This paper present an overview of some emerging trends in the application of NLP in the domain of the so-called Digital Humanities and discusses the role and nature of metadata, the annotation layer that is so characteristic of documents that play a role in the scholarly practises of the humanities. It is explained how metadata are the key to the added value of techniques such as text and link mining, and an outline is given of what measures could be taken to increase the chances for a bright future for the old ties between NLP and the humanities. There is no data like metadata! 1

We summarize our fully automatic approach to the TRECVID 2009 Search task. Our submissions summarized in Table 1 use linear combinations of the following basic techniques. • text ASR text retrieved by the Dutch translation of selected topic terms. • image Similarity of representative frames of shots. • face Face detector output for topics involving people. • feature Total weight of high level feature classifiers considered relevant by text based similarity to the topic. We used the publicly available feature predictions. • motion Motion information extracted from videos where relevant to topic. • wide A variation of text with wider shot neighborhood considered relevant. • lattice Text retrieval based on ASR lattices where available. The combination of feature and face together contributed most to the performance of the system. In this experiment the use of lattices, although they were available only for part of the shots, did not improve over the most probable ASR output. The best ASR text based run is text + wide, a combination where more distant shots also receive partial score for a matching speech. We notice that the plain linear combination of all scores deteriorated performance. In the paper we measure independent performance of the methods and observe that feature alone would have outperformed all of our runs. An improved combination includes wide with lower weight. 1