What is Named Entity Recognition (NER)? Methods, Use Cases, and Challenges

Named Entity Recognition (NER) is a sub-task of information extraction in Natural Language Processing (NLP) that classifies named entities into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, and more. In the realm of NLP, understanding these entities is crucial for many applications, as they often contain the most significant information in a text.

Named Entity Recognition Explained

Named Entity Recognition (NER) serves as a bridge between unstructured text and structured data, enabling machines to sift through vast amounts of textual information and extract nuggets of valuable data in categorized forms. By pinpointing specific entities within a sea of words, NER transforms the way we process and utilize textual data.

Purpose: NER's primary objective is to comb through unstructured text and identify specific chunks as named entities, subsequently classifying them into predefined categories. This conversion of raw text into structured information makes data more actionable, facilitating tasks like data analysis, information retrieval, and knowledge graph construction.

How it works: The intricacies of NER can be broken down into several steps:

1. Tokenization. Before identifying entities, the text is split into tokens, which can be words, phrases, or even sentences. For instance, "Steve Jobs co-founded Apple" would be split into tokens like "Steve", "Jobs", "co-founded", "Apple".
2. Entity identification. Using various linguistic rules or statistical methods, potential named entities are detected. This involves recognizing patterns, such as capitalization in names ("Steve Jobs") or specific formats (like dates).
3. Entity classification. Once entities are identified, they are categorized into predefined classes such as "Person", "Organization", or "Location". This is often achieved using machine learning models trained on labeled datasets. For our example, "Steve Jobs" would be classified as a "Person" and "Apple" as an "Organization".
4. Contextual analysis. NER systems often consider the surrounding context to improve accuracy. For instance, in the sentence "Apple released a new iPhone", the context helps the system recognize "Apple" as an organization rather than a fruit.
5. Post-processing. After initial recognition and classification, post-processing might be applied to refine results. This could involve resolving ambiguities, merging multi-token entities, or using knowledge bases to enhance entity data.

The beauty of NER lies in its ability to understand and interpret unstructured text, which constitutes a significant portion of the data in the digital world, from web pages and news articles to social media posts and research papers. By identifying and classifying named entities, NER adds a layer of structure and meaning to this vast textual landscape.

Named Entity Recognition Methods

Named Entity Recognition (NER) has seen many methods developed over the years, each tailored to address the unique challenges of extracting and categorizing named entities from vast textual landscapes.

Rule-based Methods

Rule-based methods are grounded in manually crafted rules. They identify and classify named entities based on linguistic patterns, regular expressions, or dictionaries. While they shine in specific domains where entities are well-defined, such as extracting standard medical terms from clinical notes, their scalability is limited. They might struggle with large or diverse datasets due to the rigidity of predefined rules.

Statistical Methods

Transitioning from manual rules, statistical methods employ models like Hidden Markov Models (HMM) or Conditional Random Fields (CRF). They predict named entities based on likelihoods derived from training data. These methods are apt for tasks with ample labeled datasets at their disposal. Their strength lies in generalizing across diverse texts, but they're only as good as the training data they're fed.

Machine Learning Methods

Machine learning methods take it a step further by using algorithms such as decision trees or support vector machines. They learn from labeled data to predict named entities. Their widespread adoption in modern NER systems is attributed to their prowess in handling vast datasets and intricate patterns. However, they're hungry for substantial labeled data and can be computationally demanding.

Deep Learning Methods

The latest in the line are deep learning methods, which harness the power of neural networks. Recurrent Neural Networks (RNN) and transformers have become the go-to for many due to their ability to model long-term dependencies in text. They're ideal for large-scale tasks with abundant training data but come with the caveat of requiring significant computational might.

Hybrid Methods

Lastly, there's no one-size-fits-all in NER, leading to the emergence of hybrid methods. These techniques intertwine rule-based, statistical, and machine learning approaches, aiming to capture the best of all worlds. They're especially valuable when extracting entities from diverse sources, offering the flexibility of multiple methods. However, their intertwined nature can make them complex to implement and maintain.

Named Entity Recognition Use Cases

NER has found applications across diverse sectors, transforming the way we extract and utilize information. Here's a glimpse into some of its pivotal applications:

• News aggregation. NER is instrumental in categorizing news articles by the primary entities mentioned. This categorization aids readers in swiftly locating stories about specific people, places, or organizations, streamlining the news consumption process.
• Customer support. Analyzing customer queries becomes more efficient with NER. Companies can swiftly pinpoint common issues related to specific products or services, ensuring that customer concerns are addressed promptly and effectively.
• Research. For academics and researchers, NER is a boon. It allows them to scan vast volumes of text, identifying mentions of specific entities relevant to their studies. This automated extraction speeds up the research process and ensures comprehensive data analysis.
• Legal document analysis. In the legal sector, sifting through lengthy documents to find relevant entities like names, dates, or locations can be tedious. NER automates this, making legal research and analysis more efficient.

Named Entity Recognition Challenges

Navigating the realm of Named Entity Recognition (NER) presents its own set of challenges, even as the technique promises structured insights from unstructured data. Here are some of the primary hurdles faced in this domain:

• Ambiguity. Words can be deceptive. A term like "Amazon" might refer to the river or the company, depending on the context, making entity recognition a tricky endeavor.
• Context dependency. Words often derive their meaning from surrounding text. The word "Apple" in a tech article likely refers to the corporation, while in a recipe, it's probably the fruit. Understanding such nuances is crucial for accurate entity recognition.
• Language variations. The colorful tapestry of human language, with its slang, dialects, and regional differences, can pose challenges. What's common parlance in one region might be alien in another, complicating the NER process.
• Data sparsity. For machine learning-based NER methods, the availability of comprehensive labeled data is crucial. However, obtaining such data, especially for less common languages or specialized domains, can be challenging.
• Model generalization. While a model might excel in recognizing entities in one domain, it might falter in another. Ensuring that NER models generalize well across various domains is a persistent challenge.

Addressing these challenges requires a blend of linguistic expertise, advanced algorithms, and quality data. As NER continues to evolve, refining techniques to overcome these hurdles will be at the forefront of research and development.

Building Resume Analysis Using NER

In this section, we will learn how to create a system for analyzing resumes that helps hiring managers filter candidates based on their skills and attributes.

Importing necessary packages

• For entity recognition, we will use spaCy.
• For visualization, we will use pyLDAvis, wordcloud, plotly, and matplotlib.pyplot
• For data loading and manipulation, we will use pandas and numpy.
• For stopwords and word lemmatizer, we will use nltk.

Loading the Data and NER model

We will begin by loading a CSV file that includes a unique ID, resume text, and category. Then, we will load the spacy "en_core_web_sm" model.

Entity Ruler

First, we need to add an entity ruler pipeline to our model object. Then, we can create an entity ruler by loading a JSON file that contains labels and patterns such as skills like ".net", "cloud", and "aws".

Clean the Text

In this section, we will clean our dataset using the NLTK library by following a few steps:

1. Remove hyperlinks, special characters, or punctuations using regex.
2. Convert the text to lowercase.
3. Split the text into an array based on space.
4. Lemmatize the text to its base form for normalization.
5. Remove English stop words.
6. Append the results into an array.

Entity Recognition

After adding a new pipeline to our model, we can visualize the named entities in our text using spaCy's display function. By passing the input text through the language model, you can highlight the words with their labels using displacy.render(obj, style="ent", jupyter=True, options=options).

Match Score

Let's match resumes with company requirements. The system shows the most similar resumes based on a similarity score. For example, if a company is looking for an AWS cloud engineer, it will display the most relevant resumes.

How do we get a similarity score?

We need to create a Python function that extracts skills from a resume using the entity ruler, matches them with required skills, and generates a similarity score. This application requires a simple loop and if-else statement. Hiring managers can use this to filter candidates based on skills instead of reading multiple PDFs.

Want to learn more about AI and machine learning? Check out the following resources:

• Introduction to NLP in Python
• NLP tutorial
• Learn ChatGPT course
• What is prompt engineering?