pwc-archive/papers-with-abstracts huggingface dataset:
UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features
Authors: Hareesh Bahuleyan, Olga Vechtomova
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Rumour Detection, Stance Detection

This paper describes our system for subtask-A: SDQC for RumourEval, task-8 of SemEval 2017. Identifying rumours, especially for breaking news events as they unfold, is a challenging task due to the absence of sufficient information about the exact rumour stories circulating on social media. Determining the stance of Twitter users towards rumourous messages could provide an indirect way of identifying potential rumours. The proposed approach makes use of topic independent features from two categories, namely cue features and message specific features to fit a gradient boosting classifier. With an accuracy of 0.78, our system achieved the second best performance on subtask-A of RumourEval.
IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification
Authors: Yi-Chin Chen, Zhao-Yang Liu, Hung-Yu Kao
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Stance Detection, Text Categorization

This paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural network for short text categorization using multiple filter sizes. Our approach beats the baseline classifiers on different event data with good F1 scores. The best of our submitted runs achieves rank 1st among all scores on subtask B.
NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter.
Authors: Omar Enayet, Samhaa R. El-Beltagy
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Rumour Detection

Final submission for NileTMRG on RumourEval 2017.
Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules
Authors: Marianela Garc{\'\i}a Lozano, Hanna Lilja, Edward Tj{\"o}rnhammar, Maja Karasalo
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, General Classification, Rumour Detection, Stance Classification

For the competition SemEval-2017 we investigated the possibility of performing stance classification (support, deny, query or comment) for messages in Twitter conversation threads related to rumours. Stance classification is interesting since it can provide a basis for rumour veracity assessment. Our ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9{\%} on the competition{'}s test data set for Task 8A. To improve classification we also experimented with data relabeling and using the grammatical structure of the tweet contents for classification.
DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics
Authors: Ankit Srivastava, Georg Rehm, Julian Moreno Schneider
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, regression, Rumour Detection

We describe our submissions for SemEval-2017 Task 8, Determining Rumour Veracity and Support for Rumours. The Digital Curation Technologies (DKT) team at the German Research Center for Artificial Intelligence (DFKI) participated in two subtasks: Subtask A (determining the stance of a message) and Subtask B (determining veracity of a message, closed variant). In both cases, our implementation consisted of a Multivariate Logistic Regression (Maximum Entropy) classifier coupled with hand-written patterns and rules (heuristics) applied in a post-process cascading fashion. We provide a detailed analysis of the system performance and report on variants of our systems that were not part of the official submission.
ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models
Authors: Feixiang Wang, Man Lan, Yuanbin Wu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: BIG-bench Machine Learning, Rumour Detection, Sentiment Analysis

This paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a lot of reply tweets, the subtask A is to label whether these tweets are support, deny, query or comment, and the subtask B aims to predict the veracity (i.e., true, false, and unverified) with a confidence (in range of 0-1) of the given rumoured tweet. For both subtasks, we adopted supervised machine learning methods, incorporating rich features. Since training data is imbalanced, we specifically designed a two-step classifier to address subtask A .
IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation
Authors: Vikram Singh, Sunny Narayan, Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Decision Making, General Classification, Rumour Detection

This paper describes our system participation in the SemEval-2017 Task 8 {`}RumourEval: Determining rumour veracity and support for rumours{'}. The objective of this task was to predict the stance and veracity of the underlying rumour. We propose a supervised classification approach employing several lexical, content and twitter specific features for learning. Evaluation shows promising results for both the problems.
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Authors: Keith Cortis, Andr{\'e} Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, H, Siegfried schuh, Brian Davis
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentiment Analysis

This paper discusses the {``}Fine-Grained Sentiment Analysis on Financial Microblogs and News{''} task as part of SemEval-2017, specifically under the {``}Detecting sentiment, humour, and truth{''} theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.
SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation
Authors: Jonathan May, Jay Priyadarshi
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Abstract Meaning Representation, AMR Parsing, Machine Translation

In this report we summarize the results of the 2017 AMR SemEval shared task. The task consisted of two separate yet related subtasks. In the parsing subtask, participants were asked to produce Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the biomedical domain. In the generation subtask, participants were asked to generate English sentences given AMR graphs in the news/forum domain. A total of five sites participated in the parsing subtask, and four participated in the generation subtask. Along with a description of the task and the participants{'} systems, we show various score ablations and some sample outputs.
SemEval-2017 Task 11: End-User Development using Natural Language
Authors: Juliano Sales, H, Siegfried schuh, Andr{\'e} Freitas
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Semantic Parsing

This task proposes a challenge to support the interaction between users and applications, micro-services and software APIs using natural language. The task aims for supporting the evaluation and evolution of the discussions surrounding the natural language processing approaches within the context of end-user natural language programming, under scenarios of high semantic heterogeneity/gap.
SemEval-2017 Task 12: Clinical TempEval
Authors: Steven Bethard, Guergana Savova, Martha Palmer, James Pustejovsky
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Domain Adaptation, Temporal Information Extraction

Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)? Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were evaluated on clinical and pathology notes from Mayo Clinic cancer patients, annotated with an extension of TimeML for the clinical domain. 11 teams participated in the tasks, with the best systems achieving F1 scores above 0.55 for time expressions, above 0.70 for event expressions, and above 0.40 for temporal relations. Most tasks observed about a 20 point drop over Clinical TempEval 2016, where systems were trained and evaluated on the same domain (colon cancer).
BB\_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs
Authors: Mathieu Cliche
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Sentiment Analysis, Twitter Sentiment Analysis, Word Embeddings

In this paper we describe our attempt at producing a state-of-the-art Twitter sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled data to pre-train word embeddings. We then use a subset of the unlabeled data to fine tune the embeddings using distant supervision. The final CNNs and LSTMs are trained on the SemEval-2017 Twitter dataset where the embeddings are fined tuned again. To boost performances we ensemble several CNNs and LSTMs together. Our approach achieved first rank on all of the five English subtasks amongst 40 teams.
Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR.
Authors: Gerasimos Lampouras, Andreas Vlachos
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Abstract Meaning Representation, Language Modeling, Language Modelling, Machine Translation, Text Generation

This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2). We cast language generation from AMR as a sequence of actions (e.g., insert/remove/rename edges and nodes) that progressively transform the AMR graph into a dependency parse tree. This transition-based approach relies on the fact that an AMR graph can be considered structurally similar to a dependency tree, with a focus on content rather than function words. An added benefit to this approach is the greater amount of data we can take advantage of to train the parse-to-text linearizer. Our submitted run on the test data achieved a BLEU score of 3.32 and a Trueskill score of -22.04 on automatic and human evaluation respectively.
The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
Authors: Waleed Ammar, Matthew Peters, Ch Bhagavatula, ra, Russell Power
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Dependency Parsing, Part-Of-Speech Tagging, Relation, Relation Extraction

This paper describes our submission for the ScienceIE shared task (SemEval- 2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3).
LIMSI-COT at SemEval-2017 Task 12: Neural Architecture for Temporal Information Extraction from Clinical Narratives
Authors: Julien Tourille, Olivier Ferret, Xavier Tannier, Aur{\'e}lie N{\'e}v{\'e}ol
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Domain Adaptation, Entity Extraction using GAN, Relation, Relation Extraction, Temporal Information Extraction, Temporal Relation Extraction

In this paper we present our participation to SemEval 2017 Task 12. We used a neural network based approach for entity and temporal relation extraction, and experimented with two domain adaptation strategies. We achieved competitive performance for both tasks.
OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model
Authors: Ramy Baly, Gilbert Badaro, Ali Hamdi, Rawan Moukalled, Rita Aoun, Georges El-Khoury, Ahmad Al Sallab, Hazem Hajj, Nizar Habash, Khaled Shaban, Wassim El-Hajj
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Opinion Mining, Sentiment Analysis

While sentiment analysis in English has achieved significant progress, it remains a challenging task in Arabic given the rich morphology of the language. It becomes more challenging when applied to Twitter data that comes with additional sources of noise including dialects, misspellings, grammatical mistakes, code switching and the use of non-textual objects to express sentiments. This paper describes the {``}OMAM{''} systems that we developed as part of SemEval-2017 task 4. We evaluate English state-of-the-art methods on Arabic tweets for subtask A. As for the remaining subtasks, we introduce a topic-based approach that accounts for topic specificities by predicting topics or domains of upcoming tweets, and then using this information to predict their sentiment. Results indicate that applying the English state-of-the-art method to Arabic has achieved solid results without significant enhancements. Furthermore, the topic-based method ranked 1st in subtasks C and E, and 2nd in subtask D.
deepSA at SemEval-2017 Task 4: Interpolated Deep Neural Networks for Sentiment Analysis in Twitter
Authors: Tzu-Hsuan Yang, Tzu-Hsuan Tseng, Chia-Ping Chen
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, Text Classification, Word Embeddings

In this paper, we describe our system implementation for sentiment analysis in Twitter. This system combines two models based on deep neural networks, namely a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network, through interpolation. Distributed representation of words as vectors are input to the system, and the output is a sentiment class. The neural network models are trained exclusively with the data sets provided by the organizers of SemEval-2017 Task 4 Subtask A. Overall, this system has achieved 0.618 for the average recall rate, 0.587 for the average F1 score, and 0.618 for accuracy.
NNEMBs at SemEval-2017 Task 4: Neural Twitter Sentiment Classification: a Simple Ensemble Method with Different Embeddings
Authors: Yichun Yin, Yangqiu Song, Ming Zhang
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Feature Engineering, General Classification, Learning Word Embeddings, Sentiment Analysis, Sentiment Classification, Word Embeddings

Recently, neural twitter sentiment classification has become one of state-of-thearts, which relies less feature engineering work compared with traditional methods. In this paper, we propose a simple and effective ensemble method to further boost the performances of neural models. We collect several word embedding sets which are publicly released (often are learned on different corpus) or constructed by running Skip-gram on released large-scale corpus. We make an assumption that different word embeddings cover different words and encode different semantic knowledge, thus using them together can improve the generalizations and performances of neural models. In the SemEval 2017, our method ranks 1st in Accuracy, 5th in AverageR. Meanwhile, the additional comparisons demonstrate the superiority of our model over these ones based on only one word embedding set. We release our code for the method duplicability.
CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification
Authors: Raj Kumar Gupta, Yinping Yang
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Opinion Mining, Sarcasm Detection, Sentiment Analysis, Sentiment Classification

This paper describes a system developed for a shared sentiment analysis task and its subtasks organized by SemEval-2017. A key feature of our system is the embedded ability to detect sarcasm in order to enhance the performance of sentiment classification. We first constructed an affect-cognition-sociolinguistics sarcasm features model and trained a SVM-based classifier for detecting sarcastic expressions from general tweets. For sentiment prediction, we developed CrystalNest{--} a two-level cascade classification system using features combining sarcasm score derived from our sarcasm classifier, sentiment scores from Alchemy, NRC lexicon, n-grams, word embedding vectors, and part-of-speech features. We found that the sarcasm detection derived features consistently benefited key sentiment analysis evaluation metrics, in different degrees, across four subtasks A-D.
SINAI at SemEval-2017 Task 4: User based classification
Authors: Salud Mar{\'\i}a Jim{\'e}nez-Zafra, Arturo Montejo-R{\'a}ez, Maite Martin, L. Alfonso Ure{\~n}a-L{\'o}pez
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, Collaborative Filtering, General Classification, Sentiment Analysis, Word Embeddings

This document describes our participation in SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have only reported results for subtask B - English, determining the polarity towards a topic on a two point scale (positive or negative sentiment). Our main contribution is the integration of user information in the classification process. A SVM model is trained with Word2Vec vectors from user{'}s tweets extracted from his timeline. The obtained results show that user-specific classifiers trained on tweets from user timeline can introduce noise as they are error prone because they are classified by an imperfect system. This encourages us to explore further integration of user information for author-based Sentiment Analysis.
HLP@UPenn at SemEval-2017 Task 4A: A simple, self-optimizing text classification system combining dense and sparse vectors
Authors: Abeed Sarker, Graciela Gonzalez
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, Epidemiology, General Classification, Sentiment Analysis, text-classification, Text Classification

We present a simple supervised text classification system that combines sparse and dense vector representations of words, and generalized representations of words via clusters. The sparse vectors are generated from word n-gram sequences (1-3). The dense vector representations of words (embeddings) are learned by training a neural network to predict neighboring words in a large unlabeled dataset. To classify a text segment, the different representations of it are concatenated, and the classification is performed using Support Vector Machines (SVM). Our system is particularly intended for use by non-experts of natural language processing and machine learning, and, therefore, the system does not require any manual tuning of parameters or weights. Given a training set, the system automatically generates the training vectors, optimizes the relevant hyper-parameters for the SVM classifier, and trains the classification model. We evaluated this system on the SemEval-2017 English sentiment analysis task. In terms of average F1-score, our system obtained 8th position out of 39 submissions (F1-score: 0.632, average recall: 0.637, accuracy: 0.646).
ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter
Authors: Enkhzol Dovdon, Jos{\'e} Saias
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Sentiment Analysis, Text Classification

This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic-Based Message Polarity Classification according to a two-point scale) of SemEval-2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system.
SentiME++ at SemEval-2017 Task 4: Stacking State-of-the-Art Classifiers to Enhance Sentiment Classification
Authors: Rapha{\"e}l Troncy, Enrico Palumbo, Efstratios Sygkounas, Giuseppe Rizzo
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, Sentiment Classification

In this paper, we describe the participation of the SentiME++ system to the SemEval 2017 Task 4A {``}Sentiment Analysis in Twitter{''} that aims to classify whether English tweets are of positive, neutral or negative sentiment. SentiME++ is an ensemble approach to sentiment analysis that leverages stacked generalization to automatically combine the predictions of five state-of-the-art sentiment classifiers. SentiME++ achieved officially 61.30{\%} F1-score, ranking 12th out of 38 participants.
TWINA at SemEval-2017 Task 4: Twitter Sentiment Analysis with Ensemble Gradient Boost Tree Classifier
Authors: Naveen Kumar Laskari, Suresh Kumar Sanampudi
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Classification, General Classification, Information Retrieval, Position, Sentiment Analysis, Twitter Sentiment Analysis

This paper describes the TWINA system, with which we participated in SemEval-2017 Task 4B (Topic Based Message Polarity Classification {--} Two point scale) and 4D (two-point scale Tweet quantification). We implemented ensemble based Gradient Boost Trees classification method for both the tasks. Our system could perform well for the task 4D and ranked 13th among 15 teams, for the task 4B our model ranked 23rd position.
Tw-StAR at SemEval-2017 Task 4: Sentiment Classification of Arabic Tweets
Authors: Hala Mulki, Hatem Haddad, Mourad Gridach, Ismail Babaoglu
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Decision Making, General Classification, Sentiment Analysis, Sentiment Classification, Twitter Sentiment Analysis

In this paper, we present our contribution in SemEval 2017 international workshop. We have tackled task 4 entitled {``}Sentiment analysis in Twitter{''}, specifically subtask 4A-Arabic. We propose two Arabic sentiment classification models implemented using supervised and unsupervised learning strategies. In both models, Arabic tweets were preprocessed first then various schemes of bag-of-N-grams were extracted to be used as features. The final submission was selected upon the best performance achieved by the supervised learning-based model. However, the results obtained by the unsupervised learning-based model are considered promising and evolvable if more rich lexica are adopted in further work.
OMAM at SemEval-2017 Task 4: English Sentiment Analysis with Conditional Random Fields
Authors: Chukwuyem Onyibe, Nizar Habash
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Opinion Mining, Sentiment Analysis, Stance Detection

We describe a supervised system that uses optimized Condition Random Fields and lexical features to predict the sentiment of a tweet. The system was submitted to the English version of all subtasks in SemEval-2017 Task 4.
Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter
Authors: Athanasia Kolovou, Filippos Kokkinos, Aris Fergadis, Pinelopi Papalampidi, Elias Iosif, Mal, Nikolaos rakis, Elisavet Palogiannidi, Haris Papageorgiou, Shrikanth Narayanan, Alex Potamianos, ros
Date: 2017-08-01 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification, Sentiment Analysis, Word Embeddings

In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter. Specifically the proposed system participated both to tweet polarity classification (two-, three- and five class) and tweet quantification (two and five-class) tasks.