Convert Your Text into Insights With AI

Information extraction from the given text is one of the most valuable and advanced feature offered by our text analytics AI. It identifies key phrases and relationships within the text. OSP’ artificial intelligence text analytics does this by looking for predefined sequences in a text, a process usually called pattern matching, typically based on regular expressions. The named entity recognition (NER) seeks to locate and classify atomic elements in text into predefined categories that usually match with pre-established ontologies. NER techniques extract features such as the names of persons, organizations, places, quantities, monetary values, temporal or spatial expressions, stock values, percentages, protein or gene names, etc.

Text summarization offered by the artificial intelligence text analytics falls under the natural language generation. It helps in figuring out whether or not a lengthy document meets the user’s requirements and is worth reading for further information. With comprehensive texts, text analytics AI software solutions can process and summarize the document in the time it would take the user to read the first paragraph. The key to summarization is to decrease the length and detail of a document while retaining its main points and overall meaning. The text analytics AI solutions statistically weigh and rank important sentences from an article. Summarization tool can be customized to search for headings and other markers of subtopics in order to identify the key points of a document. OSP’s text analytics AI solutions offers powerful text summarization that can be classified into two broad

groups:

• Shallow analysis: It is restricted to the syntactic level of representation and try to extract significant parts of the text.

• Deeper analysis: It assumes a semantics level of representation of the original text.

Grouping similar documents together are called clustering. For clustering, understanding these documents is a vital step. It differs from the classification as the classification offered text analytics AI is based on the supervises machine learning. Classification requires previous knowledge to assign a given document to a presented category. Clustering module in text analytics AI software is based on unsupervised learning: there are no previously defined topics or categories. Using clustering, documents can appear in various subtopics, thus ensuring that a valuable document will not be omitted from search results. A basic clustering algorithm in our text analytics AI solutions creates a vector of topics for each document and assigns the document to a given topic cluster. Medicine and Legal research papers have been a prolific ground to apply text clustering techniques offered by text analytics AI.

OSP’s text analytics AI has inbuilt concept linkage tools that can connect related documents by recognizing their commonly-shared concepts. The artificial analytics text analytics can also help users find the data that they perhaps would not have discovered using traditional searching methods. It encourages browsing for information rather than searching for it. In the fields like legal and biomedical, where there is a huge amount of research is done and a pile of textual data seems overwhelming. Pir tailored text analytics AI software has concept linkage –  a valuable concept in text mining. OSP’s text analytics AI has the search tool for finding related and possibly unfamiliar concepts that lie on a path between two well-known concepts. The tool searches semistructured data in knowledge repositories which are based on finding previously unknown concepts.

Entities like the people, organizations, places, and things are the key players in your text data. Using a synthesis of established text analysis best practices and machine learning statistical modeling, our text analytics AI solutions uncover these entities. Our text analytics AI can deliver the structure, clarity, and insight to your content. OSP can customize the text analytics AI software solutions for you to resolve entity ambiguity by finding the synonyms and connecting information about an entity within and across documents. Our artificial intelligence text analytics has the feature to add metadata from the linking process to your text. It provides the accuracy and prediction confidence measures to boost the entity-centric search and notification. Our text analytics AI can track new entities in text streams, and create custom knowledge graphs.