6533b829fe1ef96bd1289964
RESEARCH PRODUCT
Multi-class Text Complexity Evaluation via Deep Neural Networks
Giovanni PilatoAlfredo CuzzocreaDaniele SchicchiGiosuè Lo Boscosubject
050101 languages & linguisticsSettore INF/01 - InformaticaArtificial neural networkText simplificationbusiness.industryComputer science05 social sciencesText simplification02 engineering and technologyDeep neural networkMachine learningcomputer.software_genreClass (biology)Task (project management)Simple (abstract algebra)Automatic Text Complexity Evaluation0202 electrical engineering electronic engineering information engineeringDeep neural networks020201 artificial intelligence & image processing0501 psychology and cognitive sciencesArtificial intelligencebusinesscomputerScope (computer science)description
Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results show that a higher detail level of the classification makes the ATE problem much harder to resolve, showing the weaknesses of the model to accomplish the task correctly.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 |