Some examples and use cases from the internet:

Some examples and use cases from the internet:

• We assume that there is no unknown words in the disambiguition model. If all word candidates can be found in the dictionary, we just need to solve the ...
• When there is no unknown words in the tested sentences, all the above approaches perform quite well, with accuracy rates ranging from 95% to 99%.
• means that there is no unknown words have been detected in corpus 2. Next is by evaluating the precision, recall and f
• ... relying on a dictionary. What you should do is brush up on your Russian until there is no "unknown words" left to be able to do great translations of anything.
• measure for each main tag sets in the ...

• baseline model when there are unknown words. When there are no unknown words, the performance is about 99% for words and 97% for sentence. 22 ...
• ties as the solution without interacting with ...
• One clear example of a significant issue being sidestepped here is unknown words. There are no unknown words when you test on the training set, and the truth ...
• methods assume that there are no unknown words, and automatically choose a segmentation among the possibili
• to be quite satisfactory [Chang 91, Chiang 92] when there are no unknown words in the corpus. Therefore, a probabilistic approach is adopted in this module.