What is Named Entity Recognition (NER)?
Named entity recognition, also referred to as NER, is a concept in natural language processing (NLP) that categorizes entities from a text, such as names of people, organizations, locations, or product names. By enabling systems to extract structured data from unstructured content, NER can power multiple applications from chatbots to document summarization tools.
As a subfield of NLP, NER works to bridge the gap between human speech and machine understanding. When systems are capable of processing speech and text in context, the results are more precise AI-driven language solutions.
In this post, we’ll take a closer look at the components behind named entity recognition, including its benefits, challenges, and some examples of how it’s used in real-world applications such as aiOla speech AI technology.
How Named Entity Recognition Works
By identifying specific data points such as entities within speech, NER can improve the way AI processes and uses text-based data, providing a layer of context and insight into otherwise unstructured data. To accomplish this, NER systems rely on a series of steps to accurately pinpoint and categorize entities within a text. Here’s a breakdown of what it looks like:
Step 1: Data Collection and Annotation
The process begins by gathering a dataset of annotated text. These annotations serve as labels for specific words within a text to determine their corresponding categories, such as “Person” or “Location.” This dataset can be created manually or automatically through pre-trained models.
Step 2: Data Preprocessing
Next, the text is prepared for analysis. This includes cleaning it up to remove unnecessary data, standardizing text formats, and splitting text into tokens. Tokenization breaks text into smaller units, like words or phrases, making entity identification easier.
Step 3: Feature Extraction
Features that provide meaningful insights get extracted from the text. For example, these features can include part-of-speech (POS) tags or context clues. These features help models discern patterns in the text for accurate entity recognition.
Step 4: Entity Identification
Using linguistic rules, statistical models, and machine learning (ML), potential named entities get detected. For instance, specific formats like dates or capitalization patterns may help identify entities.
Step 5: Entity Classification
Once identified, entities are organized into predefined categories, such as “Person,” “Organization,” or “Date.” ML models trained on labeled data can handle this step, relying on features and context to enrich accuracy.
Step 6: Contextual Analysis
NER systems incorporate contextual information to refine results. For example, “Apple” could refer to a fruit or a company, so analyzing the surrounding text will help distinguish the context.
Step 7: Post-Processing
The output undergoes refinement to fix ambiguities, merge or link entities, or enhance data with additional information from knowledge bases. This final step ensures that the results are accurate and actionable.
Benefits of NER for users and businesses
NER offers significant advantages for both users and businesses by transforming structured text into actionable insights. To better understand the implications of NER in natural language processing and its effects, let’s take a look at some key benefits.
- Improved data retrieval: NER enables quick identification of relevant entities, simplifying searches within large datasets or documents
- Enhanced data analysis: Categorizing unstructured text into structured formats helps detect patterns, trends, and correlations, leading to more well-rounded decision-making
- Streamlined automation: NER can help to automate workflows and tasks such as document classification, email sorting, and chatbot functions, leading to more accurate and efficient operations
- Cost and resource reduction: Decreases reliance on manual data processing, saving time and resources while boosting productivity
Challenges and Limitations of Named Entity Recognition
While NER does make NLP applications more accurate and reliable, the technology comes with its own unique challenges. As with any technology, it has its limitations. Here are some challenges and limitations of NER to consider if you’re looking at implementing this technology:
- Language variations: Handling multilingual text or regional dialects can be complex, requiring models trained on diverse datasets while currently NER models have been primarily trained on English text
- Data sparsity: The limited availability of labeled data for machine learning purposes for specific languages or domains can inhibit named entity recognition model performance
- Ambiguity and context dependency: Words or phrases with multiple meanings (e.g., “Apple”) can be challenging without the right context to result in accurate entity classification
- Model generalization: Models trained on one dataset may struggle to perform well on unseen datasets, especially with different text styles or content
- Domain-specific entities: Identifying entities unique to specialized fields, such as medical or legal terms, requires domain-specific training and expertise
- Computational complexity: Training and deploying NER models, especially deep learning-based ones, require significant computational resources that may not be realistically available to every organization
Named Entity Recognition Examples and Use Cases
NER has a variety of use cases in different industries, making it a versatile tool. To get a better idea of how this technology can work in action and be applied to various workflows, let’s take a closer look at some NER use cases.
Customer Service
NER can help companies route customers to the right agent and pinpoint precise needs. For example, NLP systems that use NER can help identify specific products customers are having trouble with to route them to the right agent and be more proactive when it comes to problem-solving.
Legal Documentation
Sorting through hundreds or thousands of pages of legal documentation can be a time-consuming affair. With NER, systems can help legal teams identify important dates, companies, and names much quicker.
News Aggregation
News aggregators turn to NER systems to categorize stories based on key entities’ names in titles and texts, making it easier to group similar stories. This makes it simpler and quicker for readers to locate the news stories they’re searching for.
Virtual Assistants
Virtual assistants and chatbots can use NER to better understand user inquiries in context. For example, if a user searches “Flights from New York to Paris on December 15th,” the NER system will be able to identify the names of the destinations and the dates and provide accurate search results.
aiOla: Domain-Specific Entity and Speech Recognition
There are a few NER applications out there that can help organizations with entity identification in text, such as the Natural Language Toolkit and The Standford Named Entity Recognizer. However, these tools alone aren’t always enough to help organizations better manage their workflows. For that, you need a complete speech AI solution, such as aiOla.
aiOla is a speech AI technology that empowers frontline workers to complete actions and collect critical data entirely through speech. With its ability to understand over 120 languages including domain-specific jargon and entities, aiOla can deliver accurate results every time, helping teams work more collaboratively and productively.
Combining the power of natural language understanding (NLU) with automatic speech recognition (ASR) and other systems, aiOla delivers reliable results in real time. With aiOla speech AI, companies can improve specific workflows and metrics such as:
- Cutting down on inspection time
- Collecting data in real-time to inform better decision-making
- Execute processes quicker through speech
- Improve safety during various work processes
- Reduce compliance risk by keeping frontline workers focused on their tasks
With aiOla, companies can transform words into actions without worrying that important names, dates, or product names are going to be lost in translation. Since aiOla can listen and decipher real speech instantly, workers can continue their processes uninterrupted while making sure aiOla does the heavy lifting to automate tasks and collect important data.
Looking Ahead With Named Entity Recognition
In the future, we can expect NER technology to become more predominant in a wider variety of fields, with new models and NER systems popping up as well as integrations with existing text and speech systems. As the demand for accurate and context-aware speech technology such as aiOla grows, so too will NER’s capabilities and applications, making it easier to integrate NER with other NLP workflows.
