The Scenario
You're a pharma analyst and the compliance team has a new requirement: the entity extraction model you're using for clinical abstract analysis has to recognize your company's proprietary drug names and trial codes as named entities, not generic text. You have 50 of them listed in column A of the 'Terms' sheet in an Excel workbook, and 200 clinical abstract texts in column A of the 'Content' sheet waiting for extraction. The TextRazor custom dictionary feature handles this, but setting it up through the API while also managing the extraction run is more moving parts than you have time for this week.
The bad version:
- Read the TextRazor API docs to find the custom entity dictionary endpoint, construct a POST request with the correct payload structure, authenticate with your API key, and upload all 50 terms.
- Write a second script that runs extraction on each abstract with the custom dictionary ID referenced in the request.
- Parse the response to separate standard entities from custom dictionary matches and decide which column should get which.
- Realize the API returned custom entity matches in a different field than standard entities, go back and update the parser, and re-run all 200 rows.
The compliance deadline is next Tuesday. The abstracts have been in the workbook for two weeks while you worked out the infrastructure problem.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads both worksheets, understands what you're building, and through its built-in TextRazor integration can create the custom entity dictionary from your terms and run extraction using it — all from a single prompt in the sidebar.
Upload all 50 terms from column A of the 'Terms' sheet as entries in a new TextRazor dictionary called 'Product Catalog', then extract entities from the texts in the 'Content' sheet using that dictionary and write found entities into column B.
What You Get
- SheetXAI uploads all 50 terms from the 'Terms' worksheet to a new TextRazor custom dictionary named 'Product Catalog.'
- It then runs entity extraction on each abstract in the 'Content' sheet with that dictionary active, so proprietary drug names and trial codes are recognized as entities alongside standard ones.
- Column B of the 'Content' sheet receives the extracted entity names — including any custom dictionary matches — for each abstract.
- Rows where no custom or standard entities are found get a noted placeholder rather than a blank.
What If the Data Is Not Quite Ready
The dictionary terms include variant spellings in column B
Create a TextRazor custom entity dictionary named 'Product Catalog' using the primary terms from column A of the 'Terms' sheet, including the variant spellings in column B as aliases for each entry, then run entity extraction on each text in the 'Content' sheet and write found entities into column B.
Some abstracts are in a mix of English and Latin
Create the TextRazor custom dictionary from column A of the 'Terms' sheet, then for each text in the 'Content' sheet, run entity extraction with the custom dictionary and write found entities into column B. Flag any row where the text appears to contain non-English content in column C.
You want to separate custom dictionary matches from standard entity matches
Create the TextRazor custom entity dictionary from column A of the 'Terms' sheet, run extraction on each text in the 'Content' sheet, and write custom dictionary matches into column B and standard entity matches into column C.
Full kill chain: upload dictionary, extract, compliance audit
Create a TextRazor custom entity dictionary named 'Product Catalog' from column A of the 'Terms' sheet. Run entity extraction on each text in the 'Content' sheet using that dictionary. Write custom dictionary matches into column B and their relevance scores into column C. Add an 'Audit' sheet listing each dictionary term, how many texts it appeared in, and the average relevance score — sorted by frequency descending.
The audit sheet is what compliance needs — getting it in one prompt means you're not building it manually after the extraction.
Try It
Get the 7-day free trial of SheetXAI and open the Excel workbook with your proprietary terms in one worksheet and the texts to analyze in another. Ask SheetXAI to create the TextRazor custom dictionary from your terms and run extraction on the abstracts in one step. The IAB or IPTC classification spokes cover topic categorization if you need that for the same content.