(2011) successfully apply this idea to the transfer of dependency parsers, using part-of-speech tags as the shared representation of words.

This setup requires that we use the same feature representation for both languages, for example part-of-speech tags and dependency relation labels should be from the same inventory.

In this study we will confine ourselves to those features that are applicable to all languages in question, namely: part-of-speech tags , syntactic dependency structures and representations of the word’s identity.

Part-of-speech Tags .

We also assume that the predicate identification information is available — in most languages it can be obtained using a relatively simple heuristic based on part-of-speech tags .

(2011), we assume that a part-of-speech tagger is available for the target language.

Standard methods for part-of-speech tagging suffer from data sparseness when used on highly inflectional languages (which require large lexical tagset inventories).

Several neural network architectures have been proposed for the task of part-of-speech tagging .

We presented a new approach for large tagset part-of-speech tagging using neural networks.

Part-of-speech tagging is a crucial preliminary process in many natural language processing applications.

Because many words in natural languages have more than one part-of-speech tag , resolving part-of-speech ambiguity is an important task.

Previous work (Yatbaz et al., 2012) demonstrates that clustering substitute vectors of all word types alone has limited success in predicting part-of-speech tag of a word.

We use the features of Zhang and Nivre (2011), except that all lexical identities are dropped from the templates during training and testing, hence inducing a ‘delexicalized’ model that employs only ‘universal’ properties from source-side treebanks, such as part-of-speech tags , labels, head-modifier distance, etc.

In the context of part-of-speech tagging , universal representations, such as that of Petrov et al.

For both English and Swedish, we mapped the language-specific part-of-speech tags to universal tags using the mappings of Petrov et al.

To detect noun phrases that potentially refer to entities, we apply a part-of-speech tagger to the input text.

HYENA’s relatively poor performance can be attributed to the fact that its features are mainly syntactic such as bi-grams and part-of-speech tags .

All methods use trained classifiers over a variety of linguistic features, most importantly, words and bigrams with part-of-speech tags in a mention and in the textual context preceding and following the mention.