4d)Describe or draw the correct parse tree for the most natural in.pdf
4d) Describe or draw the correct parse tree for the most natural interpretation of the following English sentence: She took the lesson to heart Question 4 (a) A sentiment analyzer that predicts if a product review is positive, negative or neutral produces the following confusion matrix: Calculate the following: (i) Precision and recall for each class [6] (ii) Micro average precision and macro average precision. Why do they differ? [4] (iii) F measure for each class [6] (b) You are designing a text classifier to identify fraudulent insurance claims and your initial prototype returns an accuracy of 97%. Why might this figure be misleading? Suggest two strategies for dealing with this situation [5] (c) What techniques might you use to deal with typographic errors in search queries? Suggest one method based on distributional techniques and another based on a more rule-based or algorithmic approach. [4] (d) Describe or draw the correct parse tree for the most natural interpretation of the following English sentence: 'She took the lesson to heart'.[5].
4d)Describe or draw the correct parse tree for the most natural in.pdf
4d) Describe or draw the correct parse tree for the most natural interpretation of the following English sentence: She took the lesson to heart Question 4 (a) A sentiment analyzer that predicts if a product review is positive, negative or neutral produces the following confusion matrix: Calculate the following: (i) Precision and recall for each class [6] (ii) Micro average precision and macro average precision. Why do they differ? [4] (iii) F measure for each class [6] (b) You are designing a text classifier to identify fraudulent insurance claims and your initial prototype returns an accuracy of 97%. Why might this figure be misleading? Suggest two strategies for dealing with this situation [5] (c) What techniques might you use to deal with typographic errors in search queries? Suggest one method based on distributional techniques and another based on a more rule-based or algorithmic approach. [4] (d) Describe or draw the correct parse tree for the most natural interpretation of the following English sentence: 'She took the lesson to heart'.[5].
![4d)
Describe or draw the correct parse tree for the most natural interpretation of the following
English sentence: She took the lesson to heart Question 4 (a) A sentiment analyzer that predicts
if a product review is positive, negative or neutral produces the following confusion matrix:
Calculate the following: (i) Precision and recall for each class [6] (ii) Micro average precision
and macro average precision. Why do they differ? [4] (iii) F measure for each class [6] (b) You
are designing a text classifier to identify fraudulent insurance claims and your initial prototype
returns an accuracy of 97%. Why might this figure be misleading? Suggest two strategies for
dealing with this situation [5] (c) What techniques might you use to deal with typographic errors
in search queries? Suggest one method based on distributional techniques and another based on a
more rule-based or algorithmic approach. [4] (d) Describe or draw the correct parse tree for the
most natural interpretation of the following English sentence: 'She took the lesson to heart'.[5]](https://image.slidesharecdn.com/4ddescribeordrawthecorrectparsetreeforthemostnaturalin-230331162742-5c234c7c/75/4ddescribe-or-draw-the-correct-parse-tree-for-the-most-natural-inpdf-1-2048.jpg?cb=1680281047)
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4d)Describe or draw the correct parse tree for the most natural in.pdf
- 1. 4d) Describe or draw the correct parse tree for the most natural interpretation of the following English sentence: She took the lesson to heart Question 4 (a) A sentiment analyzer that predicts if a product review is positive, negative or neutral produces the following confusion matrix: Calculate the following: (i) Precision and recall for each class [6] (ii) Micro average precision and macro average precision. Why do they differ? [4] (iii) F measure for each class [6] (b) You are designing a text classifier to identify fraudulent insurance claims and your initial prototype returns an accuracy of 97%. Why might this figure be misleading? Suggest two strategies for dealing with this situation [5] (c) What techniques might you use to deal with typographic errors in search queries? Suggest one method based on distributional techniques and another based on a more rule-based or algorithmic approach. [4] (d) Describe or draw the correct parse tree for the most natural interpretation of the following English sentence: 'She took the lesson to heart'.[5]