corrected a few spelling mistakes

README.md

@@ -1,55 +1,55 @@
 # Search Engine
 
-My first search engine built using Python, Numpy, Pandas and spaCy
+My first search engine built using Python, Numpy, Pandas, and spaCy
 
 ## Functionalities
 
 ### Boolean retrieval and filtering: Free and phrase queries
 
-By default passing a string is an **OR** query where we search for documents that contain any of the words in the query string. For example, if we pass the query string `python pandas` we will get all the documents that contain either `python` or `pandas` or both. 
+By default, passing a string is an **OR** query where we search for documents that contain any of the words in the query string. For example, if we pass the query string `python pandas` we will get all the documents that contain either `python` or `pandas` or both. 
 
-Additionally, if we pass any of the words in the query string in double quotes, we will get all the documents that contain the phrase in the double quotes. For example, if we pass the query string `"python" "pandas"` we will get all the documents that contain both words `"python"` and `"pandas"` (an **AND** query).
+Additionally, if we pass any of the words in the query string in double quotes, we will get all the documents that contain the phrase in the double quotes. For example, if we pass the query string `"python" "pandas"` we will get all the documents that contain both words `"python"` and `"pandas"` (an **AND** query). Both of these queries are implemented querying inverted indexes built from the cleaned and lemmatized corpus.
 
-**TODO:** Every word that needs to be present in the document as an and query needs to be in double quotes separately. As in if we want both the words `python` and `pandas`, we would need to pass the query string as `"python" "pandas"`. It would be better if we could pass the query string as `"python pandas"`.
+**TODO:** Every word that **needs** to be present in the document needs to be in double quotes separately. If we want the words `python` and `pandas`, we must pass the query string as `"python" "pandas"`. It would be better if we could pass the query string as `"python pandas"`.
 
-If the option `is_phrase` is set to `True`, we nbow consider the order of the words to be important (phrase queries). It is implemented using biwords. So if we pass the string `I love python`, we will get all the documents that contain the phrase `I love` **AND** `love python` wherein each of the words in the biword is present in the document but the biwords themselves might be in a different order in the docuemt.
+If the option `is_phrase` is set to `True`, we now consider the order of the words to be significant **(phrase queries)**. It is implemented using biwords. So if we pass the string `I love python`, we will get all the documents that contain the phrase `I love` **AND** `love python` wherein each of the words in the biword is present in the document but the biwords themselves might be in a different order in the docuemt.
 
 ### Wildcard queries
 
-Wildcard queries are queries that contain a wildcard character `*` which can be used to match any number of characters. For example, if we pass the query string `pyth*n` we will get all the documents that contain words that start with `pyth` and end with `n`. We can pass wildcard queries in three different ways:
+Wildcard queries contain a wildcard character `*` which can be used to match any number of characters. For example, if we pass the query string `pyth*n` we will get all the documents that contain words that start with `pyth` and end with `n`. We can pass wildcard queries in three different ways:
 
 1. At the beginning of the word: `*thon`
 
 2. At the end of the word: `pyth*`
 
 3. In the middle of the word: `py*on`
 
-**Note**: Currently only one wildcard character `*` is supported per query word (though each query word in the query string can have its own wildcard character), so a query like `p*yth*n` is translated to `p*n` in the backend.
+**Note**: Currently only one wildcard character `*` is supported per query word (though each query word in the query string can have its wildcard character), so a query like `p*yth*n` is translated to `p*n` in the backend.
 
 Additionally while using wildcard characters in a phrase query, it is retirved as on **OR** query on the biwords instead of an **AND** query.
 
-**Wildcard queries are automtically identified** and the queried on, and don't need any extra effort on the user's part.
+**Wildcard queries are automtically identified** and then queried on, and don't need any extra effort on the user's part.
 
-**TODO:** As of now the search functionality will consider any `*` as a wildcard query, so I am currently building an option to turn wilcard querying off so they can search for test containing an actual `*` in them.
+**TODO:** The search functionality will now consider any `*` as a wildcard query, so I am currently building an option to turn wilcard querying off so they can search for test containing an actual `*` in them.
 
 
 ### Ranking
 
-Ranking is done based on the `tf-idf` scores of the documents with respect to the query. The `tf-idf` score is calculated document at a time and the results are sorted and sent to the main `search` function. Only those scores that pass the initial boolean filter will have their scores sent from the backend. If the user just wants a boolean filtered result he/she/they just need to turn the `ranked` parameter to `False`. 
+Ranking is done based on the `tf-idf` scores of the documents for the query. The `tf-idf` score is calculated document at a time and the results are sorted and sent to the primary `search` function. Only those scores that pass the initial boolean filter will have their scores sent from the backend. If the user just wants a boolean filtered result he/she/they just need to turn the `ranked` parameter to `False`. 
 
-If wildcard characters are present in the query, all the words that match the wildcard query contribute to the score. For example if we pass `dat*`, both `data` and `date`  (and any others that match as well) will contribute to the score.
+If wildcard characters are present in the query, all the words that match the wildcard query contribute to the score. For example if we pass `dat*`, both `data` and `date`  (and any others that match) will contribute to the score.
 
 While retieving the documents, the user can choose the number of documents they want retrieved using the `retrieve_n` parameter in the engine's `search` function. 
 
-**Note:** Biwords do not have a seperate ranking and each word individually counts towards ranking. The initial filter takes care that the documents do indeed contain these biwords. Though performance comparisons are yet to be done, it is expected that the ranking will be better if the biwords are ranked as well.
+**Note:** Biwords do not have a seperate ranking and each word individually counts towards ranking. The initial filter takes care that the documents do indeed contain these biwords. Though performance comparisons are yet to be done, the ranking is expected to be better if the biwords are ranked as well.
 
 ### Spelling correction
 
-Spelling correction is done using a slightly modified version of the `levenshtien edit distance` algorithm. If the `spell_check` option is set to `True` and the given query string does not match any documents, the engine will try to find the closest match to every word from the corpus and return the documents that contain those words.
+Spelling correction uses a slightly modified version of the `levenshtien edit distance` algorithm. If the `spell_check` option is set to `True` and the given query string does not match any documents, the engine will try to find the closest match to every word from the corpus and return the documents that contain those words.
 
 Additionally, to the normal edit distance algorithm, I added a check if the word has two adjacent characters swapped (i.e.. now a new operation *swapping adjacent characters* is added to the normal *replacing, inserting, and deleting* options to find the minimum distance between two words).
 
-Spell checks ignore the wildcard characters and only check for the words that are not wildcard queries. It currently converts phrase and **AND** queries to **OR** queries and then checks for the closest match as we are changing the query itself.
+Spell checks ignore the wildcard characters and only check for the words that are not wildcard queries. It converts phrase and **AND** queries to **OR** queries and then checks for the closest match as we change the query itself.
 
 ### Autocomplete suggestions