pwc-archive/papers-with-abstracts huggingface dataset:
Aggression Identification and Multi Lingual Word Embeddings
Authors: Thiago Galery, Efstathios Charitos, Ye Tian
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification, Multi-Label Text Classification, Text Classification, Word Embeddings

The system presented here took part in the 2018 Trolling, Aggression and Cyberbullying shared task (Forest and Trees team) and uses a Gated Recurrent Neural Network architecture (Cho et al., 2014) in an attempt to assess whether combining pre-trained English and Hindi fastText (Mikolov et al., 2018) word embeddings as a representation of the sequence input would improve classification performance. The motivation for this comes from the fact that the shared task data for English contained many Hindi tokens and therefore some users might be doing code-switching: the alternation between two or more languages in communication. To test this hypothesis, we also aligned Hindi and English vectors using pre-computed SVD matrices that pulls representations from different languages into a common space (Smith et al., 2017). Two conditions were tested: (i) one with standard pre-trained fastText word embeddings where each Hindi word is treated as an OOV token, and (ii) another where word embeddings for Hindi and English are loaded in a common vector space, so Hindi tokens can be assigned a meaningful representation. We submitted the second (i.e., multilingual) system and obtained the scores of 0.531 weighted F1 for the EN-FB dataset and 0.438 weighted F1 for the EN-TW dataset.
A K-Competitive Autoencoder for Aggression Detection in Social Media Text
Authors: Promita Maitra, Ritesh Sarkhel
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

We present an approach to detect aggression from social media text in this work. A winner-takes-all autoencoder, called Emoti-KATE is proposed for this purpose. Using a log-normalized, weighted word-count vector at input dimensions, the autoencoder simulates a competition between neurons in the hidden layer to minimize the reconstruction loss between the input and final output layers. We have evaluated the performance of our system on the datasets provided by the organizers of TRAC workshop, 2018. Using the encoding generated by Emoti-KATE, a 3-way classification is performed for every social media text in the dataset. Each data point is classified as {`}Overtly Aggressive{'}, {`}Covertly Aggressive{'} or {`}Non-aggressive{'}. Results show that our (team name: PMRS) proposed method is able to achieve promising results on some of these datasets. In this paper, we have described the effects of introducing an winner-takes-all autoencoder for the task of aggression detection, reported its performance on four different datasets, analyzed some of its limitations and how to improve its performance in future works.
Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling
Authors: Segun Taofeek Aroyehun, Alex Gelbukh, er
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Data Augmentation, Feature Engineering, Hate Speech Detection

With the advent of the read-write web which facilitates social interactions in online spaces, the rise of anti-social behaviour in online spaces has attracted the attention of researchers. In this paper, we address the challenge of automatically identifying aggression in social media posts. Our team, saroyehun, participated in the English track of the Aggression Detection in Social Media Shared Task. On this task, we investigate the efficacy of deep neural network models of varying complexity. Our results reveal that deep neural network models require more data points to do better than an NBSVM linear baseline based on character n-grams. Our improved deep neural network models were trained on augmented data and pseudo labeled examples. Our LSTM classifier receives a weighted macro-F1 score of 0.6425 to rank first overall on the Facebook subtask of the shared task. On the social media sub-task, our CNN-LSTM model records a weighted macro-F1 score of 0.5920 to place third overall.
Identifying Aggression and Toxicity in Comments using Capsule Network
Authors: Saurabh Srivastava, Prerna Khurana, Vartika Tewari
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Data Augmentation, Transliteration, Word Embeddings

Aggression and related activities like trolling, hate speech etc. involve toxic comments in various forms. These are common scenarios in today{'}s time and websites react by shutting down their comment sections. To tackle this, an algorithmic solution is preferred to human moderation which is slow and expensive. In this paper, we propose a single model capsule network with focal loss to achieve this task which is suitable for production environment. Our model achieves competitive results over other strong baseline methods, which show its effectiveness and that focal loss exhibits significant improvement in such cases where class imbalance is a regular issue. Additionally, we show that the problem of extensive data preprocessing, data augmentation can be tackled by capsule networks implicitly. We achieve an overall ROC AUC of 98.46 on Kaggle-toxic comment dataset and show that it beats other architectures by a good margin. As comments tend to be written in more than one language, and transliteration is a common problem, we further show that our model handles this effectively by applying our model on TRAC shared task dataset which contains comments in code-mixed Hindi-English.
Aggressive Language Identification Using Word Embeddings and Sentiment Features
Authors: Constantin Or{\u{a}}san
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification, BIG-bench Machine Learning, Language Identification, Word Embeddings

This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on machine learning to identify social media texts which contain aggression. The main features employed by our method are information extracted from word embeddings and the output of a sentiment analyser. Several machine learning methods and different combinations of features were tried. The official submissions used Support Vector Machines and Random Forests. The official evaluation showed that for texts similar to the ones in the training dataset Random Forests work best, whilst for texts which are different SVMs are a better choice. The evaluation also showed that despite its simplicity the method performs well when compared with more elaborated methods.
Aggression Detection in Social Media using Deep Neural Networks
Authors: Sreekanth Madisetty, Maunendra Sankar Desarkar
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

With the rise of user-generated content in social media coupled with almost non-existent moderation in many such systems, aggressive contents have been observed to rise in such forums. In this paper, we work on the problem of aggression detection in social media. Aggression can sometimes be expressed directly or overtly or it can be hidden or covert in the text. On the other hand, most of the content in social media is non-aggressive in nature. We propose an ensemble based system to classify an input post to into one of three classes, namely, Overtly Aggressive, Covertly Aggressive, and Non-aggressive. Our approach uses three deep learning methods, namely, Convolutional Neural Networks (CNN) with five layers (input, convolution, pooling, hidden, and output), Long Short Term Memory networks (LSTM), and Bi-directional Long Short Term Memory networks (Bi-LSTM). A majority voting based ensemble method is used to combine these classifiers (CNN, LSTM, and Bi-LSTM). We trained our method on Facebook comments dataset and tested on Facebook comments (in-domain) and other social media posts (cross-domain). Our system achieves the F1-score (weighted) of 0.604 for Facebook posts and 0.508 for social media posts.
Merging Datasets for Aggressive Text Identification
Authors: Paula Fortuna, Jos{\'e} Ferreira, Luiz Pires, Guilherme Routar, S{\'e}rgio Nunes
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification, Classification, General Classification

This paper presents the approach of the team {``}groutar{''} to the shared task on Aggression Identification, considering the test sets in English, both from Facebook and general Social Media. This experiment aims to test the effect of merging new datasets in the performance of classification models. We followed a standard machine learning approach with training, validation, and testing phases, and considered features such as part-of-speech, frequencies of insults, punctuation, sentiment, and capitalization. In terms of algorithms, we experimented with Boosted Logistic Regression, Multi-Layer Perceptron, Parallel Random Forest and eXtreme Gradient Boosting. One question appearing was how to merge datasets using different classification systems (e.g. aggression vs. toxicity). Other issue concerns the possibility to generalize models and apply them to data from different social networks. Regarding these, we merged two datasets, and the results showed that training with similar data is an advantage in the classification of social networks data. However, adding data from different platforms, allowed slightly better results in both Facebook and Social Media, indicating that more generalized models can be an advantage.
Cyberbullying Detection Task: the EBSI-LIA-UNAM System (ELU) at COLING'18 TRAC-1
Authors: Ignacio Arroyo-Fern{\'a}ndez, Dominic Forest, Juan-Manuel Torres-Moreno, Mauricio Carrasco-Ruiz, Thomas Legeleux, Karen Joannette
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

The phenomenon of cyberbullying has growing in worrying proportions with the development of social networks. Forums and chat rooms are spaces where serious damage can now be done to others, while the tools for avoiding on-line spills are still limited. This study aims to assess the ability that both classical and state-of-the-art vector space modeling methods provide to well known learning machines to identify aggression levels in social network cyberbullying (i.e. social network posts manually labeled as Overtly Aggressive, Covertly Aggressive and Non-aggressive). To this end, an exploratory stage was performed first in order to find relevant settings to test, i.e. by using training and development samples, we trained multiple learning machines using multiple vector space modeling methods and discarded the less informative configurations. Finally, we selected the two best settings and their voting combination to form three competing systems. These systems were submitted to the competition of the TRACK-1 task of the Workshop on Trolling, Aggression and Cyberbullying. Our voting combination system resulted second place in predicting Aggression levels on a test set of untagged social network posts.
Aggression Identification Using Deep Learning and Data Augmentation
Authors: Julian Risch, Ralf Krestel
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification, Data Augmentation, Deep Learning

Social media platforms allow users to share and discuss their opinions online. However, a minority of user posts is aggressive, thereby hinders respectful discussion, and {---} at an extreme level {---} is liable to prosecution. The automatic identification of such harmful posts is important, because it can support the costly manual moderation of online discussions. Further, the automation allows unprecedented analyses of discussion datasets that contain millions of posts. This system description paper presents our submission to the First Shared Task on Aggression Identification. We propose to augment the provided dataset to increase the number of labeled comments from 15,000 to 60,000. Thereby, we introduce linguistic variety into the dataset. As a consequence of the larger amount of training data, we are able to train a special deep neural net, which generalizes especially well to unseen data. To further boost the performance, we combine this neural net with three logistic regression classifiers trained on character and word n-grams, and hand-picked syntactic features. This ensemble is more robust than the individual single models. Our team named {``}Julian{''} achieves an F1-score of 60{\%} on both English datasets, 63{\%} on the Hindi Facebook dataset, and 38{\%} on the Hindi Twitter dataset.
Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text
Authors: Ahmed Husseini Orabi, Mahmoud Husseini Orabi, Qianjia Huang, Diana Inkpen, David Van Bruwaene
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: General Classification, Multi-Task Learning, text-classification, Text Classification

In this paper, we propose a novel deep-learning architecture for text classification, named cross segment-and-concatenate multi-task learning (CSC-MTL). We use CSC-MTL to improve the performance of cyber-aggression detection from text. Our approach provides a robust shared feature representation for multi-task learning by detecting contrasts and similarities among polarity and neutral classes. We participated in the cyber-aggression shared task under the team name uOttawa. We report 59.74{\%} F1 performance for the Facebook test set and 56.9{\%} for the Twitter test set, for detecting aggression from text.
Delete or not Delete? Semi-Automatic Comment Moderation for the Newsroom
Authors: Julian Risch, Ralf Krestel
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles

Comment sections of online news providers have enabled millions to share and discuss their opinions on news topics. Today, moderators ensure respectful and informative discussions by deleting not only insults, defamation, and hate speech, but also unverifiable facts. This process has to be transparent and comprehensive in order to keep the community engaged. Further, news providers have to make sure to not give the impression of censorship or dissemination of fake news. Yet manual moderation is very expensive and becomes more and more unfeasible with the increasing amount of comments. Hence, we propose a semi-automatic, holistic approach, which includes comment features but also their context, such as information about users and articles. For evaluation, we present experiments on a novel corpus of 3 million news comments annotated by a team of professional moderators.
Textual Aggression Detection through Deep Learning
Authors: Antonela Tommasel, Juan Manuel Rodriguez, Daniela Godoy
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Deep Learning, Word Embeddings

Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Internet users. With the widespread of social media networks, bullying, once limited to particular places, can now occur anytime and anywhere. Cyberaggression refers to aggressive online behaviour that aims at harming other individuals, and involves rude, insulting, offensive, teasing or demoralising comments through online social media. Considering the dangerous consequences that cyberaggression has on its victims and its rapid spread amongst internet users (specially kids and teens), it is crucial to understand how cyberbullying occurs to prevent it from escalating. Given the massive information overload on the Web, there is an imperious need to develop intelligent techniques to automatically detect harmful content, which would allow the large-scale social media monitoring and early detection of undesired situations. This paper presents the Isistanitos{'}s approach for detecting aggressive content in multiple social media sites. The approach is based on combining Support Vector Machines and Recurrent Neural Network models for analysing a wide-range of character, word, word embeddings, sentiment and irony features. Results confirmed the difficulty of the task (particularly for detecting covert aggressions), showing the limitations of traditionally used features.
Combining Shallow and Deep Learning for Aggressive Text Detection
Authors: Viktor Golem, Mladen Karan, Jan {\v{S}}najder
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: BIG-bench Machine Learning, Deep Learning, Text Detection

We describe the participation of team TakeLab in the aggression detection shared task at the TRAC1 workshop for English. Aggression manifests in a variety of ways. Unlike some forms of aggression that are impossible to prevent in day-to-day life, aggressive speech abounding on social networks could in principle be prevented or at least reduced by simply disabling users that post aggressively worded messages. The first step in achieving this is to detect such messages. The task, however, is far from being trivial, as what is considered as aggressive speech can be quite subjective, and the task is further complicated by the noisy nature of user-generated text on social networks. Our system learns to distinguish between open aggression, covert aggression, and non-aggression in social media texts. We tried different machine learning approaches, including traditional (shallow) machine learning models, deep learning models, and a combination of both. We achieved respectable results, ranking 4th and 8th out of 31 submissions on the Facebook and Twitter test sets, respectively.
Filtering Aggression from the Multilingual Social Media Feed
Authors: S Modha, ip, Prasenjit Majumder, M, Thomas l
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Aggression Identification, BIG-bench Machine Learning, Information Retrieval, Multi-class Classification, Retrieval

This paper describes the participation of team DA-LD-Hildesheim from the Information Retrieval Lab(IRLAB) at DA-IICT Gandhinagar, India in collaboration with the University of Hildesheim, Germany and LDRP-ITR, Gandhinagar, India in a shared task on Aggression Identification workshop in COLING 2018. The objective of the shared task is to identify the level of aggression from the User-Generated contents within Social media written in English, Devnagiri Hindi and Romanized Hindi. Aggression levels are categorized into three predefined classes namely: {`}Overtly Aggressive{`}, {`}Covertly Aggressive{`} and {`}Non-aggressive{`}. The participating teams are required to develop a multi-class classifier which classifies User-generated content into these pre-defined classes. Instead of relying on a bag-of-words model, we have used pre-trained vectors for word embedding. We have performed experiments with standard machine learning classifiers. In addition, we have developed various deep learning models for the multi-class classification problem. Using the validation data, we found that validation accuracy of our deep learning models outperform all standard machine learning classifiers and voting based ensemble techniques and results on test data support these findings. We have also found that hyper-parameters of the deep neural network are the keys to improve the results.
Learning Diachronic Analogies to Analyze Concept Change
Authors: Matthias Orlikowski, Matthias Hartung, Philipp Cimiano
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Word Embeddings

We propose to study the evolution of concepts by learning to complete diachronic analogies between lists of terms which relate to the same concept at different points in time. We present a number of models based on operations on word embedddings that correspond to different assumptions about the characteristics of diachronic analogies and change in concept vocabularies. These are tested in a quantitative evaluation for nine different concepts on a corpus of Dutch newspapers from the 1950s and 1980s. We show that a model which treats the concept terms as analogous and learns weights to compensate for diachronic changes (weighted linear combination) is able to more accurately predict the missing term than a learned transformation and two baselines for most of the evaluated concepts. We also find that all models tend to be coherent in relation to the represented concept, but less discriminative in regard to other concepts. Additionally, we evaluate the effect of aligning the time-specific embedding spaces using orthogonal Procrustes, finding varying effects on performance, depending on the model, concept and evaluation metric. For the weighted linear combination, however, results improve with alignment in a majority of cases. All related code is released publicly.
A Linked Coptic Dictionary Online
Authors: Frank Feder, Maxim Kupreyev, Emma Manning, Caroline T. Schroeder, Amir Zeldes
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

We describe a new project publishing a freely available online dictionary for Coptic. The dictionary encompasses comprehensive cross-referencing mechanisms, including linking entries to an online scanned edition of Crum{'}s Coptic Dictionary, internal cross-references and etymological information, translated searchable definitions in English, French and German, and linked corpus data which provides frequencies and corpus look-up for headwords and multiword expressions. Headwords are available for linking in external projects using a REST API. We describe the challenges in encoding our dictionary using TEI XML and implementing linking mechanisms to construct a Web interface querying frequency information, which draw on NLP tools to recognize inflected forms in context. We evaluate our dictionary{'}s coverage using digital corpora of Coptic available online.
Using relative entropy for detection and analysis of periods of diachronic linguistic change
Authors: Stefania Degaetano-Ortlieb, Elke Teich
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

We present a data-driven approach to detect periods of linguistic change and the lexical and grammatical features contributing to change. We focus on the development of scientific English in the late modern period. Our approach is based on relative entropy (Kullback-Leibler Divergence) comparing temporally adjacent periods and sliding over the time line from past to present. Using a diachronic corpus of scientific publications of the Royal Society of London, we show how periods of change reflect the interplay between lexis and grammar, where periods of lexical expansion are typically followed by periods of grammatical consolidation resulting in a balance between expressivity and communicative efficiency. Our method is generic and can be applied to other data sets, languages and time ranges.
Cliche Expressions in Literary and Genre Novels
Authors: Andreas van Cranenburgh
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Should writers {``}avoid clich{\'e}s like the plague{''}? Clich{\'e}s are said to be a prominent characteristic of {``}low brow{''} literature, and conversely, a negative marker of {``}high brow{''} literature. Clich{\'e}s may concern the storyline, the characters, or the style of writing. We focus on clich{\'e} expressions, ready-made stock phrases which can be taken as a sign of uncreative writing. We present a corpus study in which we examine to what extent clich{\'e} expressions can be attested in a corpus of various kinds of contemporary fiction, based on a large, curated lexicon of clich{\'e} expressions. The results show to what extent the negative view on clich{\'e}s is supported by data: we find a significant negative correlation of -0.48 between clich{\'e} density and literary ratings of texts. We also investigate interactions with genre and characterize the language of clich{\'e}s with several basic textual features. Code used for this paper is available at \url{https://github.com/andreasvc/litcliches/}