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SEERLAB: A System for Extracting Keyphrases from Scholarly Documents
We describe the SEERLAB system that participated in the SemEval 2010’s Keyphrase Extraction Task. SEERLAB utilizes the DBLP corpus for generating a set of candidate keyphrases from a document. Random …...
The TALP-UPC Approach to System Selection: Asiya Features and Pairwise Classification Using Random Forests
This paper describes the TALP-UPC par- ticipation in the WMT’13 Shared Task on Quality Estimation (QE). Our partic- ipation is reduced to task 1.2 on System Selection. We used a …...
ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity Estimation
This paper reports our submissions to the Cross Level Semantic Similarity (CLSS) task in SemEval 2014. We submitted one Random Forest regression system on each cross level text pair, i.e., …...
IITP: Supervised Machine Learning for Aspect based Sentiment Analysis
The shared task on Aspect based Senti- ment Analysis primarily focuses on mining relevant information from the thousands of online reviews available for a popular product or service. In this …...
JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest
In this paper, we describe how we cre- ated a meta-classifier to detect the mes- sage-level sentiment of tweets. We par- ticipated in SemEval-2014 Task 9B by combining the results …...
“How Bullying is this Message?”: A Psychometric Thermometer for Bullying
AbstractCyberbullying statistics are shocking, the number of affected young people is increasing dramatically with the affordability of mobile technology devices combined with a growing number of social networks. This paper …...
Addressing the MFS Bias in WSD systems
AbstractWord Sense Disambiguation (WSD) systems tend to have a strong bias towards assigning the Most Frequent Sense (MFS), which results in high performance on the MFS but in a very …...
Distinguishing Literal and Non-Literal Usage of German Particle Verbs
This paper provides a binary, token-based classification of German particle verbs (PVs) into literal vs. non-literal usage. A random forest improving standard features (e.g., bag- of-words; affective ratings) with PV-specific …...
IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter
We present the IUCL system, based on super- vised learning, for the shared task on stance detection. Our official submission, the ran- dom forest model, reaches a score of 63.60, …...
JU_NLP at SemEval-2016 Task 11: Identifying Complex Words in a Sentence
The complex word identification task refers to the process of identifying difficult words in a sentence from the perspective of readers be- longing to a specific target audience. This task …...
Nine Features in a Random Forest to Learn Taxonomical Semantic Relations
AbstractROOT9 is a supervised system for the classification of hypernyms, co-hyponyms and random words that is derived from the already introduced ROOT13 (Santus et al., 2016). It relies on a …...
BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches
AbstractThis paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system …...
PKU_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge
AbstractThis paper presents a system that participated in SemEval 2017 Task 10 (subtask A and subtask B): Extracting Keyphrases and Relations from Scientific Publications (Augenstein et al., 2017). Our proposed …...
SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets
AbstractSentiment analysis is the process of identifying the opinion expressed in text. Recently it has been used to study behavioral finance, and in particular the effect of opinions and emotions …...
Stylometric Studies based on Tone and Word Length Motifs
: We propose a new approach to stylometric analysis combining lexical and textual information, but without annotation or other pre-processing. In particular, our study makes use Chinese tones motifs and …...
UdL at SemEval-2017 Task 1: Semantic Textual Similarity Estimation of English Sentence Pairs Using Regression Model over Pairwise Features
AbstractThis paper describes the model UdL we proposed to solve the semantic textual similarity task of SemEval 2017 workshop. The track we participated in was estimating the semantics relatedness of …...
LIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction
AbstractIn 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 …...
Manchester Metropolitan at SemEval-2018 Task 2: Random Forest with an Ensemble of Features for Predicting Emoji in Tweets
AbstractWe 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 …...
DBMS-KU at SemEval-2019 Task 9: Exploring Machine Learning Approaches in Classifying Text as Suggestion or Non-Suggestion
AbstractThis paper describes the participation of DBMS-KU team in the SemEval 2019 Task 9, that is, suggestion mining from online reviews and forums. To deal with this task, we explore …...
MineriaUNAM at SemEval-2019 Task 5: Detecting Hate Speech in Twitter using Multiple Features in a Combinatorial Framework
AbstractThis paper presents our approach to the Task 5 of Semeval-2019, which aims at detecting hate speech against immigrants and women in Twitter. The task consists of two sub-tasks, in …...
NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier
AbstractThe paper presents a system developed for the SemEval-2019 competition Task 5 hat- Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) …...
Turkish Tweet Classification with Transformer Encoder
AbstractShort-text classification is a challenging task, due to the sparsity and high dimensionality of the feature space. In this study, we aim to analyze and classify Turkish tweets based on …...
A Linguistic Perspective on Reference: Choosing a Feature Set for Generating Referring Expressions in Context
AbstractThis paper reports on a structured evaluation of feature-based Machine Learning algorithms for selecting the form of a referring expression in discourse context. Based on this evaluation, we selected seven …...
Aspect-based Sentiment Analysis on Indonesia’s Tourism Destinations Based on Google Maps User Code-Mixed Reviews (Study Case: Borobudur and Prambanan Temples)
In this paper, we conducted an aspect-based sentiment analysis using Google Maps user reviews of Indonesia’s tourism destinations which are Borobudur and Prambanan Temple. The aspects we used are Attractions, …...
Offensive Video Detection: Dataset and Baseline Results
AbstractWeb-users produce and publish high volumes of data of various types, such as text, images, and videos. The platforms try to restrain their users from publishing offensive content to keep …...
archer at SemEval-2021 Task 1: Contextualising Lexical Complexity
AbstractEvaluating the complexity of a target word in a sentential context is the aim of the Lexical Complexity Prediction task at SemEval-2021. This paper presents the system created to assess …...
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features
AbstractWe report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) …...
Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling
AbstractThis paper describes the submission of the team Amrita_CEN to the shared task on iSarcasm Eval: Intended Sarcasm Detection in English and Arabic at SemEval 2022. We employed machine learning …...
An Empirical Comparison of off-the-shelve Semantic Similarity methods for down-streaming Meeting Similarity
Automatic Minuting consists of automati- cally creating minutes from multiparty meet- ing transcripts. In this paper, we solve two rel- evant problems of this domain (1) given a pair of …...
Friedrich Nietzsche at SemEval-2023 Task 4: Detection of Human Values from Text Using Machine Learning
AbstractLiterature permeates through almost every facet of our lives, whether through books, magazines, or internet articles. Moreover, every piece of written work contains ideas and opinions that we tend to …...
Lexical Complexity Prediction using Word Embeddings
Lexical complexity is crucial for reading comprehension. In the past, research work of lexical complexity prediction mainly focuses on differentiating the complexity difference between two words. Moreover, most of the …...
PanwarJayant at SemEval-2023 Task 10: Exploring the Effectiveness of Conventional Machine Learning Techniques for Online Sexism Detection
AbstractThe rapid growth of online communication using social media platforms has led to an increase in the presence of hate speech, especially in terms of sexist language online. The proliferation …...