1. TDT 2002 Straw Man TDT 2001 Workshop November 12-13, 2001

2. Corpus  TDT-4 English and Mandarin required Arabic optional, but encouraged Provided in TREC-friendly format Made substantially cheaper  Topics 60 new topics, as before “Brief” redefined to be “more useful”  Stand-off notation standard So sites can provide useful annotations

3. Tasks  Drop tracking Transition to TREC filtering Entry task if TREC does not pick it up  Keep segmentation (sunset?)  Focus on FSD and SLD  Exploratory evaluations on clustering  New event-based evaluation

4. Changes to “brief”  Currently “brief” is “less than 10% is on topic” LDC does this strictly So 10.5% is “YES”!  Prefer notion of whether topic is central to the story or not If central topic, then YES If mentioned in passing, then BRIEF Requires a SHARED-CENTRAL possibility?  This idea requires rethinking

5. Standoff anotation  TDT-2 and TDT-3 distributed with: ASR into text SYSTRAN into English Named entity tagging  Standardize a means for sites to provide other annotations: POS or parsings Co-references for named entities Time expressions with normalization Alternate translations Subject-like headings à la BBN’s tags …

6. Clustering evaluations  Few people happy with clustering measures  Many people unhappy with central idea of clustering Partitioning of corpus (single topic stories) No hierarchies permitted in results  Allow exploration of new models for clustering Perhaps inspired by IFE Bio and IFE Arabic?  Both systems have UMass detection running  Or new problems based on clustering Linking clusters, describing their substructures, …

7. Event-based evaluation  Most (not all) TDT approaches would work just as well for IR filtering or event IR document retrieval  Force exploration of TDT-specific needs  Topic is made of events Seminal events and inevitable ones  Event is something that happens somewhere at a particular time Who, where, when, what  Explicitly capture components of events?

8. Event-based straw man  Based on link detection  Given two stories: Is the perpetrator (“who”) the same? Do they describe events that take place at the same location? …at the same time?  Idea: If two stories talk about events at the same time, they’re more likely to be talking about the same event (obviously more than time needed)