Information Retrieval is about devising systems to find relevant pieces of information from a large collection. It’s like the process of looking for the needle of desired data in a haystack of millions or billions of documents or data points.
Imagine you’re in a room full of toys and you’re looking for one particular Lego piece to complete your creation. “Information Retrieval” is like the process you’ll employ to find that piece. It’s the set of strategies, techniques, and systems for finding the exact toy you need amongst all the others.
Information Retrieval (IR) is a significant topic within AI, and it’s concerned with obtaining relevant information from a massive set of data. The origin of this term primarily sits within the realm of textual document retrieval, like how a search engine finds relevant web pages based on user queries. However, it extends to other domains such as image retrieval, video retrieval, and any scenario where we need to find relevant items within large collections.
The foundation of IR systems is an index, which essentially maps each unique ’element’ (like a word in text, or a feature in an image) to the set of data items (documents, images) containing that element. When a user submits a query, the system uses this index to find pertinent items.
Traditionally, Information Retrieval systems primarily relied on statistical analysis of the term frequency, i.e., how often does the query term appear in each document. This is known as the “bag of words” approach. But with advancements in AI and NLP, techniques like semantic search, using models like Word2Vec or transformer-based models like BERT, GPT, etc., have significantly improved retrieval effectiveness by understanding the meaning behind the query.
Evaluation of IR systems often involves metrics like precision (how many retrieved items are relevant?) and recall (what fraction of the full set of relevant items were actually retrieved?).
Yet, despite this progress, Information Retrieval remains a dynamic and challenging field due to issues related to understanding context, managing ambiguous queries, handling the vast and continually increasing size of data, and many others.
Indexing, Query Processing, Document Classification, Semantic Search, Precision (In The Context Of Classification), and Recall, Natural Language Processing (NLP),, Word2Vec, BERT, GPT, Bag of Words, TF-IDF, Information Extraction