Ali Hürriyetoğlu (@hurrial) MSc
The objective of ADNEXT (ADaptive informatioN EXtraction over Time) is to develop trainable, adaptable Dutch language information extraction technology for named entity recognition, event detection, and time identification. The technology has a broad coverage “default” mode and retrains dynamically to new domains upon being confronted with new (clusters of) news or user-generated data (such as Twitter).
Dreams, the involuntary perceptions that occur in our minds during sleep, have been the topic of studies in many fields of research, including psychiatry, psychology, neurobiology, and religious studies. Their narrative content also links dreams to other forms of storytelling, with sharp distinctions (such as the focus on one's personal life and the typical personal perspective) but also interesting overlaps with genres such as orally transmitted folktales. We present a study on dreams aimed at the large-scale analysis of dreams using text analytics.
Lama Events is a calendar application listing events in the near future. The events are detected and selected by a fully automatic procedure in the Dutch Twitter stream (courtesy of Twiqs.nl). Tweets referring to the same future events are clustered based on the frequent co-occurrence of words (names, phrases) and temporal expressions that characterize the event. The date and time of the event is automatically determined based on direct and indirect time references in the texts of the tweets in a cluster. The demo shows a day-by-day ranked list of automatically detected events in the Dutch language area (Netherlands and Flanders).