Information Retrieval & NLP Services

Information Retrieval & NLP Services


    What is NLP?

    Natural Language Processing (NLP) is the part of AI that studies how machines interact with human language. Combined with machine learning algorithms, NLP creates information retrieval services that learn to perform tasks on their own and get better through experience. Probably, the most popular examples of NLP in action are virtual assistants, like Google Assist, Siri, and Alexa. NLP understands and translates the human language, like “Hey Siri, where is the nearest gas station?” into numbers, making it easy for machines to understand. Another well-known application of NLP are chatbots, which can help you solve issues while performing natural language generation – in other words, holding a conversation in plain English!

    NLP services

    What is Information Retrieval?

    Online Information retrieval (IR) is the process of obtaining information system resources that are relevant to an information need from a collection of those resources.

    information retrieval services

    How we use NLP & Information Retrieval Services in SEO?

    We can use artificial intelligence for content optimization in SEO. Can use Semantic AI for use.

    Semantic: Semantics, also called semiotics, semiology, or semasiology, the philosophical and scientific study of meaning in natural and artificial languages. The term is one of a group of English words formed from the various derivatives.

    In SEO, can use artificial intelligence for extracting data from the document. Can use many types of algorithm for extracting information from large amounts of data using AI.

    Here we see one by one how we can optimize the content:-

    What is Cosine Similarity and its benefits in SEO?

    In SEO term, cosine similarity measures how much a given keyword available in the document is similar to the overall context of the landing page. In SEO, the more similar a keyword is with respect to the landing page, the better it is for ranking in SERP.

    Ideal score for Cosine similarity

    Ideal Score for cosine similarity to have good SEO is 0.5 or above

    Less than 0.5 is bad and needs improvement

    If the result is 0 that means the keyword is not at all present.

    Work mechanism:

    We first take the given keyword against the target landing page URL and compute the cosine value in % based on our AI code.

    After that, we compare it with our competitor’s mean value and check for comparison whether the data obtained is less or greater than the competitor’s. If our value is greater than the mean value of the competitor’s then no action needed. Else we run our code again to suggest how much occurrence we need to add again to the landing page for improving the score.

    What is LDA and its benefts in SEO?

    Latent Dirichlet Allocation or LDA is a Topic modeling form. In SEO LDA will help to identify relevance score of a particular keyword as well as increase a page’s relevancy in google. It shows words that help Google determine how relevant the page is to a user’s search query.

    Ideal score for LDA

    Ideal Score for LDA to have good SEO: 0.1 to 0.3

    More than 0.3 it’s excellent, Within 0.1-0.3 its ideal for SEO

    Less than 0.1 is bad

    Work mechanism:

    First we take the URL of our given campaign and scrap the whole document

    Then we use LDA algorithm & calculations to compute the relevancy signals

    We then correlate with the mean relevance value of competitor’s and we use conditional statement to check if the value is less or greater than the competitor’s

    If our value is greater than the competitor’s then no action needed else we use our code structure to suggest the missing terms which will help us to improve our score once added to the landing page.

    What is Bag of words and its benefts in SEO?

    Bag of words model is an information retrieval model. It extracts keywords from large amount of data. It also specifes the frequency of the key words used in the document. In SEO, we use this model to create tag and correlate with competitor’s tag to make use of all missing search terms as compared against competitors.

    Please bear in mind that if more search terms are targeted; greater will be the SERP visibility.

    Ideal Value for SEO

    There are no ideal values for Bag of words, the agenda remains simple. We just need to optimize our pages based on the available tag as per competitive analysis.

    The more we use, the better we will help in improving the SERP visibility. Please bear in mind, don’t over-optimize else it will lead to spamming.

    Work mechanism:

    Create model for your landing which you want to target and collect top 10 words based on frequency. Repeat the above process for competitor’s as well and fetch top 10 respective high frequency words as well. The merge all the top frequency words from the competitor’s and make a super set of word tag, make sure the set is unique and no repetition of words are available. Then check your own landing page word with the fnal super set word and collect the words which are not present in your landing page. Make a final list of the unique words and this will be your output. In other words, this final list we need to use within our landing page to optimize the context around the page.