When we think about keyword search, we typically think of the process of inputting a word or phrase into a search engine and retrieving a list of results. However, keyword search has come a long way since its inception, and it is now intricately linked to natural language processing (NLP). But before we jump into the advances ushered in by NLP-powered search, let’s get down to the basics of keyword search.
Understanding keyword search
When you think of keyword search, you probably think of the classic approach of matching key-value pairs. In this approach, the query terms are matched against a database of terms and their associated values. This can be done using a simple lookup table or a more sophisticated inverted index.
Inverted index is a data structure used to store the mapping between content and keywords. It is the foundation of many search engines and provides fast search capabilities. The inverted index, at its core, uses character-focused comparisons. This means that it works with an upside-down lookup which preprocesses each character of a query. For instance, when the search engine compares “mythology” in an inverted index, it will surface all the documents, including “m,” “my,” “myt,” “myth”, and so forth.
Once the records are found, they are then ranked in the order of relevance based on different techniques (including frequency-based ranking and tie-breaking algorithms).
In the context of keyword search, tokenization and normalization are used to process user input so that it can be compared against a known set of keywords. Tokenization helps to identify the individual keywords in a user's query, while normalization ensures that those keywords are formatted in a way that makes them easier to compare against the known set.
How NLP powers advanced keyword-based search
When you enter a query into a search engine, an NLP-based search engine parses the meaning of your words and matches them with the best results. The results are based on a complex algorithm that takes into account many factors, including the number of times the word or phrase appears on the page, how close together the words are, and whether the word is in the title or in the body text.
NLP is also used to accommodate for misspellings, typos, and synonyms. When a user types in a query, the search engine looks at all of the possible ways that the query could be spelt and returns results for all of those spellings. For example, if you search for "New York," the search engine will also return results for "New York City" and "NYC".
Normalization is the process of converting a keyword into a form that can be read and processed by a search engine. This is done by breaking down the keyword into its individual parts, such as root words, stems, and inflections. Once the keyword has been broken down, the search engine can then match it against other keywords in its database to find the best results.
Normalization is an important part of NLP-based search because it allows the search engine to understand the meaning of a keyword and its context. This helps the search engine return more relevant results for a given query.
Tokenization is the process of breaking down a string of text into smaller pieces or tokens. In the context of NLP-based search, tokenization is used to convert a query into a form that the search algorithm can understand.
There are a few different methods of tokenization, but the most common is to break down text by word boundaries. This means that each token is a separate word. Other methods include splitting by punctuation or whitespace. Once the text has been tokenized, the search algorithm can then begin its work. The algorithm will look for matching tokens in the index and return the results that have the most matches.
NLP Techniques that elevate keyword search
Once the queries have been normalized and tokenized, NLP-based tools can offer some powerful capabilities, including translating one language into another, understanding parts of speech, and stemming. Let’s take a look at some of the prominent NLP-driven search techniques
Stemming is the process of reducing a word to its root form. For example, the stem of the word "running" is "run". This can be useful for finding all instances of a word, regardless of its tense or conjugation. Stemming can be applied to both inflected (or derived) words and uninflected words. Inflected words are those that have been altered from their base form, usually by adding a suffix or prefix (such as -ed or -ing). Uninflected words have not been changed from their base form and typically refer to objects, ideas, or states rather than actions.
Lemmatization is similar to stemming, but it also takes into account the meaning of a word. For example, the lemma of the word "better" is "good." This can be useful for finding all instances of a word, regardless of its meaning.
This involves taking a sentence and identifying which word in that sentence corresponds to which part of speech. For example, in the sentence "The cat sat on the mat," the word "cat" would be tagged as a noun, "sat" would be tagged as a verb, and so on.
Part-of-speech tagging is used for a variety of tasks, such as grammar checking and building dictionaries. It can also be used to improve search results. By understanding the parts of speech in a query, a search engine can better match that query with relevant documents.
Stop words removal
As the name suggests, this preprocessing NLP technique involves removing any common words that are unlikely to be informative when searching for a specific term. For example, common English stop words like "a", "an", and "the" can be removed from a search query without affecting their meaning.
This technique can be especially useful when combined with other NLP search techniques like stemming and lemmatization. By removing stop words, you can streamline your search queries and focus on only the most relevant terms. In many cases, this can lead to much more accurate and precise results.
Transliteration is the process of converting text from one script to another using Natural Language Processing algorithms. This can be used to transliterate between languages with different writing systems or to convert text written in a non-standard script into the standard script for a language.
Conclusion - NLP-powered keyword search is the new era of search
NLP combined with Artificial Intelligence tools (like semantic and vector search) makes a potent search experience by understanding context, meaning, and relationship between words. Practical applications abound - summarizing texts, automated intelligent customer service, sentiment analysis, and personalized recommendations being only a handful of them.
Intelligent search and discovery platforms like Zevi understand this and leverage these powerful search technologies to create unforgettable online experiences. Zevi is driven by AI and machine learning (ML) and uses advanced algorithms to offer typo-tolerant search, synonym detection, natural language search, intelligent product recommendations, and a lot more..
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