In the rush to ride the generative AI wave, it’s easy to dismiss keyword-based search as yesterday’s technology. But for enterprise leaders tasked with delivering both innovation and operational efficiency, lexical search still holds serious value. 

When precision, speed, and compliance matter — as they often do in enterprise scenarios — semantic AI alone can’t carry the weight.

Here’s why keyword-based search still deserves a seat at the table in your modern tech stack, why it’s an important part of a hybrid ranking strategy, and how it can be a powerful complement to your AI-experience advantage.

Understanding Lexical Search in Information Retrieval

Lexical search (sometimes also referred to as keyword search, though the accuracy of such a comparison is a much-debated topic among search experts) exactly matches words or phrases that appear in a query with those in a document or piece of content. It relies on the user to know and enter the correct and relevant terms. 

When an individual types “Hawaii travel tips,” the search looks to precisely match the keywords to documents containing “Hawaii,” “travel” and “tips.” Using this method, the search will not retrieve related results if those keywords are missing such as “advice for Maui trip.” 

Keyword or lexical search is often referred to as traditional search because it has been the long-time dominant method for search engines and databases.

How Does Lexical Search Work?

The main steps of lexical search include:

  • Tokenization: A query and searchable documents are broken down into chunks of text called tokens.
  • Matching: The search engine finds exactly matches between the query terms and the indexed words of documents.
  • Ranking: Documents are ranked based on term frequency and other scoring models.

A key aspect of lexical search is the inverted index, a data structure that maintains a list of documents for each term, mapping words to the documents in which they appear. Instead of the need to search through each document for keywords, the search engine scans the inverted index to quickly locate and retrieve the matching documents. 

Additionally, lexical search uses the “bag of words” approach to retrieve and rank results, meaning each piece of text is treated independently of each other as if thrown into a bag, without taking into account word order. The approach counts the term frequency (TF), or occurrences of a word, and represents the document as a vector of the frequencies. 

A query is converted into a bag of word vectors and compared to the vectors in a document. Lexical search usually uses a ranking algorithm to order results by relevance, based on the frequency of words in a document or text (TF). It also uses other factors like the rarity of the words across the collection of documents, called the term frequency-inverse document frequency (TF-IDF). 

Even in modern applications, keyword search continues to be relevant today. It is a familiar approach to search, straightforward and often easier to implement. Users are also able to quickly understand why they are seeing certain results in response to their queries, which improves user transparency. Lexical search is also fast and efficient, requiring less computational resources than other more advanced and complex methods. 

Since the focus is on word frequency without taking into account context or word order, keyword-based retrieval is effective for searches that require precision. It excels in situations where the search is for a specific document or words in a repository, such as a customer’s name or an air fryer’s user manual. Lexical search is most suitable to the following types of searches:

  • Exact keyword matching
  • Searching structured databases 
  • Boolean and faceted search

In contrast, other search techniques like semantic search (one method of which is vector search, sometimes referred to as neural search) rely on natural language processing (NLP) to understand a query’s context and meaning. In scenarios where a user is not likely to know the exact term they need to search (such as articulating a question to find the right knowledge article in customer support, or searching for a specific product in an ecommerce store), semantic search’s ability to get to a query’s intent often leads to more relevant search results. 

Limitations of Lexical Search

Keyword search has been in use for a long time with good reason. Its efficiency and transparency make it the ideal choice for queries that are highly structured, require precise matching and benefit from low computational overhead. However, while lexical search shines in queries for exact matches, it can miss relevant results due to its limitations.

In today’s information retrieval landscape in which users expect personalized, intuitive search that can handle complex queries, lexical search can have several drawbacks. These limitations include:

Requires exact matching

Users must use the exact words to return the desired results, leaving little room for misspellings or other variations of words in the query (unless the system applies additional techniques, which we cover below). This can lead to missing relevant documents because lexical search does not account for human error. 

Struggles with synonyms and multiple word meanings

Lexical search struggles with more complex queries in which variations in words are important to getting to the intended results. These include words with similar meanings (synonyms) or multiple meanings (polysemy). For example, a search for “mercury” could return results for the chemical element, planet and insurance agency. As a result, users can get results that are irrelevant to their search intent. At the same time, the search can miss relevant results because it fails to understand that a query for a “new laptop” is similar to “unused laptop.”

Lacks contextual understanding

Since lexical searches treat each word as independent tokens without taking into account word meanings or relationship to each other, a keyword-based search is unable to understand a query’s context or intent. It does not take into account a user’s search history or location, or whether a query is asking a question or seeking recommendations, limiting results to exact keyword matches. For example, “how to get ready for kindergarten” may miss related results like “best backpacks for young kids,” “easy school lunches for your five year old” or “tips for the first day of school.”

Unable to interpret long or ambiguous queries

Keyword matching struggles withqueries that have long, complex wording, leading to irrelevant results. For example, a search for “best budget SUVs for family of six” can lead to results with any of these words even if they are unrelated.

Lexical search is still a powerful method of searching when accurate keyword matching is a priority. But as the way digital search evolves to be more conversational and dynamic, businesses will benefit from understanding when to apply methods like semantic search or combining lexical search with other techniques that can interpret user intent.

Lexical and Semantic Search Techniques

Lexical and semantic search are two major methods to find and retrieve relevant answers to queries. As search technologies evolve, enterprises can benefit through using techniques from both types of searches in tandem to improve the search experience and deliver more accurate results. 

How Lexical Search Works

Lexical search, with its reliance on exact term matching between a search query and a group of documents, often employs the following techniques:

  • Boolean operators: More complex queries benefit from combining multiple terms through the operators “and,” “or” and “not” to refine results.
  • Fuzzy matching: To account for word variations and errors like misspellings, this technique approximately matches between a query and documents using algorithms to help find relevant results.
  • Stemming: This technique reduces words to their base form (e.g., “eating” to “eat”) to find and match variations of a term.
  • BM25 algorithm: Best matching 25 is a popular search algorithm in lexical search to rank documents by relevance using term frequency, inverse document frequency and average document length.

How Semantic Search Works

Semantic search focuses on query intent rather than keyword matching by understanding the linguistic meaning of words. It is most useful in searches where context and word associations are important in the query, such as “what to wear in humid climates.” 

Semantic search understands nuances in language and context to return more accurate results, such as recommendations for wardrobes for summer. It is ideal for searching through large amounts of unstructured data. Use cases can include chatbots or customer support applications in which natural language and conversational interactions are needed. 

However, semantic search may lead to irrelevant results if the query is too broad or generic to make an accurate assessment of the user intent. For example, a search for “walking shoes” could bring up a variety of results including shoes for men and women, sandals, sneakers and hiking boots. 

Semantic Search Techniques

Semantic search commonly involves the following techniques that help the search engine understand nuances in language and retrieve results in context:

  • Natural language processing: A subset of artificial intelligence, natural language processing (NLP) interprets word meanings and understands user intent and context from queries.
  • Machine learning models: Semantic search uses ML, a type of AI that learns from data and improves over time, to understand the semantic meaning of words in vector space.
  • Vector search and word embeddings: Queries and documents are converted into vector representations to capture word meanings and relationships.
  • Contextual understanding: A semantic search engine will use contextual data like the user’s location or time of day to improve search results for queries such as “bakery near me.”

The best practice for companies today will likely not be a choice between semantic and lexical search, but combining the techniques in a hybrid approach. Modern search solutions build each approach on top of the other to take advantage of the capabilities of both and come up with the most effective search system. This approach, called hybrid search, combines the precision of keyword-based search with the contextual understanding of semantic search techniques. Some also refer to the layering of ML on semantic search as neural search, even though this is often standard practice.

Used in sequence, exact keyword matching can be used to enhance precision followed by NLP in semantic search to retrieve contextually relevant results. Machine learning will improve the accuracy and relevance over time based on more data, such as user behavior. These searches can be combined in parallel as well, returning relevant results for each search type (keywords and contextual search) in a unified way.

Future Trends in Lexical Search Technology

Developments in keyword search are leading this method to evolve with new technologies and changes in the way users interact with information.  

Hybrid search models

A combination of keyword-based search and vector (semantic) search in a “best of both worlds” approach will continue to expand and grow, leading to more comprehensive results. Many modern and future systems will allow for dynamic adjustments between these approaches to find the right balance according to the type of query.

AI-powered query enhancement

Augmenting traditional search techniques with AI is on the rise. AI-driven query enhancements that preprocess queries before applying exact term matching can significantly improve search experiences and the relevance of results. These enhancements may be query suggestions or expansions to include common word substitutes or synonyms, helping to overcome limitations of exact match information retrieval.

As the adoption of voice and image search grows, lexical search will enhance support for these multimodal queries with precise keyword matching. Combining lexical/keyword search with other technologies like semantic search (aka harnessing hybrid search) preserves the precision needed for accurate results while also balancing this with the recall required for natural language in responses created by large language models. 

Relevant reading: Information Retrieval Trends: What Does The Future Look Like For Search? 

Moving Forward with Lexical Search  

While semantic search has grown in prominence with interest in more nuanced, intuitive interactions, keyword-based search is still the preferred method for retrieving precise information in a straightforward, fast, and accurate way. 

Rather than fading in relevance, lexical search is evolving and combining with other search methods to create stronger information retrieval systems. Understanding both the benefits and limitations of lexical search can help businesses apply the right method according to their information retrieval needs. 

When it comes to search, the right solution will depend on the situation and use case and will often combine the strengths of search methods to achieve higher quality results.