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Found 54 papers, 0 papers with code
Analysing Rhetorical Structure as a Key Feature of Summary Coherence
We present a model for automatic scoring of coherence based on comparing the rhetorical structure (RS) of college student summaries in L2 (English) against expert summaries.
Evaluating Automatic Term Extraction Methods on Individual Documents
Automatic Term Extraction (ATE) extracts terminology from domain-specific corpora.
Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context
We address the task of automatically detecting toxic content in user generated texts.
TakeLab at SemEval-2019 Task 4: Hyperpartisan News Detection
In this paper, we demonstrate the system built to solve the SemEval-2019 task 4: Hyperpartisan News Detection (Kiesel et al., 2019), the task of automatically determining whether an article is heavily biased towards one side of the political spectrum.
Cross-Domain Detection of Abusive Language Online
We investigate to what extent the models trained to detect general abusive language generalize between different datasets labeled with different abusive language types.
Not Just Depressed: Bipolar Disorder Prediction on Reddit
Bipolar disorder, an illness characterized by manic and depressive episodes, affects more than 60 million people worldwide.
Combining Shallow and Deep Learning for Aggressive Text Detection
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.
Reddit: A Gold Mine for Personality Prediction
Automated personality prediction from social media is gaining increasing attention in natural language processing and social sciences communities.
Lexical Substitution for Evaluating Compositional Distributional Models
LexSub is a more natural task, enables us to evaluate meaning composition at the level of individual words, and provides a common ground to compare CDSMs with dedicated LexSub models.
TakeLab at SemEval-2018 Task12: Argument Reasoning Comprehension with Skip-Thought Vectors
This paper describes our system for the SemEval-2018 Task 12: Argument Reasoning Comprehension Task.
TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts
We describe two systems for semantic relation classification with which we participated in the SemEval 2018 Task 7, subtask 1 on semantic relation classification: an SVM model and a CNN model.
Using Analytic Scoring Rubrics in the Automatic Assessment of College-Level Summary Writing Tasks in L2
Assessing summaries is a demanding, yet useful task which provides valuable information on language competence, especially for second language learners.
Predicting News Values from Headline Text and Emotions
We present a preliminary study on predicting news values from headline text and emotions.
Toward Stance Classification Based on Claim Microstructures
Claims are the building blocks of arguments and the reasons underpinning opinions, thus analyzing claims is important for both argumentation mining and opinion mining.
Does Free Word Order Hurt? Assessing the Practical Lexical Function Model for Croatian
The Practical Lexical Function (PLF) model is a model of computational distributional semantics that attempts to strike a balance between expressivity and learnability in predicting phrase meaning and shows competitive results.
TakeLab at SemEval-2017 Task 4: Recent Deaths and the Power of Nostalgia in Sentiment Analysis in Twitter
This paper describes the system we submitted to SemEval-2017 Task 4 (Sentiment Analysis in Twitter), specifically subtasks A, B, and D. Our main focus was topic-based message polarity classification on a two-point scale (subtask B).
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA
We use features based on different semantic similarity models (e. g., Latent Dirichlet Allocation), as well as features based on several types of pre-trained word embeddings.
TakeLab at SemEval-2017 Task 5: Linear aggregation of word embeddings for fine-grained sentiment analysis of financial news
Our best-performing submission scored 3rd on the task out of 29 teams and 4th out of 45 submissions with a cosine score of 0. 733.
TakeLab at SemEval-2017 Task 6: \#RankingHumorIn4Pages
This paper describes our system for humor ranking in tweets within the SemEval 2017 Task 6: {\#}HashtagWars (6A and 6B).
Comparison of Short-Text Sentiment Analysis Methods for Croatian
We focus on the task of supervised sentiment classification of short and informal texts in Croatian, using two simple yet effective methods: word embeddings and string kernels.
Two Layers of Annotation for Representing Event Mentions in News Stories
In this paper, we describe our preliminary study on annotating event mention as a part of our research on high-precision news event extraction models.
The First Cross-Lingual Challenge on Recognition, Normalization, and Matching of Named Entities in Slavic Languages
The reported evaluation figures reflect the relatively higher level of complexity of named entity-related tasks in the context of processing texts in Slavic languages.
Debunking Sentiment Lexicons: A Case of Domain-Specific Sentiment Classification for Croatian
We experiment with the graph-based acquisition of sentiment lexicons, analyze their quality, and investigate how effectively they can be used in sentiment classification.
A Preliminary Study of Croatian Lexical Substitution
Lexical substitution is a task of determining a meaning-preserving replacement for a word in context.
Combining Linguistic Features for the Detection of Croatian Multiword Expressions
As multiword expressions (MWEs) exhibit a range of idiosyncrasies, their automatic detection warrants the use of many different features.
Unsupervised Acquisition of Comprehensive Multiword Lexicons using Competition in an n-gram Lattice
We present a new model for acquiring comprehensive multiword lexicons from large corpora based on competition among n-gram candidates.
Predictability of Distributional Semantics in Derivational Word Formation
Compositional distributional semantic models (CDSMs) have successfully been applied to the task of predicting the meaning of a range of linguistic constructions.
VerbCROcean: A Repository of Fine-Grained Semantic Verb Relations for Croatian
In this paper we describe VerbCROcean, a broad-coverage repository of fine-grained semantic relations between Croatian verbs.
Cro36WSD: A Lexical Sample for Croatian Word Sense Disambiguation
We introduce Cro36WSD, a freely-available medium-sized lexical sample for Croatian word sense disambiguation (WSD). Cro36WSD comprises 36 words: 12 adjectives, 12 nouns, and 12 verbs, balanced across both frequency bands and polysemy levels.
Graph-Based Induction of Word Senses in Croatian
Word sense induction (WSI) seeks to induce senses of words from unannotated corpora.
DerivBase.hr: A High-Coverage Derivational Morphology Resource for Croatian
We describe an evaluation methodology based on manually constructed derivational families from which we sample and annotate pairs of lemmas.
HiEve: A Corpus for Extracting Event Hierarchies from News Stories
In this work, we present HiEve, a corpus for recognizing relations of spatiotemporal containment between events.
Evaluation of Classification Algorithms and Features for Collocation Extraction in Croatian
Features which contributed the most to overall performance were PMI, semantic relatedness, and POS information.
