Tech Deep Dive: Topic Modelling With The Analyze Tool
In our ongoing Tech Deep Dive series, we've explored various facets of Iris.ai's technological arsenal designed to enhance scientific research. Today, we’ll explore how Iris.ai’s Analyze tool – a component of the RSpace™ – applies topic modeling techniques to transform the way researchers handle scientific literature. Learn how this AI-powered solution automates concept extraction, enhances filtering, and accelerates literature reviews across millions of academic documents. Stay updated by subscribing to the newsletter!
What Is the Analyze Tool in RSpace™?
The Analyze tool is a part of the RSpace™ - an AI-based platform for scientific research. If you have used our (soon retiring) Explore & Focus tools in the past, you will find some similarities between the Focus and Analyze tool.
In a nutshell, the Analyze tool reads through the titles and abstracts of the documents in the dataset and generates key concepts, keywords and topics. Generated words and topics can then be used to easily filter through a long reading list to receive more focused and relevant results. This advanced tool delves into large data pools – from a staggering 150 million document database – to tailored article collections. These insights enable researchers to filter through extensive reading lists efficiently, focusing their efforts on the most relevant materials.
Deep Dive into the Technology of the Analyze Tool
Understanding the inner workings of the Analyze tool reveals a complex yet fascinating process involving dynamic learning of several key elements of a document set simultaneously. The analyzing process includes iterative adjustment of topics, classification of documents and generation of key concepts and key words.
Topics are formulated as a combination of key concepts and words describing the documents classified in the analyzed documents set. Concept is an abstract meaning representing a cluster of contextual synonyms (usually defined and expressed in a vector space). While keyword is the concrete word used in the document that represents a unique type of information.
Topic Modeling
At its heart, the Analyze tool uses topic modeling techniques to sift through vast amounts of data. Topic modeling is a type of statistical model for discovering abstract topics within a collection of documents. Firstly, the machine determines which keywords are most probable to appear together to create a topic. Then the tool analyzes the frequency, distribution and contextual relevance of words across the documents to identify potential concept clusters. This process isn't static; it dynamically adjusts as new data is fed into the system, allowing the model to evolve and refine its understanding. At the same time, the machine analyzes each of the documents, trying to categorize them into the created clusters using document classification. The well formed concept clusters together with the classified documents in them are what we call information topics.
Document Classification
Each document is analyzed based on its textual content to determine its relevance to identified topics. The tool assesses the probability of a document belonging to each identified topic, allowing documents to overlap across different themes. This means that a single document can contribute to multiple topics based on its content, providing a nuanced understanding of the dataset.
Iterative Learning and Adjustment
To sum it up, the machine starts with random words and their probability to appear together to create the topics. As the machine processes new documents, the tool continuously adjusts the definitions and boundaries of topics. Meanwhile, trying to categorize the documents to specific topics. This iterative process ensures that the topic modeling remains accurate and reflects the content present in the dataset. Each new document introduced to the system helps adjust and improve the topic models, refining the keywords and concepts associated with each topic.
Probabilistic Topic Assignment
The tool uses a probabilistic approach to assign documents to topics. This means that instead of rigidly categorizing a document under a single topic, it acknowledges the possibility that documents may share elements with multiple topics. The probability distribution allows one document to be a part of multiple topics.
Word Analysis
Besides creating a Topic list, the Analyze tool creates Keywords and Concepts lists, for advanced filtering of the documents.
At the core of the Analyze tool's efficiency is its ability to create and refine a 'fingerprint' for each document. This fingerprint is a unique identifier derived from the most relevant concepts within the document. It isn’t just about finding keywords but understanding their context within the specific domain, making the results both precise and contextually enriched.
Concepts are representative of contextual clusters of synonymous words in the documents and essentially they are the base unit for the fingerprints. Concepts are being rated on how rare they are in a particular domain and how common they are in selected dataset in order to be helpful in the filtering process. They are ranked based on TF-IDF rank. The drop down menu in the concept analysis filters, shows top highly ranked words. These words are specific for the domain, prominent in the fingerprints but they are rare in general English. Picking rare words with the least ambiguous meaning aids in avoiding words with multiple meanings and helps in efficient filtering and search.
Keywords are just exact words present in the dataset.
With the concepts and keywords the researchers can use Boolean operators (AND or OR) and choose the words with either inclusion or exclusion.
How Researchers Benefit from AI-Powered Filtering
The practical applications of the Analyze tool are vast. Researchers can use this tool to quickly get an overview of a new dataset, identifying key areas of focus without prior knowledge of the content. This bottom-up approach not only saves time but also enhances the accuracy of research by highlighting the most significant and recurrent themes.
What’s Next for the Analyze Tool?
As Iris.ai continues to refine its technology, future versions of the Analyze tool will incorporate more sophisticated algorithms for better accuracy and usability. Enhancements such as real-time learning capabilities, showing top concepts and visual graphs, integration with new types of data, and more intuitive user interfaces are on the horizon.
Conclusion
The Analyze tool represents a significant leap forward in how we handle and interpret large volumes of academic literature. By automating the extraction of topics and concepts and continually refining these insights, Iris.ai helps researchers not only keep pace with but also lead in their respective fields.
Ready to transform your research process with AI-powered topic modeling? Book a demo of RSpace™ today! Don’t forget to subscribe to our newsletter for future tech deep dives and product updates.