UParse: the Edinburgh system for the CoNLL 2017 UD shared task
نویسندگان
چکیده
This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for lowresource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants, our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 treebanks).
منابع مشابه
Explorer UParse : the Edinburgh system for the CoNLL 2017 UD shared task
This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for lowresource treebanks and surprise languages, we train several multilingual mode...
متن کاملCoNLL ’ 17 : UD Shared Task
This paper describes LIMSI’s submission to the CoNLL 2017 UD Shared Task, which is focused on small treebanks, and how to improve low-resourced parsing only by ad hoc combination of multiple views and resources. We present our approach for low-resourced parsing, together with a detailed analysis of the results for each test treebank. We also report extensive analysis experiments on model select...
متن کاملUniversal Joint Morph-Syntactic Processing: The Open University of Israel's Submission to The CoNLL 2017 Shared Task
We present the Open University’s submission (ID OpenU-NLP-Lab) to the CoNLL 2017 UD Shared Task on multilingual parsing from raw text to Universal Dependencies. The core of our system is a joint morphological disambiguator and syntactic parser which accepts morphologically analyzed surface tokens as input and returns morphologically disambiguated dependency trees as output. Our parser requires ...
متن کاملA Semi-universal Pipelined Approach to the CoNLL 2017 UD Shared Task
This paper presents the TRL team’s system submitted for the CoNLL 2017 Shared Task, “Multilingual Parsing from Raw Text to Universal Dependencies.” We ran the system for all languages with our own fully pipelined components without relying on either pre-trained baseline or machine learning techniques. We used only the universal part-of-speech tags and distance between words, and applied determi...
متن کاملIMS at the CoNLL 2017 UD Shared Task: CRFs and Perceptrons Meet Neural Networks
This paper presents the IMS contribution to the CoNLL 2017 Shared Task. In the preprocessing step we employed a CRF POS/morphological tagger and a neural tagger predicting supertags. On some languages, we also applied word segmentation with the CRF tagger and sentence segmentation with a perceptron-based parser. For parsing we took an ensemble approach by blending multiple instances of three pa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2017