Since we focus on the domain adaptation issue, we incorporate a less domain dependent language resource (i.e.

The same dataset has been used for the domain adaptation track of the CoNLL 2007 Shared Task.

This is the other datasets used in the domain adaptation track of the CoNLL 2007 Shared Task.

It should be noted that domain adaptation also presents a challenge to the disambiguation model of the HP SG parser.

In addition, most of the previous work have been focusing on constituent-based parsing, while the domain adaptation of the dependency parsing has not been fully explored.

This not to say that domain adaptation is

One particularly promising area for further study is the combination of smoothing and instance weighting techniques for domain adaptation .

3.3 Domain Adaptation

For our experiment on domain adaptation , we focus on NP chunking and POS tagging, and we use the labeled training data from the CoNLL 2000 shared task as before.

(2006): the semi-supervised Alternating Structural Optimization (ASO) technique and the Structural Correspondence Learning (SCL) technique for domain adaptation .

One of the benefits of our smoothing technique is that it allows for domain adaptation , a topic that has received a great deal of attention from the NLP community recently.

HMM-smoothing improves on the most closely related work, the Structural Correspondence Learning technique for domain adaptation (Blitzer et al., 2006), in experiments.

Inspired by recent work on transfer learning and domain adaptation , in this paper, we study how we can leverage labeled data of some old relation types to help the extraction of a new relation type in a weakly-supervised setting, where only a few seed instances of the new relation type are available.

Domain adaptation is a special case of transfer learning where the leam-ing task remains the same but the distribution

There has been an increasing amount of work on transfer learning and domain adaptation in natural language processing recently.

(2006) proposed a structural correspondence learning method for domain adaptation and applied it to part-of-speech tagging.

ald, 2008), and is also similar to the Pred baseline for domain adaptation in (Daumé III and Marcu, 2006; Daumé III, 2007).

We are especially grateful to Fernando Pereira and the anonymous reviewers for pointing us to relevant domain adaption references.

The second problem, domain adaptation , is very well-studied, e.g.

This method is very similar to some ideas in domain adaptation (Daume III and Marcu, 2006; Daume III, 2007), but we argue that the underlying problems are quite different.

Domain adaptation assumes the labeling guidelines are preserved between the two domains, e.g., an adjective is always labeled as JJ regardless of from Wall Street Journal (WSJ) or Biomedical texts, and only the distributions are different, e. g., the word “control” is most likely a verb in WSJ but often a noun in Biomedical texts (as in “control experiment”).

Recently there have been some works on using multiple treebanks for domain adaptation of parsers, where these treebanks have the same grammar formalism (McClosky et al., 2006b; Roark and Bacchiani, 2003).

Recently there have been some studies addressing how to use treebanks with same grammar formalism for domain adaptation of parsers.

Roark and Bachiani (2003) presented count merging and model interpolation techniques for domain adaptation of parsers.

Their results indicated that both unlabeled in-domain data and labeled out-of-domain data can help domain adaptation .