Semi-Supervised Dependency Parsing

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Here, we also mention the predicted POS tagging accuracy.


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The focus of the task is learning syntactic dependency parsers that can work in a real-world setting, starting from raw text, and that can work over many typologically different languages, even low-resource languages for which there is little or no training data, by exploiting a common syntactic annotation standard. Participating systems will have to find labeled syntactic dependencies between words, i.


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  2. Semi-Supervised Dependency Parsing by Wenliang Chen, Min Zhang - ekuzuzywaw.tk.
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In addition to syntactic dependencies, prediction of morphology and lemmatization will be evaluated. There will be multiple test sets in various languages but all data sets will adhere to the common annotation style of UD. Participants will be asked to parse raw text where no gold-standard pre-processing tokenization, lemmas, morphology is available.

The organizers believed that this made the task reasonably accessible for everyone.

An empirical study of semi-supervised structured conditional models for dependency parsing

Cross-lingual zero-shot parsing is the task of inferring the dependency parse of sentences from one language without any labeled training trees for that language. Models are evaluated against the Universal Dependency Treebank v2. For each of the 6 target languages, models can use the trees of all other languages and English and are evaluated by the UAS and LAS on the target.

The final score is the average score across the 6 target languages. The most common evaluation setup is to use gold POS-tags.

Semi-supervised Dependency Parsing

Unsupervised dependency parsing is the task of inferring the dependency parse of sentences without any labeled training data. As with supervised parsing, models are evaluated against the Penn Treebank. The most common evaluation setup is to use gold POS-tags as input and to evaluate systems using the unlabeled attachment score also called 'directed dependency accuracy'.

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Ambiguity-aware ensemble training for semi-supervised dependency parsing (2014)

Branch: master Find file Copy path. Find file Copy path. The evaluations on several English domains and multi-lingual data show quite good improvements on parsing accuracy. Overall this work conducted a survey of semi-supervised methods for out-of-domain dependency parsing, where I extended and compared a number of important semi-supervised methods in a unified framework.

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NLP-progress

The comparison between those techniques shows that self-training works equally well as co-training on out-of-domain parsing, while dependency language models can improve both in- and out-of-domain accuracies. New search Advanced search Search results. Semi-supervised methods for out-of-domain dependency parsing.

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