# Q5a · Feature engineering, BoW and Euclidean

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/papers/legacy-f24/q5a

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q5a/

Previous examination · complete worked answers

**Question. Explain text feature engineering and list vectorization approaches. For the three sentences below remove stopwords {is,that,are,to,and}, create vocabulary and BoW, then compute Euclidean distance between sentences1and2: “Natural Language Processing is fascinating”; “I love Natural Language processing and like to study Natural Language”; “I like things that are fascinating”.** [10 marks]

### Answer

Feature engineering converts text into informative model inputs. Common approaches: one-hot, BoW, bag of n-grams, TF–IDF and dense embeddings. This question specifically requests count-based BoW, so do not use TF–IDF in its distance.

### Given

Use lowercase lexical tokens and the exact printed stoplist; I remains because it is not a stopword here.

### To find

Vocabulary, count matrix and distance D1–D2.

### Formula: which one and why



$$
d_E(u,v)=\sqrt{\sum_i(u_i-v_i)^2}
$$



### Step 1. Tokenize and remove the exact stoplist

| Document | Retained tokens |
| --- | --- |
| 1 | natural, language, processing, fascinating |
| 2 | i, love, natural, language, processing, like, study, natural, language |
| 3 | i, like, things, fascinating |

### Step 2. Fix vocabulary and count repeats

| Document | fascinating | i | language | like | love | natural | processing | study | things |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 |
| 2 | 0 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 0 |
| 3 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |

### Step 3. Subtract coordinates

[1, -1, -1, -1, -1, -1, 0, -1, 0]

### Step 4. Square, sum and take the root

1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 = 7; √7 = 2.645751311

### Final answer

**Euclidean distance=2.645751311.** Natural and Language each occur twice in sentence2; preserve both counts.