NILC at CWI 2018: Exploring Feature Engineering and Feature Learning

WS 2018 Nathan HartmannLe dos Santosro Borges

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 network method using only word embeddings, and (iii) a Long Short-Term Memory (LSTM) language model, which is pre-trained on a large text corpus to produce a contextualized word vector... (read more)

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