More about NAMER, the Bitext Smart Entity Technology
January 14, 2025
A dinobaby product! We used some smart software to fix up the grammar. The system mostly worked. Surprised? We were.
We spotted more information about the Madrid, Spain based Bitext technology firm. The company posted “Integrating Bitext NAMER with LLMs” in late December 2024. At about the same time, government authorities arrested a person known as “Broken Tooth.” In 2021, an alert for this individual was posted. His “real” name is Wan Kuok-koi, and he has been in an out of trouble for a number of years. He is alleged to be part of a criminal organization and active in a number of illegal behaviors; for example, money laundering and human trafficking. The online service Irrawady reported that Broken Tooth is “the face of Chinese investment in Myanmar.”
Broken Tooth (né Wan Kuok-koi, born in Macau) is one example of the importance of identifying entity names and relating them to individuals and the organizations with which they are affiliated. A failure to identify entities correctly can mean the difference between resolving an alleged criminal activity and a get-out-of-jail-free card. This is the specific problem that Bitext’s NAMER system addresses. Bitext says that large language models are designed for for text generation, not entity classification. Furthermore, LLMs pose some cost and computational demands which can pose problems to some organizations working within tight budget constraints. Plus, processing certain data in a cloud increases privacy and security risks.
Bitext’s solution provides an alternative way to achieve fine-grained entity identification, extraction, and tagging. Bitext’s solution combines classical natural language processing solutions solutions with large language models. Classical NLP tools, often deployable locally, complement LLMs to enhance NER performance.
NAMER excels at:
- Identifying generic names and classifying them as people, places, or organizations.
- Resolving aliases and pseudonyms.
- Differentiating similar names tied to unrelated entities.
Bitext supports over 20 languages, with additional options available on request. How does the hybrid approach function? There are two effective integration methods for Bitext NAMER with LLMs like GPT or Llama are. The first is pre-processing input. This means that entities are annotated before passing the text to the LLM, ideal for connecting entities to knowledge graphs in large systems. The second is to configure the LLM to call NAMER dynamically.
The output of the Bitext system can generate tagged entity lists and metadata for content libraries or dictionary applications. The NAMER output can integrate directly into existing controlled vocabularies, indexes, or knowledge graphs. Also, NAMER makes it possible to maintain separate files of entities for on-demand access by analysts, investigators, or other text analytics software.
By grouping name variants, Bitext NAMER streamlines search queries, enhancing document retrieval and linking entities to knowledge graphs. This creates a tailored “semantic layer” that enriches organizational systems with precision and efficiency.
For more information about the unique NAMER system, contact Bitext via the firm’s Web site at www.bitext.com.
Stephen E Arnold, January 14, 2025
Comments
Got something to say?
-
- Subscribe to Beyond Search
Feature archive
News archive
-
