|Country of origin?||
|For how many years have you been experimenting with data?||
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Thank you very much for your questions.
Regarding questions 2 and 3:
2) We have allowed overlaps among dictionaries, yet each dictionary has its own weight for a given keyword based on the term frequency. So, even if a keyword found in a given sentence is included in multiple dictionaries, its contribution to each label types` score will be unique (with high probability).
3) We have assumed it is used over the whole sentence. Our initial explorations for trying to detect the fragment boundaries showed that this simplification gives the best results with this approach. Yet, we believe that a similar approach for learning the frequently occurring words in the fragment boundaries for each label type can improve the results.
Please let me know if you have any other questions regarding task 3 🙂