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.
Although text simplification methods have been proposed since more than a couple of years ago @cite_15 , complex word identification has not been studied as a stand-alone task until recently @cite_21 , with the first shared task on CWI organized in 2016 @cite_1 . With some exceptions @cite_11 , most of the related works are actually the system description papers of the 2016 CWI Shared Task participants. Among the top @math participants, the most popular classifier is Random Forest @cite_0 @cite_23 @cite_8 @cite_29 , while the most common type of features are lexical and semantic features @cite_0 @cite_23 @cite_14 @cite_19 @cite_8 . Some works used Naive Bayes @cite_23 or SVM @cite_29 along with the Random Forest classifier, while others used different classification methods altogether, e.g. Decision Trees @cite_19 , Nearest Centroid @cite_22 or Maximum Entropy @cite_7 . Along with the lexical and semantic features, many have used morphological @cite_23 @cite_14 @cite_22 @cite_8 and syntactic @cite_23 @cite_19 @cite_8 features.
@cite_14 @cite_22 @cite_7 @cite_8 @cite_29 @cite_21 @cite_1 @cite_0 @cite_19 @cite_23 @cite_15 @cite_11
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Comunicacio presentada al 10th International Workshop on Semantic Evaluation (SemEval 2016), celebrat els dies 16 i 17 de juny de 2016 a San Diego, EUA. Identifying complex words (CWs) is an important, yet often overlooked, task within lexical simplification (The process of automatically replacing CWs with simpler alternatives). If too many words are identified then substitutions may be made erroneously, leading to a loss of meaning. If too few words are identified then those which impede a user’s understanding may be missed, resulting in a complex final text. This paper addresses the task of evaluating different methods for CW identification. A corpus of sentences with annotated CWs is mined from Simple Wikipedia edit histories, which is then used as the basis for several experiments. Firstly, the corpus design is explained and the results of the validation experiments using human judges are reported. Experiments are carried out into the CW identification techniques of: simplifying everything, frequency thresholding and training a support vector machine. These are based upon previous approaches to the task and show that thresholding does not perform significantly differently to the more naive technique of simplifying everything. The support vector machine achieves a slight increase in precision over the other two methods, but at the cost of a dramatic trade off in recall. SemEval 2016 task 11 involved determining whether words in a sentence were complex or simple for a cohort of people with English as a second language. Training data consisted of 200 annotated sentences, representing the combined judgements of 20 human annota- tors, such that if any annotator of the group labelled a word as complex, then it was con- sidered to be complex. Testing was based on single annotator judgements. Our system used a random forest classifier with a variety of features, the most important of which were term frequency statistics garnered from four large corpora, and style lexicons built on two large corpora. Minor features in the final system include the presence or absence of words in various readability word lists; many other features we tried were not successful. Our rank- ing amongst submitted systems did not reflect the strength of our system, due to submitting a far from optimal weighting between complex and simple, but we show that when a more ap- propriate weighting is used, our system ranks amongst the best submitted systems. We present two systems created for SemEval2016s Task 11: Complex Word Identification. Our two systems, a regression tree and decision tree, were trained with a word’s unigram and lemma word counts, average ageof-acquisition, and a measure of concreteness. The systems ranked 5th and 6th, respectively, on the test set by G-score (the harmonic mean between accuracy and recall). With the regression tree’s predictions earning a G-score of 0.766, and the decision tree’s earning 0.765, the two systems scored within 1 percent of the score of the best-performing system in the task. The complex word identification task refers to the process of identifying difficult words in a sentence from the perspective of readers belonging to a specific target audience. This task has immense importance in the field of lexical simplification. Lexical simplification helps in improving the readability of texts consisting of challenging words. As a participant of the SemEval-2016: Task 11 shared task, we developed two systems using various lexical and semantic features to identify complex words, one using Naive Bayes and another based on Random Forest Classifiers. The Naive Bayes classifier based system achieves the maximum G-score of 76.7 after incorporating rule based post-processing techniques. This paper evaluates the effect of the context on the identification of complex words in natural language texts. The approach automatically tags words as either complex or not, based on two sets of features: base features that only pertain to the target word, and contextual features that take the context of the target word into account. We experimented with several supervised machine learning models, and trained and tested the approach with the SemEval-2016 dataset. Results show that considering contextual features significantly improves the identification of complex words by reaching an F-measure of 0.260 compared to 0.184 without them.