5 essential techniques to optimize the recognition of named entities in AI
Recognition of named entities (Named Entity Recognition or NER) has become an important component in many modern applications, from social media analytics to recommendation systems. However, we see that even the most sophisticated artificial intelligence systems can fail when faced with complex or ambiguous texts.
As specialists in natural language processing, we know that NER requires careful optimization to achieve satisfactory performance. Improving a system NLP indeed requires a methodical approach and precise techniques.
💡 In this article, we're going to explore five essential techniques to optimize your entity recognition systems. We'll cover every aspect, from data preparation to performance evaluation, to Fine tuning models. Follow the guide!
Understand the fundamentals of NER entity recognition
We begin our exploration of named entity recognition (NER) systems by examining their essential foundations. As a sub-task of information extraction, NER plays an important role in natural language processing.
Definition and examples of entity recognition
Entity recognition is an essential natural language processing (NLP) technique that aims to identify and classify named entities in text. These entities can be names of people, places, organizations, dates, amounts, and more. For example, in a text, “Apple” may be recognized as a named entity belonging to the “Organization” category, while “Paris” will be classified as a “Location.” Likewise, “2022” will be identified as a “Date.” These examples illustrate how entity recognition makes it possible to structure and analyze texts more effectively.
Entity recognition approaches
There are several approaches to entity recognition, each with its own pros and cons. Rule-based systems use predefined rules to extract named entities, offering high accuracy in specific contexts but lacking flexibility. Systems based on statistical models, on the other hand, use probabilistic models to detect entities, offering greater adaptability to different types of texts. Finally, systems based on machine learning exploit sophisticated algorithms to learn from large amounts of annotated data, allowing for more robust and generalizable entity recognition.
The essential components of an NER system
In our experience, an effective NER system is based on several key components:
- Tokenization and segmentation : To identify entity boundaries
- Entity classification : To categorize identified items, including medical codes and other categories
- Statistical models : For learning patterns
- Reference databases : For the validation of entities
💡 Systems based on formal grammars, combined with statistical models, generally achieve the best results in major evaluation campaigns.
Common challenges in recognizing named entities
We regularly encounter several major obstacles in implementing NER systems:
- Contextual ambiguity : The same word can represent different entities depending on the context (for example, “Apple” can refer to the company or the fruit). Additionally, extracting relevant information such as candidate names from resumes can be complex due to this ambiguity.
- Linguistic variations : The different ways to write the same entity (such as “USA”, “U.S.A.”, “United States”).
- Multilingual limitations : Accuracy varies considerably between languages, mainly due to the lack of labelled data.
The importance of optimization for performance
We see that optimization is in the process of achieving high performance. Modern systems achieve F-measure scores in excess of 90%, approaching human performance, which is around 97%. However, these impressive results need to be nuanced because they are obtained in specific and controlled evaluation contexts.
To improve accuracy, we use hybrid approaches that combine linguistic rules and machine learning methods. This combination allows us to benefit from the precision of manual rules while maintaining the flexibility of statistical models.
Optimizing the quality of training data
The quality of training data is the cornerstone of a successful named entity recognition system. Using articles to train these systems can improve the accuracy and understanding of named entities. Our experience shows that this preliminary step largely determines the final success of the model.
Data cleaning and preparation techniques
We've found that thorough data cleaning is critical to achieving optimal results. Data should be carefully reviewed and organized before starting the learning process. Here are the steps we take:
- Removing duplicates and irrelevant samples
- Standardization of the data format
- Fixing syntactic errors
- Standardization of annotations, including the classification of values such as monetary values and quantities
- Structured data organization
Effective annotation strategies
Accurate data annotation is fundamental to model learning. Entity recognition, or NER (Named Entity Recognition), makes it possible to analyze and classify textual data by extracting entities such as names, places, and organizations. Our analyses show that an entity type requires at least 15 instances labelled in the training data to obtain acceptable accuracy.
To optimize this process, we recommend:
- Establish clear annotation guidelines
- Train annotators in the specificities of the field
- Set up a cross-validation system
Data validation and enrichment
Our validation approach is based on a balanced distribution of data. Entity types should be evenly distributed between training and test sets. To enrich our data, we use several techniques:
Increase in data
We apply techniques such as synonymization and the generation of synthetic examples to enrich our data set.
Cross validation
Data is randomly assigned into three categories (training, validation, and testing) to avoid sampling bias.
For complex named entity recognition NLP projects, we recommend using platforms from crowdsourcing or specialized tools for annotation. This approach makes it possible to obtain a sufficient volume of labelled data while maintaining a high level of quality.
Refine model parameters
Optimizing parameters is a key step in maximizing the performance of our named entity recognition models. To help users understand how to effectively use this feature in their applications, it is essential to highlight reference documentation and sample code. We found that this phase requires a methodical approach and adapted tools.
Selecting the optimal hyperparameters
We use several optimization methods to identify the best hyperparameters. Our experience shows that for complex NER models, the number of hyperparameters can quickly become very important, up to 20 parameters for methods based on decision trees.
The main techniques we use are:
- Grid Search : Ideal for 2-3 hyperparameters
- Random Search : More effective for extended search spaces
- Bayesian approaches : Optimal for complex models
Fine-tuning techniques
For fine-tuning our models, we use MLflow and Tensorboard to track metrics and training parameters. Our optimization process focuses on several key aspects:
- Adjustment of the learning rate
- Configuring hidden layers
- Optimizing the size of mini-batches
- Adjusting the dropout rate
We observed that the use of an early stopping strategy makes it possible to significantly improve the efficiency of the calculation. This approach helps us quickly identify underperforming configurations.
Comparative performance assessment
Our assessment framework is based on three essential components:
- A data layer for preparing datasets
- A model layer for extracting entities
- An evaluation layer for performance analysis
To measure the effectiveness of our optimizations, we use specific metrics such as accuracy and recall. We found that evaluating at the entity level and at the model level can reveal significant differences in performance.
Automating hyperparameter optimization allows us to effectively explore the parameter space while maintaining a detailed record of our experiments. This systematic approach helps us identify the optimal configurations for our NLP named entity recognition models.
Implement advanced pretreatment techniques
In our journey to optimize named entity recognition systems, advanced preprocessing of textual data plays a key role. We found that the quality of this stage directly influences the performance of our NER models.
Normalizing the text
Standardization is the critical first step in our pre-treatment pipeline. We mainly use two complementary approaches:
- Stemming : Reduces words to their root by removing affixes
- Lemmatization : Convert words into their canonical form
- Unicode normalization : Standardize character representations
- Contextual standardization : Adapts standardization according to the field
Our experience shows that lemmatization with identification of parts of speech (POS tagging) generally offers better results than the Stemming alone.
Management of special cases
We pay particular attention to dealing with special cases in our NLP named entity recognition systems. Managing special tokens like [CLS] and [SEP] requires a methodical approach.
To optimize the treatment of specific cases, we have developed a strategy in three phases:
- Identifying special tokens
- Applying appropriate attention masks
- Controlled propagation of labels
The spread of labels to the sub-parts of words is a major challenge. We found that the choice to propagate or not the labels significantly influences the performance of the model.
Optimization of Tokenization
Our approach to tokenization is based on encoding by pairs of bytes (Byte Pair Encoding). This method makes it possible to effectively manage non-vocabulary words and subwords. We observed that some words can be divided into several sub-tokens, such as “antechamber” which becomes “anti” and “chamber”.
To optimize this process, we use attention masks with a value of 0 for the tokens of Padding, allowing the model to ignore them during processing. This technique significantly improves the efficiency of our named entity recognition system.
Establishing a robust assessment pipeline
Rigorous performance evaluation is the final but critical component of our optimization pipeline for Named Entity Recognition (NER). Our experience in evaluation campaigns has shown us the importance of a systematic and methodical approach.
Essential valuation metrics
In our daily practice, we rely on three fundamental metrics to assess our NLP named entity recognition systems:
- Precision : Measures the accuracy of predictions, calculated as the ratio between correctly identified positives and all identified positives
- Reminder : Evaluates the ability of the model to identify all relevant entities
- F1 score : Represents the harmonic mean between precision and recall
Our analyses show that modern systems consistently achieve F-measure scores in excess of 90%, with performances peaking at 95% in recent campaigns, while human annotators maintain an accuracy level of around 97%.
Systematic performance tests
We have developed a careful approach to evaluating our named entity recognition (NER) models. Our assessment pipeline follows a three-step process:
- Using the Trained Model to Predict Entities on the Test Set
- Comparison with reference labels
- Detailed analysis of results and errors
To ensure the reliability of our evaluations, we generally repeat the execution of the evaluation pipeline 10 times for each NER tool. This approach allows us to measure performance variability and establish solid confidence intervals.
Continuous improvement of the model
Our continuous improvement strategy is based on in-depth error analysis and iterative optimization. We have found that in open conditions, without specific learning, even the best systems struggle to exceed 50% performance. By analyzing and understanding different topics, we can better focus our optimization efforts and improve the discovery of relevant information.
To continuously improve our models, we focus on:
- Enrichment of training data, especially for under-represented entity types
- Hyperparameter adjustment based on test results
- Cross-validation to identify potential biases
We use a confusion Matrix To identify entities that are often misinterpreted, which allows us to precisely target our optimization efforts. This systematic approach helps us maintain an effective continuous improvement cycle.
Possible applications
Entity recognition has many practical applications in a variety of fields. For example, it can improve the relevance of search engine results by identifying key entities in user queries. In text analysis, entity recognition makes it possible to extract valuable information from unstructured texts, thus facilitating data-based decision making. It is also used to classify texts into predefined categories, detect spam messages by identifying entities frequently used in these messages, and improve the quality of machine translation by recognizing entities that require specific translation. These applications show the importance and versatility of entity recognition in natural language processing.
Conclusion
Optimizing named entity recognition systems represents a complex technical challenge that requires a methodical and consistent approach. Our exploration of the five essential techniques shows that a successful optimization strategy is based on several fundamental pillars.
The quality of training data is the basis of any successful system. We have seen that advanced preprocessing, combined with accurate annotation techniques, can significantly improve results. The careful adjustment of the model parameters, supported by robust evaluation methods, helps us to achieve performances that are close to human capabilities.
Modern NER systems can now achieve F-measure scores in excess of 90% under controlled conditions. However, these results require constant work of optimization and improvement. Our experience shows that the success of an NER system depends on the systematic application of these optimization techniques, combined with continuous performance evaluation.
