Comunicació presentada a la 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), organitzada pel SIGDAT i celebrada els dies 17 a 21 de setembre 2015 a Lisboa (Portugal).

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.

MB was supported by the European Commission under the contract numbers FP7-ICT-610411 (project MULTISENSOR) and H2020-RIA-645012 (project KRISTINA). This research was supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-10-1-0533 and NSF IIS-1054319. This work was completed while NAS was at CMU.