In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

Q: To evaluate real-world performance of our selected classifier (i.e., performance in the absence of model and parameter bias), we perform classification of the holdout set. On this set, our classifier had an accuracy and F1-score of 89.6% and 89.2%, respectively.
Question: What was their system's performance?

A: accuracy and F1-score of 89.6% and 89.2%, respectively
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Q: Maximum Entropy theory is applied to solve Vietnamese word segmentation BIBREF15 , BIBREF22 , BIBREF23 . There are several studies about the Vietnamese word segmentation task over the last decade. Dinh et al. started this task with Weighted Finite State Transducer (WFST) approach and Neural Network approach BIBREF9 . In addition, machine learning approaches are studied and widely applied to natural language processing and word segmentation as well. In fact, several studies used support vector machines (SVM) and conditional random fields (CRF) for the word segmentation task BIBREF7 , BIBREF8 .
Question: Which approaches have been applied to solve word segmentation in Vietnamese?

A: Maximum Entropy Weighted Finite State Transducer (WFST) support vector machines (SVM) conditional random fields (CRF)
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Q: It has been shown that one can significantly increase the semantic information carried by a NER system when we successfully linking entities from a deep learning method to the related entities from a knowledge base BIBREF26 , BIBREF27 . Redirection: For the Wikidata linking element, we recognize that the lookup will be constrained by the most common lookup name for each entity.
Question: How do they combine a deep learning model with a knowledge base?

A:
Entities from a deep learning model are linked to the related entities from a knowledge base by a lookup.
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