Complex Word Identification: Convolutional Neural Network vs. Feature Engineering
We describe the systems of NLP-CIC team that participated in the Complex Word Identification (CWI) 2018 shared task. The shared task aimed to benchmark approaches for identifying complex words in English and other languages from the perspective of non-native speakers. Our goal is to compare two approaches: feature engineering and a deep neural network. Both approaches achieved comparable performance on the English test set. We demonstrated the flexibility of the deep-learning approach by using the same deep neural network setup in the Spanish track. Our systems achieved competitive results: all our systems were within 0.01 of the system with the best macro-F1 score on the test sets except on Wikipedia test set, on which our best system is 0.04 below the best macro-F1 score.
Code (0)
등록된 구현이 없습니다.
Tasks
Complex Word IdentificationFeature EngineeringText SimplificationSimilar Papers 제목 키워드 기반
Multilingual Complex Word Identification: Convolutional Neural Networks with Morphological and Linguistic Features
The paper is about our experiments with Complex Word Identification system using deep learning approach with word embeddings and engineered features.
Complex Word IdentificationDeep LearningWord EmbeddingsComplex Word Identification as a Sequence Labelling Task
Complex Word Identification (CWI) is concerned with detection of words in need of simplification and is a crucial first step in a simplification pipeline. It has been shown that reliable CWI systems considerably improve …
Complex Word IdentificationFeature EngineeringText SimplificationNILC at CWI 2018: Exploring Feature Engineering and Feature Learning
This paper describes the results of NILC team at CWI 2018. We developed solutions following three approaches: (i) a feature engineering method using lexical, n-gram and psycholinguistic features, (ii) a shallow neural ne…
Complex Word IdentificationFeature EngineeringGeneral ClassificationLanguage Modeling+4Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification
We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale. Both models utilize word and character embeddings alongside lexical features as inputs. Ou…
Complex Word IdentificationregressionEvent Argument Identification on Dependency Graphs with Bidirectional LSTMs
In this paper we investigate the performance of event argument identification. We show that the performance is tied to syntactic complexity. Based on this finding, we propose a novel and effective system for event argume…
Feature Engineering