more apparent. In this position paper, we look at the narrative structure of objects: that is, how objects reference events through their intrinsic attributes, such as affordances, purposes, and functions. We argue that, not only do objects encode conventionalized events, but that when they are composed within specific habitats, the ensemble can be viewed as modeling coherent event sequences, thereby enriching the global interpretation of the evolving narrative being constructed.
A Rich Annotation Scheme for Mental Events
Authors: William Croft, Pavl{\'\i}na Pe{\v{s}}kov{\'a}, Michael Regan, Sook-kyung Lee
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

We present a rich annotation scheme for the structure of mental events. Mental events are those in which the verb describes a mental state or process, usually oriented towards an external situation. While physical events have been described in detail and there are numerous studies of their semantic analysis and annotation, mental events are less thoroughly studied. The annotation scheme proposed here is based on decompositional analyses in the semantic and typological linguistic literature. The scheme was applied to the news corpus from the 2016 Events workshop, and error analysis of the test annotation provides suggestions for refinement and clarification of the annotation scheme.
Cross-Document Narrative Alignment of Environmental News: A Position Paper on the Challenge of Using Event Chains to Proxy Narrative Features
Authors: Ben Miller
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, Event Segmentation, Position, Segmentation, Word Embeddings

Cross-document event chain co-referencing in corpora of news articles would achieve increased precision and generalizability from a method that consistently recognizes narrative, discursive, and phenomenological features such as tense, mood, tone, canonicity and breach, person, hermeneutic composability, speed, and time. Current models that capture primarily linguistic data such as entities, times, and relations or causal relationships may only incidentally capture narrative framing features of events. That limits efforts at narrative and event chain segmentation, among other predicate tasks for narrative search and narrative-based reasoning. It further limits research on audience engagement with journalism about complex subjects. This position paper explores the above proposition with respect to narrative theory and ongoing research on segmenting event chains into narrative units. Our own work in progress approaches this task using event segmentation, word embeddings, and variable length pattern matching in a corpus of 2,000 articles describing environmental events. Our position is that narrative features may or may not be implicitly captured by current methods explicitly focused on events as linguistic phenomena, that they are not explicitly captured, and that further research is required.
Identifying the Discourse Function of News Article Paragraphs
Authors: W. Victor Yarlott, Cristina Cornelio, Tian Gao, Mark Finlayson
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, BIG-bench Machine Learning

Discourse structure is a key aspect of all forms of text, providing valuable information both to humans and machines. We applied the hierarchical theory of news discourse developed by van Dijk to examine how paragraphs operate as units of discourse structure within news articles{---}what we refer to here as document-level discourse. This document-level discourse provides a characterization of the content of each paragraph that describes its relation to the events presented in the article (such as main events, backgrounds, and consequences) as well as to other components of the story (such as commentary and evaluation). The purpose of a news discourse section is of great utility to story understanding as it affects both the importance and temporal order of items introduced in the text{---}therefore, if we know the news discourse purpose for different sections, we should be able to better rank events for their importance and better construct timelines. We test two hypotheses: first, that people can reliably annotate news articles with van Dijk{'}s theory; second, that we can reliably predict these labels using machine learning. We show that people have a high degree of agreement with each other when annotating the theory (F1 {\textgreater} 0.8, Cohen{'}s kappa {\textgreater} 0.6), demonstrating that it can be both learned and reliably applied by human annotators. Additionally, we demonstrate first steps toward machine learning of the theory, achieving a performance of F1 = 0.54, which is 65{\%} of human performance. Moreover, we have generated a gold-standard, adjudicated corpus of 50 documents for document-level discourse annotation based on the ACE Phase 2 corpus.
An Evaluation of Information Extraction Tools for Identifying Health Claims in News Headlines
Authors: Shi Yuan, Bei Yu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Relation, Relation Extraction, Triplet

This study evaluates the performance of four information extraction tools (extractors) on identifying health claims in health news headlines. A health claim is defined as a triplet: IV (what is being manipulated), DV (what is being measured) and their relation. Tools that can identify health claims provide the foundation for evaluating the accuracy of these claims against authoritative resources. The evaluation result shows that 26{\%} headlines do not in-clude health claims, and all extractors face difficulty separating them from the rest. For those with health claims, OPENIE-5.0 performed the best with F-measure at 0.6 level for ex-tracting {``}IV-relation-DV{''}. However, the characteristic linguistic structures in health news headlines, such as incomplete sentences and non-verb relations, pose particular challenge to existing tools.
Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation
Authors: Tommaso Caselli, Oana Inel
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, Relation, valid

This paper describes a crowdsourcing experiment on the annotation of plot-like structures in English news articles. CrowdThruth methodology and metrics have been applied to select valid annotations from the crowd. We further run an in-depth analysis of the annotated data by comparing them with available expert data. Our results show a valuable use of crowdsourcing annotations for such complex semantic tasks, and suggest a new annotation approach which combine crowd and experts.
Can You Spot the Semantic Predicate in this Video?
Authors: Christopher Reale, Claire Bonial, Heesung Kwon, Clare Voss
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Activity Recognition, Human Activity Recognition, Multi-Task Learning

We propose a method to improve human activity recognition in video by leveraging semantic information about the target activities from an expert-defined linguistic resource, VerbNet. Our hypothesis is that activities that share similar event semantics, as defined by the semantic predicates of VerbNet, will be more likely to share some visual components. We use a deep convolutional neural network approach as a baseline and incorporate linguistic information from VerbNet through multi-task learning. We present results of experiments showing the added information has negligible impact on recognition performance. We discuss how this may be because the lexical semantic information defined by VerbNet is generally not visually salient given the video processing approach used here, and how we may handle this in future approaches.
Fine-grained Structure-based News Genre Categorization
Authors: Zeyu Dai, Himanshu Taneja, Ruihong Huang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, General Classification, Question Answering

Journalists usually organize and present the contents of a news article following a well-defined structure. In this work, we propose a new task to categorize news articles based on their content presentation structures, which is beneficial for various NLP applications. We first define a small set of news elements considering their functions (e.g., \textit{introducing the main story or event, catching the reader{'}s attention} and \textit{providing details}) in a news story and their writing style (\textit{narrative} or \textit{expository}), and then formally define four commonly used news article structures based on their selections and organizations of news elements. We create an annotated dataset for structure-based news genre identification, and finally, we build a predictive model to assess the feasibility of this classification task using structure indicative features.
On Training Classifiers for Linking Event Templates
Authors: Jakub Piskorski, Fredi {\v{S}}ari{\'c}, Vanni Zavarella, Martin Atkinson
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: BIG-bench Machine Learning

The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7{\%} (92.9{\%}) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6{\%} (GOLD) and 97{\%} (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events.
HEI: Hunter Events Interface A platform based on services for the detection and reasoning about events
Authors: Antonio Sorgente, Antonio Calabrese, Gianluca Coda, Paolo Vanacore, Francesco Mele
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

In this paper we present the definition and implementation of the Hunter Events Interface (HEI) System. The HEI System is a system for events annotation and temporal reasoning in Natural Language Texts and media, mainly oriented to texts of historical and cultural contents available on the Web. In this work we assume that events are defined through various components: actions, participants, locations, and occurrence intervals. The HEI system, through independent services, locates (annotates) the various components, and successively associates them to a specific event. The objective of this work is to build a system integrating services for the identification of events, the discovery of their connections, and the evaluation of their consistency. We believe this interface is useful to develop applications that use the notion of story, to integrate data of digital cultural archives, and to build systems of fruition in the same field. The HEI system has been partially developed within the TrasTest project
Benchmarking Aggression Identification in Social Media
Authors: Ritesh Kumar, Atul Kr. Ojha, Shervin Malmasi, Marcos Zampieri
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification, Benchmarking

In this paper, we present the report and findings of the Shared Task on Aggression Identification organised as part of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC - 1) at COLING 2018. The task was to develop a classifier that could discriminate between Overtly Aggressive, Covertly Aggressive, and Non-aggressive texts. For this task, the participants were provided with a dataset of 15,000 aggression-annotated Facebook Posts and Comments each in Hindi (in both Roman and Devanagari script) and English for training and validation. For testing, two different sets - one from Facebook and another from a different social media - were provided. A total of 130 teams registered to participate in the task, 30 teams submitted their test runs, and finally 20 teams also sent their system description paper which are included in the TRAC workshop proceedings. The best system obtained a weighted F-score of 0.64 for both Hindi and English on the Facebook test sets, while the best scores on the surprise set were 0.60 and 0.50 for English and Hindi respectively. The results presented in this report depict how challenging the task is. The positive response from the community and the great levels of participation in the first edition of this shared task also highlights the interest in this topic.
IRIT at TRAC 2018
Authors: Rami, Faneva risoa, Josiane Mothe
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification

This paper describes the participation of the IRIT team to the TRAC 2018 shared task on Aggression Identification and more precisely to the shared task in English language. The three following methods have been used: a) a combination of machine learning techniques that relies on a set of features and document/text vectorization, b) Convolutional Neural Network (CNN) and c) a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). Best results were obtained when using the method (a) on the English test data from Facebook which ranked our method sixteenth out of thirty teams, and the method (c) on the English test data from other social media, where we obtained the fifteenth rank out of thirty.
Fully Connected Neural Network with Advance Preprocessor to Identify Aggression over Facebook and Twitter
Authors: Kashyap Raiyani, Teresa Gon{\c{c}}alves, Paulo Quaresma, Vitor Beires Nogueira
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification, Hate Speech Detection, Language Identification, Text Classification

Paper presents the different methodologies developed {\&} tested and discusses their results, with the goal of identifying the best possible method for the aggression identification problem in social media.
Cyberbullying Intervention Based on Convolutional Neural Networks
Authors: Qianjia Huang, Diana Inkpen, Jianhong Zhang, David Van Bruwaene
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: BIG-bench Machine Learning, General Classification, text-classification, Text Classification

This paper describes the process of building a cyberbullying intervention interface driven by a machine-learning based text-classification service. We make two main contributions. First, we show that cyberbullying can be identified in real-time before it takes place, with available machine learning and natural language processing tools. Second, we present a mechanism that provides individuals with early feedback about how other people would feel about wording choices in their messages before they are sent out. This interface not only gives a chance for the user to revise the text, but also provides a system-level flagging/intervention in a situation related to cyberbullying.
TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING'18
Authors: Ritesh Kumar, Guggilla Bhanodai, Rajendra Pamula, Maheshwar Reddy Chennuru
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification, Transfer Learning, Word Embeddings

This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards TRAC-1 Shared Task on Aggression Identification in Social Media for COLING 2018. In this paper we label aggression identification into three categories: Overtly Aggressive, Covertly Aggressive and Non-aggressive. We train a model to differentiate between these categories and then analyze the results in order to better understand how we can distinguish between them. We participated in two different tasks named as English (Facebook) task and English (Social Media) task. For English (Facebook) task System 05 was our best run (i.e. 0.3572) above the Random Baseline (i.e. 0.3535). For English (Social Media) task our system 02 got the value (i.e. 0.1960) below the Random Bseline (i.e. 0.3477). For all of our runs we used Long Short-Term Memory model. Overall, our performance is not satisfactory. However, as new entrant to the field, our scores are encouraging enough to work for better results in future.
An Ensemble Approach for Aggression Identification in English and Hindi Text
Authors: Arjun Roy, Prashant Kapil, Kingshuk Basak, Asif Ekbal
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification

This paper describes our system submitted in the shared task at COLING 2018 TRAC-1: Aggression Identification. The objective of this task was to predict online aggression spread through online textual post or comment. The dataset was released in two languages, English and Hindi. We submitted a single system for Hindi and a single system for English. Both the systems are based on an ensemble architecture where the individual models are based on Convoluted Neural Network and Support Vector Machine. Evaluation shows promising results for both the languages.The total submission for English was 30 and Hindi was 15. Our system on English facebook and social media obtained F1 score of 0.5151 and 0.5099 respectively where Hindi facebook and social media obtained F1 score of 0.5599 and 0.3790 respectively.