UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification
نویسنده
چکیده
In this paper, we present our system developed for the SemEval 2016 Task 11: Complex Word Identification. Our team achieved the 3rd place among 21 participants. Our systems ranked 4th and 13th among 42 submitted systems. We proposed multiple features suitable for complex word identification, evaluated them, and discussed their properties. According to the results of our experiments, our final system used maximum entropy classifier with a single feature – document frequency.
منابع مشابه
USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity
This paper describes an information-theoretic approach to complex word identification using a classifier based on an entropy based measure based on word senses and sentence-level perplexity features. We describe the motivation behind these features based on information density and demonstrate that they perform modestly well in the complex word identification task in SemEval-2016. We also discus...
متن کاملCLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification
This paper describes the system deployed by the CLaC-EDLK team to the SemEval 2016, Complex Word Identification task. The goal of the task is to identify if a given word in a given context is simple or complex. Our system relies on linguistic features and cognitive complexity. We used several supervised models, however the Random Forest model outperformed the others. Overall our best configurat...
متن کاملIIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification
This paper describes the system that was submitted to SemEval2016 Task 11: Complex Word Identification. It presents a preliminary investigation into exploring word difficulty for non-native English speakers. We developed two systems using Nearest Centroid Classification technique to distinguish complex words from simple words. Optimized over G-score, the presented solution obtained a G-score of...
متن کاملLTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles
We present the description of the LTG entry in the SemEval-2016 Complex Word Identification (CWI) task, which aimed to develop systems for identifying complex words in English sentences. Our entry focused on the use of contextual language model features and the application of ensemble classification methods. Both of our systems achieved good performance, ranking in 2nd and 3rd place overall in ...
متن کاملMAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier
This paper describes team MAZA entries for the 2016 SemEval Task 11: Complex Word Identification (CWI). The task is a binary classification task in which systems are trained to predict whether a word in a sentence is considered to be complex or not. We developed our two systems for this task based on classifier stacking using decision stumps and decision trees. Our best system, using contextual...
متن کامل