Jochen L. Leidner

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In Information Extraction (IE), processing of named entities in text has traditionally been seen as a two-step process comprising a flat text span recognition sub-task and an atomic classification sub-task; relating the text span to a model of the world has been ignored by evaluations such as DARPA/NIST's MUC or ACE. However, spatial and temporal(More)
Currently there is a lot of interest in improving information access by providing systems capable of answering questions in natural language. It is caused by a combination of (a) the availability of large amounts of textual data in electronic form, (b) the affordability of fast, networked personal computers , (c) the success and fast growth of the World(More)
The task of named entity annotation of unseen text has recently been successfully automated with near-human performance. But the full task involves more than annotation, i.e. identifying the scope of each (continuous) text span and its class (such as place name). It also involves grounding the named entity (i.e. establishing its denotation with respect to(More)
Recognizing spatial language in text documents, termed <i>geoparsing</i>, is useful for many applications, because together with mapping such language to lat/long values, also known as <i>geocoding</i>, it enables the connection of the unstructured textual realm with the structured realm of <i>Geographic Information Systems (GIS)</i> [11]. For example, news(More)
In TAC 2008 we participated in the main task (Update Summarization) as well as the Sentiment Summarization pilot task. We modified the FastSum system (Schilder and Kondadadi, 2008) and added more aggressive filtering in order to adapt the system to update summarization and sentiment summarization. For the Update Summarization task, we show that a classifier(More)