UnibucKernel: A kernel-based learning method for complex word identification
In this paper, we present a kernel-based learning approach for the 2018 Complex Word Identification (CWI) Shared Task. Our approach is based on combining multiple low-level features, such as character n-grams, with high-level semantic features that are either automatically learned using word embeddings or extracted from a lexical knowledge base, namely WordNet. After feature extraction, we employ a kernel method for the learning phase. The feature matrix is first transformed into a normalized kernel matrix. For the binary classification task (simple versus complex), we employ Support Vector Machines. For the regression task, in which we have to predict the complexity level of a word (a word is more complex if it is labeled as complex by more annotators), we employ v-Support Vector Regression. We applied our approach only on the three English data sets containing documents from Wikipedia, WikiNews and News domains. Our best result during the competition was the third place on the English Wikipedia data set. However, in this paper, we also report better post-competition results.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationComplex Word IdentificationregressionWord EmbeddingsSimilar Papers 제목 키워드 기반
UnibucKernel: An Approach for Arabic Dialect Identification Based on Multiple String Kernels
The most common approach in text mining classification tasks is to rely on features like words, part-of-speech tags, stems, or some other high-level linguistic features. Unlike the common approach, we present a method th…
Dialect IdentificationText CategorizationUnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row
We present a machine learning approach that ranked on the first place in the Arabic Dialect Identification (ADI) Closed Shared Tasks of the 2018 VarDial Evaluation Campaign. The proposed approach combines several kernels…
Dialect IdentificationCan string kernels pass the test of time in Native Language Identification?
We describe a machine learning approach for the 2017 shared task on Native Language Identification (NLI). The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based…
Language IdentificationNative Language IdentificationCombining Deep Learning and String Kernels for the Localization of Swiss German Tweets
In this work, we introduce the methods proposed by the UnibucKernel team in solving the Social Media Variety Geolocation task featured in the 2020 VarDial Evaluation Campaign. We address only the second subtask, which ta…
Dialect IdentificationregressionUnibucKernel: Geolocating Swiss German Jodels Using Ensemble Learning
In this work, we describe our approach addressing the Social Media Variety Geolocation task featured in the 2021 VarDial Evaluation Campaign. We focus on the second subtask, which is based on a data set formed of approxi…
Dialect IdentificationEnsemble Learningregression