Specifically, you are given a corpus of news articles in which all tokens have been labeled as either belonging to personal name mentions or not.

Clearly the problems of identifying names in news articles and e-mails are closely related, and learning to do well on one should help your performance on the other.

When only the type of data being examined is allowed to vary (from news articles to e-mails, for example), the problem is called domain adaptation (Daumé III and Marcu, 2006).

These are: abstracts from biological journals [UT (Bunescu et al., 2004), Yapex (Franzen et al., 2002)]; news articles [MUC6 (Fisher et al., 1995), MUC7 (Borthwick et al., 1998)]; and personal e-mails [CSPACE (Kraut et al., 2004)].

0 person names in news articles and e-mails We chose this array of corpora so that we could evaluate our hierarchical prior’s ability to generalize across and incorporate information from a variety of domains, genres and tasks.

Figure 3 shows the results of an experiment in learning to recognize person names in MUC6 news articles .

We create a database of pictures that are naturally embedded into news articles and propose to use their captions as a proxy for annotation keywords.

We also demonstrate that the news article associated with the picture can be used to boost image annotation performance.

Many online news providers supply pictures with news articles , some even classify news into broad topic categories (e.g., business, world, sports, entertainment).

We downloaded 3,361 news articles from the BBC News website.2 Each article was accompanied with an image and its caption.

News articles associated with images and their captions spring readily to mind (e.g., BBC News, Yahoo News).

Importantly, our images are not standalone, they come with news articles whose content is shared with the image.

For example, news articles often contain images whose captions can be thought of as annotations.