# NLP

<details>

<summary>[OKCUPID] Term Frequency</summary>

Say you are given a text document in the form of a string with the following sentences:

**Input**

`document = "I have a nice car with a nice tires"`

**Output**

`{ "I":0.11, "have":0.11, "a":0.22, "nice":0.22, "car": 0.11, "with":0.11, "tires":0.11 }`&#x20;

Write a program in python to determine the TF (term frequency) values for each term of this document.

**Answer**

{% code overflow="wrap" %}

```python
# term freq = freq of term 't' in doc 'd' / total terms in 'd'

def tf(doc):
    temp_list = doc.split(" ") # split terms
    final_dict = {} # store final dictionary with term freq
    for i in temp_list:
        if i not in final_dict.keys(): # check if term is already present in dict
            final_dict[i] = round(temp_list.count(i)/len(temp_list),2) # calculating tf
    return final_dict

tf("I have a nice car with a nice tires")
```

{% endcode %}

</details>


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