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How to use natural language processing and SQL to analyze text data

The vast amount of data available today necessitates the employment of powerful, adaptable querying and processing tools. SQL can be difficult for users who are unfamiliar with it and struggle to construct precise queries.


NLP is a branch of computer science and artificial intelligence that studies computers’ capacity to interpret human language. In recent years, substantial advances in NLP have allowed computers to comprehend and understand human language more accurately.

Data storage systems have advanced dramatically, transitioning beyond flat files to hierarchical and network data storage forms. Relational databases are now widely used for dealing with complicated data connections. Relational databases store data in tables and may extract essential information from several tables, integrate it, and generate a unique output. Each table consists of rows and columns. Each row contains numerous bits of information linked to a key, whereas column names reflect qualities or data relevant to a search query.

The fundamentals of SQL

SQL is a standard language for relational database administration that can store, manipulate, and retrieve huge datasets. As big data analysis becomes an important part of corporate decisions, the necessity for natural communication between humans and computers grows. Natural language processing allows computers to analyze and understand text and voice messages, allowing users to provide commands in their native language while deep learning models decode messages and execute requests effectively. This method reduces the difficulties when interacting with SQL, making data interaction harder for beginners. Semantic parsing is the process of translating natural language queries and commands into SQL commands that may be understood through a series of processes.

Elements used to translate natural language to SQL commands

1. Normalization in natural language processing entails stemming and lemmatization, which decrease text unpredictability and provide a predetermined format. Stemming prepares words for processing, whereas lemmatization reduces words to their base form to facilitate searching or joining.

2. Tokenization is the process of breaking down text into individual words known as tokens. They might be words, sentences, or other types of writing.

3. Part-of-speech tagging delivers a part-of-speech tag to every token in a text sequence. The part-of-speech tags provide the grammatical purpose for each token in the text. As in the field of linguistics the word “cat” has the tag “noun,” but the word “run” has the tag “verb.”

4. Named entity recognition is the process of identifying and categorizing named entities in text, such as people’s names, organizations, or locations.

5. Parsing is a technique for analyzing text or phrases to discover their grammatical structure, which is commonly used in natural language processing to comprehend their meaning. It entails detecting noun and verb phrases and transforming this data to SQL queries via syntactic mapping and syntax trees.

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Requirements for transforming natural languages into SQL queries

1. Dataset

A dataset is an organized set of data that scientists utilize or that software automatically compiles from various sources, such as text, pictures, and numerical data. It is vital for transforming text inquiries to SQL queries as computers struggle with unstructured data. Certain datasets are exclusive to certain sector categories; for example, the IMDB dataset, which includes movie titles, is not useful for marketing or meteorological information.

The databases Spider, WikiSQL, ATIS, WordNet, and GeoQuery are widely used for semantic parsing to SQL. These datasets include an extensive number of SQL and natural language queries, responses, and information for text-to-SQL models.

2. Trained model

An intermediary representation across the user’s query and the database is offered by a semantic parsing model. The instructions for working with datasets are included in a model. In terms of architecture, a model consists of a decoder and an encoder. To determine the word semantics, the encoder maps words into logical, comparable forms via word embedding. The decoder converts the literal meaning of the words into SQL queries following encoding.

Although these models usually aren’t 100% accurate at translating language, with more query-generating jobs under their belt, they gradually improve.

Steps for developing a SQL query from natural language

These are the steps for developing natural language inquiries to SQL queries using Google’s Big Query API’s Data QnA interface. Accurate text-to-SQL creation necessitates the usage of an encoder-decoder framework or trained interpretation model. Below are mentioned steps to develop a SQL query from a natural language

1. Create an account on the Google Cloud platform by going to the sign-up page and selecting “Get started for free.”

2. After entering your account details, click “Continue.”

3. Use the Google Cloud platform to search for “Data QnA” and create a new data table from your dataset. 

4. To activate the newly created table, navigate to “Manage” and select “Enable New Table.”

5. Type in the name of your table and select “Enable Table.”

6. Configure the display name, synonyms, data type, column type, and name of the table.

7. The “Synonyms” column permits synonyms, while the “Name column” shows distinct tags for every search.

8. To construct an index, save the data collection.

9. To open Big Query, search for “Big Query” and choose the stored data set name.

10. Pose inquiries concerning the entries in natural language

11. Select the table you wish to inquire about and click “Generate Equivalent SQL.”

12. The algorithm creates a semantic understanding of the question and converts it to SQL.

13. Start the query editor and run it to analyze the data and obtain your values.


Natural language processing (NLP) and Structured query language (SQL) are strong tools for analyzing text data, and integrating them can provide several advantages and opportunities for data-driven decision making. However, several obstacles and research concerns must be solved to improve the reliability, scalability, and usefulness of this technology.