40,302 research outputs found
Portuguese Native Language Identification
This study presents the first Native Language Identification (NLI) study for L2 Portuguese.We used a sub-set of the NLI-PT dataset, containing texts written by speakers of five different native languages: Chinese, English, German, Italian, and Spanish.We explore the linguistic annotations available in NLI-PT to extract a range of (morpho-)syntactic features and apply NLI classification methods to predict the native language of the authors. The best results were obtained using an ensemble combination of the features, achieving 54:1% accuracy.info:eu-repo/semantics/publishedVersio
Native Language Identification on Text and Speech
This paper presents an ensemble system combining the output of multiple SVM
classifiers to native language identification (NLI). The system was submitted
to the NLI Shared Task 2017 fusion track which featured students essays and
spoken responses in form of audio transcriptions and iVectors by non-native
English speakers of eleven native languages. Our system competed in the
challenge under the team name ZCD and was based on an ensemble of SVM
classifiers trained on character n-grams achieving 83.58% accuracy and ranking
3rd in the shared task.Comment: Proceedings of the Workshop on Innovative Use of NLP for Building
Educational Applications (BEA
Complex Word Identification: Challenges in Data Annotation and System Performance
This paper revisits the problem of complex word identification (CWI)
following up the SemEval CWI shared task. We use ensemble classifiers to
investigate how well computational methods can discriminate between complex and
non-complex words. Furthermore, we analyze the classification performance to
understand what makes lexical complexity challenging. Our findings show that
most systems performed poorly on the SemEval CWI dataset, and one of the
reasons for that is the way in which human annotation was performed.Comment: Proceedings of the 4th Workshop on NLP Techniques for Educational
Applications (NLPTEA 2017
Detecting Hate Speech in Social Media
In this paper we examine methods to detect hate speech in social media, while
distinguishing this from general profanity. We aim to establish lexical
baselines for this task by applying supervised classification methods using a
recently released dataset annotated for this purpose. As features, our system
uses character n-grams, word n-grams and word skip-grams. We obtain results of
78% accuracy in identifying posts across three classes. Results demonstrate
that the main challenge lies in discriminating profanity and hate speech from
each other. A number of directions for future work are discussed.Comment: Proceedings of Recent Advances in Natural Language Processing
(RANLP). pp. 467-472. Varna, Bulgari
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