pwc-archive/papers-with-abstracts huggingface dataset:
DUTH at SemEval-2018 Task 2: Emoji Prediction in Tweets
Authors: Dimitrios Effrosynidis, Georgios Peikos, Symeon Symeonidis, Avi Arampatzis
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Information Retrieval, Task 2

This paper describes the approach that was developed for SemEval 2018 Task 2 (Multilingual Emoji Prediction) by the DUTH Team. First, we employed a combination of pre-processing techniques to reduce the noise of tweets and produce a number of features. Then, we built several N-grams, to represent the combination of word and emojis. Finally, we trained our system with a tuned LinearSVC classifier. Our approach in the leaderboard ranked 18th amongst 48 teams.
TAJJEB at SemEval-2018 Task 2: Traditional Approaches Just Do the Job with Emoji Prediction
Authors: Angelo Basile, Kenny W. Lino
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Opinion Mining, Sentiment Analysis, Task 2, Word Embeddings

Emojis are widely used on social media andunderstanding their meaning is important forboth practical purposes (e.g. opinion mining,sentiment detection) and theoretical purposes(e.g. how different L1 speakers use them, dothey have some syntax?); this paper presents aset of experiments that aim to predict a singleemoji from a tweet. We built different mod-els and we found that the test results are verydifferent from the validation results.
SyntNN at SemEval-2018 Task 2: is Syntax Useful for Emoji Prediction? Embedding Syntactic Trees in Multi Layer Perceptrons
Authors: Fabio Massimo Zanzotto, Andrea Santilli
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Task 2

In this paper, we present SyntNN as a way to include traditional syntactic models in multilayer neural networks used in the task of Semeval Task 2 of emoji prediction. The model builds on the distributed tree embedder also known as distributed tree kernel. Initial results are extremely encouraging but additional analysis is needed to overcome the problem of overfitting.
CENNLP at SemEval-2018 Task 2: Enhanced Distributed Representation of Text using Target Classes for Emoji Prediction Representation
Authors: Naveen J R, Hariharan V, Barathi Ganesh H. B., An Kumar M, Soman K. P
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Cultural Vocal Bursts Intensity Prediction, Opinion Mining, Sentiment Analysis, Task 2

Emoji is one of the {``}fastest growing language {''} in pop-culture, especially in social media and it is very unlikely for its usage to decrease. These are generally used to bring an extra level of meaning to the texts, posted on social media platforms. Providing such an added info, gives more insights to the plain text, arising to hidden interpretation within the text. This paper explains our analysis on Task 2, {''} Multilingual Emoji Prediction{''} sharedtask conducted by Semeval-2018. In the task, a predicted emoji based on a piece of Twitter text are labelled under 20 different classes (most commonly used emojis) where these classes are learnt and further predicted are made for unseen Twitter text. In this work, we have experimented and analysed emojis predicted based on Twitter text, as a classification problem where the entailing emoji is considered as a label for every individual text data. We have implemented this using distributed representation of text through fastText. Also, we have made an effort to demonstrate how fastText framework can be useful in case of emoji prediction. This task is divide into two subtask, they are based on dataset presented in two different languages English and Spanish.
Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets
Authors: Luciano Gerber, Matthew Shardlow
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Task 2, Text Classification

We present our submission to the Semeval 2018 task on emoji prediction. We used a random forest, with an ensemble of bag-of-words, sentiment and psycholinguistic features. Although we performed well on the trial dataset (attaining a macro f-score of 63.185 for English and 81.381 for Spanish), our approach did not perform as well on the test data. We describe our features and classi cation protocol, as well as initial experiments, concluding with a discussion of the discrepancy between our trial and test results.
Tweety at SemEval-2018 Task 2: Predicting Emojis using Hierarchical Attention Neural Networks and Support Vector Machine
Authors: Daniel Kopev, Atanas Atanasov, Dimitrina Zlatkova, Momchil Hardalov, Ivan Koychev, Ivelina Nikolova, Galia Angelova
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Task 2, Word Embeddings

We present the system built for SemEval-2018 Task 2 on Emoji Prediction. Although Twitter messages are very short we managed to design a wide variety of features: textual, semantic, sentiment, emotion-, and color-related ones. We investigated different methods of text preprocessing including replacing text emojis with respective tokens and splitting hashtags to capture more meaning. To represent text we used word n-grams and word embeddings. We experimented with a wide range of classifiers and our best results were achieved using a SVM-based classifier and a Hierarchical Attention Neural Network.
LIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction
Authors: Ga{\"e}l Guibon, Magalie Ochs, Patrice Bellot
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Information Retrieval, Task 2, Word Embeddings

In this paper we present the system submitted to the SemEval2018 task2 : Multilingual Emoji Prediction. Our system approaches both languages as being equal by first; considering word embeddings associated to automatically computed features of different types, then by applying bagging algorithm RandomForest to predict the emoji of a tweet.
ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets
Authors: Kevin Swanberg, Madiha Mirza, Ted Pedersen, Zhenduo Wang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Feature Engineering, regression, Semantic Textual Similarity

This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set.
NEUROSENT-PDI at SemEval-2018 Task 3: Understanding Irony in Social Networks Through a Multi-Domain Sentiment Model
Authors: Mauro Dragoni
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis, Word Embeddings

This paper describes the NeuroSent system that participated in SemEval 2018 Task 3. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were built by starting from a repository of user generated reviews. Thus, they are specific for sentiment analysis tasks. Then, tweets are converted in the corresponding vector representation and given as input to the neural network with the aim of learning the different semantics contained in each emotion taken into account by the SemEval task. The output layer has been adapted based on the characteristics of each subtask. Preliminary results obtained on the provided training set are encouraging for pursuing the investigation into this direction.
UWB at SemEval-2018 Task 3: Irony detection in English tweets
Authors: Tom{\'a}{\v{s}} Hercig
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: POS, Sentiment Analysis, Word Embeddings

This paper describes our system created for the SemEval-2018 Task 3: Irony detection in English tweets. Our strongly constrained system uses only the provided training data without any additional external resources. Our system is based on Maximum Entropy classifier and various features using parse tree, POS tags, and morphological features. Even without additional lexicons and word embeddings we achieved fourth place in Subtask A and seventh in Subtask B in terms of accuracy.
LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony Detection
Authors: Bilal Ghanem, Francisco Rangel, Paolo Rosso
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis

In this paper we describe our participation in the SemEval-2018 task 3 Shared Task on Irony Detection. We have approached the task with our low dimensionality representation method (LDR), which exploits low dimensional features extracted from text on the basis of the occurrence probability of the words depending on each class. Our intuition is that words in ironic texts have different probability of occurrence than in non-ironic ones. Our approach obtained acceptable results in both subtasks A and B. We have performed an error analysis that shows the difference on correct and incorrect classified tweets.
PunFields at SemEval-2018 Task 3: Detecting Irony by Tools of Humor Analysis
Authors: Elena Mikhalkova, Yuri Karyakin, Alex Voronov, er, Dmitry Grigoriev, Artem Leoznov
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6

The paper describes our search for a universal algorithm of detecting intentional lexical ambiguity in different forms of creative language. At SemEval-2018 Task 3, we used PunFields, the system of automatic analysis of English puns that we introduced at SemEval-2017, to detect irony in tweets. Preliminary tests showed that it can reach the score of F1=0.596. However, at the competition, its result was F1=0.549.
HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection
Authors: Won Ik Cho, Woo Hyun Kang, Nam Soo Kim
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Feature Engineering, Hate Speech Detection, Opinion Mining, Sentiment Analysis

This paper proposes a novel feature extraction process for SemEval task 3: Irony detection in English tweets. The proposed system incorporates a concatenative featurization of tweet and hashtags, which helps distinguishing between the irony-related and the other components. The system embeds tweets into a vector sequence with widely used pretrained word vectors, partially using a character embedding for the words that are out of vocabulary. Identification was performed with BiLSTM and CNN classifiers, achieving F1 score of 0.5939 (23/42) and 0.3925 (10/28) each for the binary and the multi-class case, respectively. The reliability of the proposed scheme was verified by analyzing the Gold test data, which demonstrates how hashtags can be taken into account when identifying various types of irony.
WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
Authors: Omid Rohanian, Shiva Taslimipoor, Richard Evans, Ruslan Mitkov
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis

This paper describes the systems submitted to SemEval 2018 Task 3 {``}Irony detection in English tweets{''} for both subtasks A and B. The first system leveraging a combination of sentiment, distributional semantic, and text surface features is ranked third among 44 teams according to the official leaderboard of the subtask A. The second system with slightly different representation of the features ranked ninth in subtask B. We present a method that entails decomposing tweets into separate parts. Searching for contrast within the constituents of a tweet is an integral part of our system. We embrace an extensive definition of contrast which leads to a vast coverage in detecting ironic content.
Random Decision Syntax Trees at SemEval-2018 Task 3: LSTMs and Sentiment Scores for Irony Detection
Authors: Aidan San
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Binary Classification, Chatbot, Classification, General Classification, Sarcasm Detection, Sentiment Analysis, Word Embeddings

We propose a Long Short Term Memory Neural Network model for irony detection in tweets in this paper. Our model is trained using word embeddings and emoji embeddings. We show that adding sentiment scores to our model improves the F1 score of our baseline LSTM by approximately .012, and therefore show that high-level features can be used to improve word embeddings in certain Natural Language Processing applications. Our model ranks 24/43 for binary classification and 5/31 for multiclass classification. We make our model easily accessible to the research community.
ELiRF-UPV at SemEval-2018 Tasks 1 and 3: Affect and Irony Detection in Tweets
Authors: Jos{\'e}-{\'A}ngel Gonz{\'a}lez, Llu{\'\i}s-F. Hurtado, Ferran Pla
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Emotion Classification, General Classification, Sentiment Analysis

This paper describes the participation of ELiRF-UPV team at tasks 1 and 3 of Semeval-2018. We present a deep learning based system that assembles Convolutional Neural Networks and Long Short-Term Memory neural networks. This system has been used with slight modifications for the two tasks addressed both for English and Spanish. Finally, the results obtained in the competition are reported and discussed.
IronyMagnet at SemEval-2018 Task 3: A Siamese network for Irony detection in Social media
Authors: Aniruddha Ghosh, Tony Veale
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Binary Classification, General Classification, Task 2, Text Classification

This paper describes our system, entitled IronyMagnet, for the 3rd Task of the SemEval 2018 workshop, {``}Irony Detection in English Tweets{''}. In Task 1, irony classification task has been considered as a binary classification task. Now for the first time, finer categories of irony are considered as part of a shared task. In task 2, three types of irony are considered; {``}Irony by contrast{''} - ironic instances where evaluative expression portrays inverse polarity (positive, negative) of the literal proposition; {``}Situational irony{''} - ironic instances where output of a situation do not comply with its expectation; {``}Other verbal irony{''} - instances where ironic intent does not rely on polarity contrast or unexpected outcome. We proposed a Siamese neural network for irony detection, which is consisted of two subnetworks, each containing a long short term memory layer(LSTM) and an embedding layer initialized with vectors from Glove word embedding 1 . The system achieved a f-score of 0.72, and 0.50 in task 1, and task 2 respectively.
CTSys at SemEval-2018 Task 3: Irony in Tweets
Authors: Myan Sherif, Sherine Mamdouh, Wegdan Ghazi
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Feature Engineering, General Classification

The objective of this paper is to provide a description for a system built as our participation in SemEval-2018 Task 3 on Irony detection in English tweets. This system classifies a tweet as either ironic or non-ironic through a supervised learning approach. Our approach is to implement three feature models, and to then improve the performance of the supervised learning classification of tweets by combining many data features and using a voting system on four different classifiers. We describe the process of pre-processing data, extracting features, and running different types of classifiers against our feature set. In the competition, our system achieved an F1-score of 0.4675, ranking 35th in subtask A, and an F1-score score of 0.3014 ranking 22th in subtask B.
Irony Detector at SemEval-2018 Task 3: Irony Detection in English Tweets using Word Graph
Authors: Usman Ahmed, Lubna Zafar, Faiza Qayyum, Muhammad Arshad Islam
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Opinion Mining, Sentiment Analysis

This paper describes the Irony detection system that participates in SemEval-2018 Task 3: Irony detection in English tweets. The system participated in the subtasks A and B. This paper discusses the results of our system in the development, evaluation and post evaluation. Each class in the dataset is represented as directed unweighted graphs. Then, the comparison is carried out with each class graph which results in a vector. This vector is used as features by machine learning algorithm. The model is evaluated on a hold on strategy. The organizers randomly split 80{\%} (3,833 instances) training set (provided to the participant in training their system) and testing set 20{\%}(958 instances). The test set is reserved to evaluate the performance of participants systems. During the evaluation, our system ranked 23 in the Coda Lab result of the subtask A (binary class problem). The binary class system achieves accuracy 0.6135, precision 0.5091, recall 0.7170 and F measure 0.5955. The subtask B (multi-class problem) system is ranked 22 in Coda Lab results. The multiclass model achieves the accuracy 0.4158, precision 0.4055, recall 0.3526 and f measure 0.3101.
Lancaster at SemEval-2018 Task 3: Investigating Ironic Features in English Tweets
Authors: Edward Dearden, Alistair Baron
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis

This paper describes the system we submitted to SemEval-2018 Task 3. The aim of the system is to distinguish between irony and non-irony in English tweets. We create a targeted feature set and analyse how different features are useful in the task of irony detection, achieving an F1-score of 0.5914. The analysis of individual features provides insight that may be useful in future attempts at detecting irony in tweets.
INAOE-UPV at SemEval-2018 Task 3: An Ensemble Approach for Irony Detection in Twitter
Authors: Delia Iraz{\'u} Hern{\'a}ndez Far{\'\i}as, Fern S{\'a}nchez-Vega, o, Manuel Montes-y-G{\'o}mez, Paolo Rosso
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: General Classification, Sentiment Analysis, text-classification, Text Classification, Word Embeddings

This paper describes an ensemble approach to the SemEval-2018 Task 3. The proposed method is composed of two renowned methods in text classification together with a novel approach for capturing ironic content by exploiting a tailored lexicon for irony detection. We experimented with different ensemble settings. The obtained results show that our method has a good performance for detecting the presence of ironic content in Twitter.
ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods
Authors: Zhenghang Yin, Feixiang Wang, Man Lan, Wenting Wang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: BIG-bench Machine Learning, Binary Classification, Classification, Feature Engineering, General Classification

The paper describes our submissions to task 3 in SemEval-2018. There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask B is a fine-grained classification task including four classes. To address them, we explored supervised machine learning method alone and in combination with neural networks.
KLUEnicorn at SemEval-2018 Task 3: A Naive Approach to Irony Detection
Authors: Luise D{\"u}rlich
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sarcasm Detection

This paper describes the KLUEnicorn system submitted to the SemEval-2018 task on {``}Irony detection in English tweets{''}. The proposed system uses a naive Bayes classifier to exploit rather simple lexical, pragmatical and semantical features as well as sentiment. It further takes a closer look at different adverb categories and named entities and factors in word-embedding information.
YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter
Authors: Bo Peng, Jin Wang, Xue-jie Zhang
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, Ensemble Learning, General Classification, Sarcasm Detection, Sentiment Analysis

This paper describe the system we proposed to participate the first year of Irony detection in English tweets competition. Previous works demonstrate that LSTMs models have achieved remarkable performance in natural language processing; besides, combining multiple classification from various individual classifiers in general is more powerful than a single classification. In order to obtain more precision classification of irony detection, our system trained several individual neural network classifiers and combined their results according to the ensemble-learning algorithm.
SSN MLRG1 at SemEval-2018 Task 3: Irony Detection in English Tweets Using MultiLayer Perceptron
Authors: Rajalakshmi S, Angel Deborah S, S Milton Rajendram, Mirnalinee T T
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: feature selection, Opinion Mining, Sarcasm Detection, Sentiment Analysis

Sentiment analysis plays an important role in E-commerce. Identifying ironic and sarcastic content in text plays a vital role in inferring the actual intention of the user, and is necessary to increase the accuracy of sentiment analysis. This paper describes the work on identifying the irony level in twitter texts. The system developed by the SSN MLRG1 team in SemEval-2018 for task 3 (irony detection) uses rule based approach for feature selection and MultiLayer Perceptron (MLP) technique to build the model for multiclass irony classification subtask, which classifies the given text into one of the four class labels.
NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in Tweets
Authors: Harsh Rangwani, Devang Kulshreshtha, Anil Kumar Singh
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Classification, General Classification, Sarcasm Detection

This paper describes our participation in SemEval 2018 Task 3 on Irony Detection in Tweets. We combine linguistic features with pre-trained activations of a neural network. The CNN is trained on the emoji prediction task. We combine the two feature sets and feed them into an XGBoost Classifier for classification. Subtask-A involves classification of tweets into ironic and non-ironic instances whereas Subtask-B involves classification of the tweet into - non-ironic, verbal irony, situational irony or other verbal irony. It is observed that combining features from these two different feature spaces improves our system results. We leverage the SMOTE algorithm to handle the problem of class imbalance in Subtask-B. Our final model achieves an F1-score of 0.65 and 0.47 on Subtask-A and Subtask-B respectively. Our system ranks 4th on both tasks respectively, outperforming the baseline by 6{\%} on Subtask-A and 14{\%} on Subtask-B.
ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English Tweets
Authors: Delia Iraz{\'u} Hern{\'a}ndez Far{\'\i}as, Viviana Patti, Paolo Rosso
Date: 2018-06-01 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Sentiment Analysis

In this paper we describe the system used by the ValenTO team in the shared task on Irony Detection in English Tweets at SemEval 2018. The system takes as starting point emotIDM, an irony detection model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. We experimented with different settings, by exploiting different classifiers and features, and participated both to the binary irony detection task and to the task devoted to distinguish among different types of irony. We report on the results obtained by our system both in a constrained setting and unconstrained setting, where we explored the impact of using additional data in the training phase, such as corpora annotated for the presence of irony or sarcasm from the state of the art. Overall, the performance of our system seems to validate the important role that affective information has for identifying ironic content in Twitter.