Question

Compare the given TrigramTagger from the previous questions with a TrigramTagger where no backoff is provided. Train this tagger on all of the sentences from the Brown corpus with the category news. Then evaluate your tagger using "accuracy" function on all of the sentences from the Brown corpus with the category lore. What are your numbers? Which tagger performs better? Why?

a. The trained trigram tagger from this question evaluated on lore category shows much higher accuracy than the trigram tagger from previous question evaluated on lore
b. The trained trigram tagger from this question evaluated on lore category shows insignificantly higher accuracy than the trigram tagger from previous question evaluated on lore
c. The trained trigram tagger from this question evaluated on lore category shows the same accuracy than the trigram tagger from previous question evaluated on lore
d. The trained trigram tagger from this question evaluated on lore category shows insignificantly lower accuracy than the trigram tagger from previous question evaluated on lore
e. The trained trigram tagger from this question evaluated on lore category shows much lower accuracy than the trigram tagger from previous question evaluated on lore

Answer 1

The TrigramTagger without backoff when trained on 'news' and evaluated on 'lore' is expected to have much lower accuracy than the TrigramTagger with backoff, due to the inability to handle new contexts and vocabulary.

Step-by-step explanation:

When comparing the performance of a TrigramTagger with a backoff tagger to a TrigramTagger without a backoff tagger, you typically expect the one with backoff to perform better. This is because a backoff tagger will use a more general tagger (like a bigram or unigram tagger) when it can't find a context in the trigram tagger. The tagger trained on the 'news' category from the Brown corpus and evaluated on the 'lore' category is expected to show lower accuracy without backoff, since 'lore' contains different contexts and vocabulary, and the tagger won't have a fallback strategy.

In this hypothetical evaluation, had it been conducted, the accuracy of the TrigramTagger without backoff would likely be lower when compared to the TrigramTagger with backoff. This is because the TrigramTagger without backoff would struggle to tag words that do not appear in the same context in the training set (from the news category), especially since 'lore' consists of a different style of language and topics.

Therefore, we would generally expect the answer to be 'e. The trained trigram tagger from this question evaluated on lore category shows much lower accuracy than the trigram tagger from previous question evaluated on lore' given the usual expectations of a tagger's performance without backoff capability.