You have been given the following statements that can be considered to be the training data for the CRF model.

Sentences:

S$1$: $$Bella is not giving money$$

S$2$: $$Tony is teaching NLP to Chris$$

S$3$: $$Joe should focus to his job$$

S$4$: $$Peter is trained by Adele$$

S$5$: $$Temperature is maintained by AC$$

S$6$: $$He is playing the Cricket$$

S$7$: $$Guitar has been played by him$$

Labels:

S$1$: $$Bella $($P$)$ is not giving $($A$)$ money $($E$)$

S$2$: $$Tony $($P$)$ is teaching $($A$)$ NLP $($E$)$ to Chris $($P$)$

S$3$: $$Joe $($P$)$ should focus $($A$)$ to his job $($E$)$

S$4$: $$Peter $($P$)$ is trained $($A$)$ by Adele $($P$)$

S$5$: $$Temperature $($E$)$ is maintained $($A$)$ by AC $($E$)$

S$6$: $$He is playing $($A$)$ the Cricket $($E$)$

S$7$: $$Guitar $($E$)$ has been played $($A$)$ by him$($V$)$

Here, in this training data, the three labels $$P$,$A$$ and $$E$$ stand for Person, Activity and Entity, respectively.

Three feature functions have been defined below:

F$1($X$,$ xi$,$ xi$-1,$ i$)=1$ if xi starts with a capital letter; otherwise, $0.$

F$2($X$,$ xi$,$ xi$-1,$ i$)=1$ if xi is the parent of the $$nsubj$$ or $$nsubjpass$$ dependency tagged words AND the corresponding children words should have the NER tag of $$E$$ or $$P$$; otherwise, $0.$

F$3($X$,$ xi$,$ xi$-1,$ i$)=1,$ if xi is the parent of the $$dobj$$ dependency tagged words AND the corresponding children words should have the NER tag of $$E$$ or $$P$$; otherwise, $0.$

You have been given the following sentence with its NER tags.

Sentence: $$Harry is not gardening as it is raining$$

Labels: $$Harry $($P$)$ is not gardening$($A$)$ as it is raining $($A$)$

What will be the value of the feature functions F$1,$ F$2$ and F$3,$ respectively, for the word $$Harry$?$

a$.0,0,0$

b$.1,0,0$

c$.1,1,1$

d$.0,1,0$