Abstract
We present extensions to a continuousstate dependency parsing method that makes it applicable to morphologically rich languages. Starting with a highperformance transition-based parser that uses long short-term memory (LSTM) recurrent neural networks to learn representations of the parser state, we replace lookup-based word representations with representations constructed from the orthographic representations of the words, also using LSTMs. This allows statistical sharing across word forms that are similar on the surface. Experiments for morphologically rich languages show that the parsing model benefits from incorporating the character-based encodings of words.
Keywords
Computer scienceParsingNatural language processingArtificial intelligenceTransition (genetics)Word (group theory)Character (mathematics)Dependency grammarDependency (UML)Recurrent neural networkArtificial neural networkLinguistics
Affiliated Institutions
Related Publications
Finding Structure in Time
Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implici...
Feature-rich part-of-speech tagging with a cyclic dependency network
We present a new part-of-speech tagger that demonstrates the following ideas: (i) explicit use of both preceding and following tag contexts via a dependency network representati...
Learning the hidden structure of speech
In the work described here, the backpropagation neural network learning procedure is applied to the analysis and recognition of speech. This procedure takes a set of input/outpu...
Attention Is All You Need
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also co...
Glove: Global Vectors for Word Representation
Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the o...
Publication Info
- Year
- 2015
- Type
- article
- Pages
- 349-359
- Citations
- 244
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
244
OpenAlex
Cite This
Miguel Ballesteros,
Chris Dyer,
Noah A. Smith
(2015).
Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs.
, 349-359.
https://doi.org/10.18653/v1/d15-1041
Identifiers
- DOI
- 10.18653/v1/d15-1041