In the digital age, data proliferates at an astonishing rate. From news articles to social media posts, the information explosion presents unique challenges in processing and understanding content accurately. One significant challenge is distinguishing between entities with similar or identical names in different contexts. named entity disambiguation (NED) is a sophisticated technology within natural language processing (NLP) aimed at tackling this issue. This technology ensures that when you search for "Orange," the results accurately reflect whether you meant the color, the fruit, or the multinational corporation. This article explores the concept of NED, underscores its importance, and elaborates on how SESAMm employs this technology to stand out from other companies in the artificial intelligence (AI) landscape.

What Is Named Entity Disambiguation?

Named Entities: Defining the Basics

In data science and text processing, a named entity is defined as any real-world object that can be denoted with a proper name. This includes people like "Elon Musk," companies like "Google," and landmarks like "Mount Everest." These entities are distinct because they refer to unique individuals, organizations, or locations, unlike common nouns such as "manager" or "river," which are non-specific and can refer to many different entities globally.

Named Entities: Defining the Basics

Named Entity Disambiguation, also known as entity linking, involves identifying which specific entity is referred to in an unstructured text when there are multiple candidates with similar names. This process utilizes a blend of machine learning, knowledge graphs, and other sophisticated NLP algorithms to analyze the text and determine which entity type is relevant in the given context. This determination is important because it affects the interpretation and subsequent processing of the information.

The Importance of Named Entity Disambiguation

The role of NED in text analysis and information processing cannot be overstated, particularly when dealing with large and complex datasets. It enables:

• Refined text analytics: For tasks like sentiment analysis, precise entity recognition ensures that emotions or sentiments are accurately associated with the right entities. This is crucial for businesses to understand public perceptions of their products or services accurately.
• Efficient construction of knowledge graphs: Knowledge graphs that organize and link real-world information rely heavily on NED to accurately populate and update their data. This accuracy is essential for applications like digital assistants, which use these graphs to provide informed responses to user inquiries.

The Importance of Named Entity Disambiguation

NED is a complex process that involves multiple steps and methodologies to accurately identify and link named entities in a given text to their correct real-world counterparts.

1. Identifying Named Entities

Before disambiguation can occur, named entities must first be identified within a text. This is typically done using named entity recognition (NER), a preliminary step that involves scanning text data to locate and classify entities into predefined categories such as person names, organizations, locations, dates, and other specific information.

Techniques Used in NER

• Rule-based systems: These utilize patterns and linguistic rules, such as capitalization or context indicators (e.g., titles like Mr. or corporate designators like Inc.), to identify entities.
• Statistical methods: Techniques like Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs) learn from large datasets of annotated text to recognize entities based on probabilistic models.
• Deep learning approaches: More recently, models based on neural networks, particularly those using architectures like LSTM (Long Short-Term Memory) or transformers, have become prevalent. These models benefit from large amounts of training data and have shown superior ability to capture context for more accurate entity recognition.

2. Categorizing Named Entities

Once entities are identified, they need to be categorized accurately. This involves classifying each entity according to its type, which helps in narrowing down the possible meanings in the subsequent disambiguation step.

Methods for categorization

• Fine-grained classification: Beyond basic categories, entities can be classified into more specific classes, such as distinguishing between types of organizations (e.g., non-profit vs. corporate) or public figures (e.g., politician vs. artist).
• Contextual classification: It involves analyzing the surrounding text to understand an entity's role and relevance, using both the immediate context and broader discourse.

3. Disambiguating Named Entities

The core of NED lies in its ability to distinguish between entities that share the same name. This step is critical because it determines the accuracy of information extraction, search engines, knowledge graph construction, and other NLP applications.

Core Techniques in Disambiguation

• Rule-based disambiguation: Applies heuristic rules based on linguistic cues and patterns, such as geographical proximity or typical associations (e.g., Apple might be linked to "technology" if the context involves words like "iPhone" or "MacBook").
• Machine learning models: Supervised learning models are trained on datasets where each entity is annotated with its correct reference. These models learn to predict the correct entity based on features extracted from the context.
• Unsupervised and semi-supervised methods: These involve clustering similar entities and using algorithms to predict the most likely meaning based on the densities of clusters and the contextual similarity.
• Knowledge-based approaches: Utilize large external databases or knowledge graphs that contain information about entities and their relationships. By querying these resources, NED systems can pull contextual information and metadata to resolve ambiguities. For example, linking to a specific Wikipedia page can clarify whether "Jordan" refers to the country, the river, or the basketball player, based on the context.

4. Linking Entities to External Databases or Knowledge Graphs

The final step in NED is often linking the disambiguated entity to a unique identifier in an external database or a node in a knowledge graph. This linkage not only confirms the entity’s identity but also enriches the text with semantic information that can be used for further processing and analysis.

Linkage methods

• URI Assignment: Each entity is assigned a unique resource identifier (URI) that points to a specific location in a database or a knowledge graph.
• Semantic tagging: Entities are tagged with semantic labels that provide additional metadata, enhancing the richness of the data for subsequent analytical tasks.

The combination of these techniques ensures that NED systems can operate with high accuracy and efficiency, making them indispensable in the field of NLP. By understanding and implementing these processes, SESAMm enhances its analytical capabilities, offering precise and context-aware solutions that stand out in the competitive AI landscape.

SESAMm's Innovative Approach to NED

SESAMm has carved a niche in the NLP field by incorporating advanced, proprietary technologies that refine and enhance the NED process:

• Cutting-edge algorithms: SESAMm develops and deploys state-of-the-art machine learning approaches and deep learning algorithms designed to increase the precision and reliability of entity disambiguation.
• Scalable data processing: SESAMm's platforms are engineered to handle extensive data volumes, making them well-suited for large-scale industrial applications that require robust data analysis capabilities.
• Customizable APIs: SESAMm offers adaptable APIs that clients can tailor to fit specific project requirements, whether for financial analysis, marketing research, or other specialized areas.
• Seamless knowledge graph integration: By integrating its NED processes with dynamic knowledge graphs, SESAMm enhances its semantic analysis capabilities, enabling deeper insights and more accurate data interpretations.

Conclusion

Named Entity Disambiguation is a fundamental component of modern NLP applications, essential for interpreting the enormous volumes of data generated daily. By accurately identifying and categorizing named entities, NED not only deepens the understanding of text but also improves the efficiency of information processing. SESAMm's approach to NED sets it apart in the AI analytics field, pushing the boundaries of what's possible with smart, context-aware technology solutions. To learn more about SESAMm’s innovative technology and how it is used to identify ESG controversies, request a demo.

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BNP Paribas has released its 2025 ESG Global Survey, revealing how institutional investors are adapting their sustainability strategies in the face of shifting market dynamics, rising regulatory scrutiny, and increased public skepticism. The report surveyed 420 asset owners, asset managers, and private capital firms across 29 countries, representing a combined $33.8 trillion in assets under management, and paints a complex but ultimately optimistic picture of ESG’s evolution.

Resilient Commitment Amid a Changing Landscape

Despite growing political backlash and accusations of greenwashing in various markets, investor commitment to ESG remains strong. A large majority, 87%, said their ESG or sustainability objectives have remained stable, and fewer than 3% expect to scale back their commitments. Furthermore, 84% anticipate that their organizations will continue to progress on sustainability goals through 2030.

However, this confidence is paired with a new sense of caution. Forty-one percent of respondents reported a more restrained approach to publicly promoting their ESG activities. This shift reflects broader concerns about reputational risk and regulatory ambiguity, particularly in markets where ESG has become politicized. Still, the underlying momentum toward long-term ESG integration appears undeterred.

From Broad ESG to Thematic and Impact Strategies

The report also signals a shift from general ESG investing toward more targeted strategies, especially thematic investing. Eighty-five percent of participants now apply sustainability criteria to investment decisions, and 59% actively pursue thematic strategies such as climate resilience or social equity.
Top investment priorities over the next two years include increasing allocations to the energy transition, divesting from carbon-intensive assets, and using active ownership to push portfolio companies toward improved ESG outcomes. This thematic focus suggests a maturation of ESG strategies, with more precise goals and performance expectations.

Emergence of ESG “Pacesetters”

A standout insight from the survey is the rise of “pacesetters,” the 19% of respondents that have achieved the highest levels of ESG integration. These advanced investors have already embedded ESG factors across portfolios, including metrics related to social impact, biodiversity, and “The Just Transition Mechanism”. Nearly all pacesetters are actively decarbonizing their portfolios and report alignment with broader societal and environmental goals.

This group is not only more sophisticated in managing ESG risk but also more likely to view ESG as a long-term value driver. Their practices highlight what comprehensive ESG adoption can look like when paired with sufficient resources, internal alignment, and robust data.

Private Capital’s Expanding ESG Role

Private capital managers are also emerging as key ESG players. More than half are using active ownership strategies, while 76% emphasize social impact, and 63% are engaged in just transition initiatives. These firms see ESG as an opportunity to generate alpha, align with stakeholder expectations, and lead in sustainability-driven innovation.

The Critical Role of Data and Partnerships

Finally, the survey underscores the growing importance of ESG data and trusted partners. Nearly half of all respondents expect to increase budgets for ESG data acquisition and analysis. When selecting financial partners, ESG reputation and expertise are increasingly decisive factors.

Overall, the 2025 survey shows that institutional investors remain deeply committed to ESG, but are evolving in how they execute and communicate these strategies. The future of ESG appears less about bold declarations and more about targeted, data-driven action.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

Discover our whitepaper highlighting the power of web data on predictive analytics for the financial industry: how alternative data strengthen the market, the challenges of collecting web data and case studies presenting different approaches, such as ESG, showcasing TextReveal features and capacities for investment purposes.

In complement, please find here our Recorded Webinar “Predict financial movements with web data”.

Introduction

In the past, investment management institutions relied mostly on traditional data to gain an edge in investing. Traditional data ranges from SEC filings to earnings reports and pricing information any type of data produced by the company itself. The rise of the digital age, however, has opened up new sources of data for investors beyond the scope of traditional data. The seemingly infinite scope of alternative data includes data produced from credit cards, satellites, social media and perhaps most importantly the web.

With the additional integration of alternative data, investment management institutions and hedge funds in particular that once relied only on traditional data now have an edge in predicting the rise and fall of the markets. As increasing numbers of financial institutions jump on the bandwagon of alternative data, spending on alternative data by trading and asset management firms is set to exceed $7 billion by 2020.^[1]

What was only a few years ago a question of when institutions should start using data has shifted to the question of how they can organize and structure these mostly unstructured datasets. And with 4 billion webpages and 1.2 million terabytes of data on the internet estimated to be generated globally by 2025, there is no shortage of web data to sort through. As increasing numbers of investment management institutions incorporate alternative web data into their predictive algorithms, it will change the face of investment as we know it.

This white paper is intended to be a guide for investment management (IMs) institutions to better understand how alternative web data is quickly becoming an essential component for generating alpha and mitigating investment risk. In addition, it explores different models of web data crawlers and what IMs need to look for as they incorporate alternative web data into their predictive analytics models.

Section 1: Beating the Market with Alternative Web Data

“Your company’s biggest database isn’t your transaction, CRM, ERP or other internal database. Rather it’s the Web itself…Treat the Internet itself as your organization’s largest data source.”
— Gartner

As previously mentioned, alternative data includes any type of data that is beyond the scope of traditional data: satellite imagery, social media data, and web data (which includes news sites, blogs, discussions and forums) along with credit card data. Alternative web data, which falls under the broader category of big data, is typically unstructured and demands a process for structuring it in order to deliver insights.

Access the full whitepaper