Natural Language Processing and Inverted Index Phase Generation
Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that makes human language intelligible to machines. NLP combines the power of linguistics and computer science to study the rules and structure of language, and create intelligent systems (run on machine learning and NLP algorithms) capable of understanding, analyzing, and extracting meaning from text and speech.
Think about how much text you see each day:
- Web Pages
- and so much more…
What Is NLP Used For?
- NLP is used to understand the structure and meaning of human language by analyzing different aspects like syntax, semantics, pragmatics, and morphology. Then, computer science transforms this linguistic knowledge into rule-based, machine learning algorithms that can solve specific problems and perform desired tasks.
- Take Gmail, for example. Emails are automatically categorized as Promotions, Social, Primary, or Spam, thanks to an NLP task called keyword extraction. By “reading” words in subject lines and associating them with predetermined tags, machines automatically learn which category to assign emails.
There are many benefits of NLP, but here are just a few top-level benefits that will help your business become more competitive:
- Perform large-scale analysis. Natural Language Processing helps machines automatically understand and analyze huge amounts of unstructured text data, like social media comments, customer support tickets, online reviews, news reports, and more.
- Automate processes in real-time. Natural language processing tools can help machines learn to sort and route information with little to no human interaction – quickly, efficiently, accurately, and around the clock.
- Tailor NLP tools to your industry. Natural language processing algorithms can be tailored to your needs and criteria, like complex, industry-specific language – even sarcasm and misused words.
How Does Natural Language Processing Work?
Using text vectorization, NLP tools transform text into something a machine can understand, then machine learning algorithms are fed training data and expected outputs (tags) to train machines to make associations between a particular input and its corresponding output. Machines then use statistical analysis methods to build their own “knowledge bank” and discern which features best represent the texts, before making predictions for unseen data (new texts):
Ultimately, the more data these NLP algorithms are fed, the more accurate the text analysis models will be.
Sentiment analysis (seen in the above chart) is one of the most popular NLP tasks, where machine learning models are trained to classify text by polarity of opinion (positive, negative, neutral, and everywhere in between).
Inverted Index Phase Generation
An inverted index is an index data structure storing a mapping from content, such as words or numbers, to its locations in a document or a set of documents. In simple words, it is a hashmap like data structure that directs you from a word to a document or a web page.
There are two types of inverted indexes: A record-level inverted index contains a list of references to documents for each word. A word-level inverted index additionally contains the positions of each word within a document. The letter form offers more functionality, but needs more processing power and space to be created.
Suppose we want to search the texts “hello everyone, ” “this article is based on inverted index, ” “which is hashmap like data structure”. If we index by (text, word within the text), the index with location in text is:
hello (1, 1) everyone (1, 2) this (2, 1) article (2, 2) is (2, 3); (3, 2) based (2, 4) on (2, 5) inverted (2, 6) index (2, 7) which (3, 1) hashmap (3, 3) like (3, 4) data (3, 5) structure (3, 6)
The word “hello” is in document 1 (“hello everyone”) starting at word 1, so has an entry (1, 1) and word “is” is in document 2 and 3 at ‘3rd’ and ‘2nd’ positions respectively (here position is based on word).
The index may have weights, frequencies, or other indicators.
Steps to build an inverted index:
- Fetch the Document
Removing of Stop Words: Stop words are most occurring and useless words in document like “I”, “the”, “we”, “is”, “an”.
- Stemming of Root Word
Whenever I want to search for “cat”, I want to see a document that has information about it. But the word present in the document is called “cats” or “catty” instead of “cat”. To relate the both words, I’ll chop some part of each and every word I read so that I could get the “root word”. There are standard tools for performing this like “Porter’s Stemmer”.
- Record Document IDs
If word is already present add reference of document to index else create new entry. Add additional information like frequency of word, location of word etc.
Words Document ant doc1 demo doc2 world doc1, doc2
Advantage of Inverted Index are:
- Inverted index is to allow fast full text searches, at a cost of increased processing when a document is added to the database.
- It is easy to develop.
- It is the most popular data structure used in document retrieval systems, used on a large scale for example in search engines.
Inverted Index also has disadvantage:
- Large storage overhead and high maintenance costs on update, delete and insert.