What question did this study set out to answer?

This research aims to enhance dependency parsing performance in morphologically rich languages using character-based models.

January 1, 2015Open Access

Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial demonstrates enhanced parsing performance in morphologically rich languages, suggesting character-level modeling is beneficial.

This research aims to enhance dependency parsing performance in morphologically rich languages using character-based models.
Developed a transition-based parser using LSTM networks to model character representations.
Replaced word representations with character-based encodings to enable statistical sharing across similar word forms.
Conducted experiments on various morphologically rich languages to evaluate performance improvements.
Character-based parsing model showed significant improvements in parsing accuracy for morphologically rich languages.
The use of character-level encoding resulted in better generalization for similar word forms.
Specific performance metrics indicate enhanced efficiency in parsing tasks compared to traditional word-based methods.

Ballesteros et al. (2015) studied this question.

View Full Paper

Ask AI

Bookmark

Randomized trial demonstrates enhanced parsing performance in morphologically rich languages, suggesting character-level modeling is beneficial.