By Wenliang Chen, Min Zhang
This booklet offers a accomplished assessment of semi-supervised methods to dependency parsing. Having turn into more and more well known in recent times, one of many major purposes for his or her luck is they could make use of enormous unlabeled info including rather small categorised info and feature proven their merits within the context of dependency parsing for lots of languages. a variety of semi-supervised dependency parsing methods were proposed in fresh works which make the most of sorts of info gleaned from unlabeled facts. The e-book bargains readers a entire creation to those methods, making it supreme as a textbook for complicated undergraduate and graduate scholars and researchers within the fields of syntactic parsing and average language processing.
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Extra resources for Semi-Supervised Dependency Parsing
We first briefly introduce the self-training and co-training approaches and then introduce the approach of ambiguity-aware ensemble training in details. The conventional approaches of the whole-tree level pick up some high-quality auto-parsed training instances from unlabeled data using bootstrapping methods, such as self-training (Yarowsky 1995), co-training (Blum and Mitchell 1998), and tri-training (Zhou and Li 2005). However, these methods gain limited success in dependency parsing. Although working well on constituent parsing (Huang and Harper 2009; McClosky et al.
Instead of using entire trees, several researchers exploit lexical information, such as word clusters and word cooccurrences (Koo et al. 2008; Zhou et al. 2011). The lexical information is easy to be used in parsing models, but it ignores the dependency relations among words which might be useful. The use of bilexical dependencies is attempted in van Noord (2007) and Chen et al. (2008). However, the bilexical dependencies provide a relatively poor level of useful information for parsing. To provide richer information, we can consider more words, such as subtrees (Chen et al.
This kind of syntactic divergence is helpful because it can provide complementary knowledge from a different perspective. Surdeanu and Manning (2010) also show that the diversity of parsers is important for performance improvement when integrating different parsers in the supervised track. Therefore, we can conclude that co-training helps dependency parsing, especially when using a more divergent parser. The last experiment in the second major row is known as tri-training, which only uses unlabeled sentences on which Berkeley Parser and ZPar produce identical outputs (“Parse B=Z”).