Comparing gliNER with LLM zero- shot labeling for Named Entity Recognition
April 18th, 2024
Named Entity Recognition (NER) plays a crucial role in various natural language processing tasks by identifying and categorizing entities such as person names, organizations, and locations within textual data. Two prominent approaches in the realm of NER, gliNER and LLM zero–shot labeling, offer distinct methodologies for entity extraction. gliNER relies on traditional text processing techniques and linguistic rules, while LLM zero- shot labeling leverages large pre–trained language models to perform NER without the need for fine–tuning on specific entity types. In this comparative analysis, we delve into the operational principles, applications, and advantages of both approaches to provide insights into their suitability for different use cases.
Understanding gliNER
Definition of gliNER and its operational principles
gliNER is a Named Entity Recognition (NER) system that relies on traditional text processing and linguistic modeling techniques. It operates by analyzing the lexical and grammatical context of a text to identify and classify named entities such as person names, organizations, locations, etc. The gliNER process typically involves the use of linguistic rules and lexicons to detect entities.
Techniques Utilized by gliNER :
Grammatical tagging: gliNER uses grammatical tagging to label each word in the text with information such as its grammatical class (noun, verb, adjective, etc.), which helps to understand the syntactic structure of the text.
Dependency parsing: This technique allows gliNER to analyze dependency relationships between words in a sentence, helping to determine which words are related to which entities.
Use of lexicons and linguistic rules: gliNER relies on word lexicons and linguistic rules to detect named entities. These lexicons contain lists of words that match specific entities, while linguistic rules define syntactic patterns for identifying entities in the text.
→By combining these techniques, gliNER achieves accurate recognition of named entities in various textual contexts, making it a versatile tool for many applications in the field of natural language processing.
Illustration through Example:
Concrete Example of gliNER’s Application in an NER Context To demonstrate the practical application of gliNER in a Named Entity Recognition (NER) context, consider the following scenario:
Scenario: A news agency wants to analyze a collection of news articles to extract named entities such as person names, locations, and organizations for further analysis and categorization.
Usage of gliNER:
Data Preparation: The news articles are preprocessed to remove noise and irrelevant information, leaving only the text content to be analyzed.
Application of gliNER: gliNER is applied to the preprocessed news articles to identify and extract named entities present within the text. This involves utilizing gliNER’s linguistic modeling techniques and rule- based approaches to recognize entities based on context and linguistic patterns.
Named Entity Extraction: Once gliNER has processed the news articles, it generates a list of identified named entities along with their corresponding entity types (e.g., person, location, organization).
Analysis and Categorization: The extracted named entities are then analyzed and categorized based on their relevance and importance to the news agency’s objectives. This may involve further processing or filtering based on specific criteria or requirements.
Practical Demonstration:
To demonstrate the practical usage of gliNER for Named Entity Recognition (NER), we begin by installing the gliNER package:
!pip install gliNER
This code snippet below imports the GLINER module and initializes a pre- trained model for named entity recognition. It puts the model into evaluation mode and confirms successful initialization by printing “ok”.
model = GLINER.from_pretrained ("urchade/gliner_mediumv2.1")
model.eval()
print("ok")
Once gliNER is installed, we can proceed to import the module and apply it to extract named entities from a sample news article:
import gliNER
# Sample news article text news article
=
"President Joe Biden announced new measures to combat climate cha
# Apply gliNER to extract named entities
extracted entities
=
gliNER.detect entities (news article)
# Display the extracted entities
print ("Named Entities Detected:")
for entity, entity_type in extracted entities. items ():
print (f" (entity): {entity_type}")
Finally, we extract these entities, demonstrating the model’s capability to predict named entities across multiple examples with consistency and reliability:
text
-
Editing Comparing gliNER with LLM zero-shot labeling for Named Entity Recognition - Medium
President Joe Biden announced new measures to combat climate change in Washington, DC.'
labels
=
["person", "LOCATION"]
entities = model.predict_entities (text, labels, threshold=0.4)
for entity in entities:
print(entity["text"], "=>", entity["label"])
President Joe Biden => person Washington, DC => LOCATION
Now, we delve deeper into the illustration with a slightly more complex example: CV extraction. The code for this example is provided in the notebook below
Successfully set up a new entity and worked closely with legal and external bodies.
Managed the recruiting process internally - interviews / 2nd interviews / trials etc.
• wrote and delivered weekly training sessions for sales team.
SKILLS
Communication
HR Policies
• Management
"
Leadership
English: fluent
French: native speaker
Spanish: beginner level
• Horse riding
Going to the theatre
• Gardening
labels = ["NAME", "NUMBER" "SKILLS" "LANGUAGES"
3
J
"HOBBIES", "EXPERIENCE"]
entities = model.predict_entities (text, labels, threshold=0.4)
for entity in entities:
print(entity["text"], "=>", entity["label"])
BEMMA SMITH => NAME
English => LANGUAGES French => LANGUAGES Spanish => LANGUAGES
Horse riding => SKILLS
Going to the theatre => HOBBIES
Gardening => SKILLS
When applying the gliNER model, it is observed that many entities are not extracted.
→ In our first example, where we seek to extract two entities from a single dataset, gliNER is utilized, yielding accurate and precise results. However, in the context of CV extraction, where the document encompasses complex structures and extensive data, attempts to extract multiple entities prove unsuccessful in capturing them all.
