Summarization is about creating short and accurate summaries of large text. TextSense™ uses machine learning techniques to create a subset of text which contains the important information about entire text.
The focused summaries can be obtained by both extractive as well as abstractive summarization techniques.
Sentiment analysis is a process of computationally extracting and classifying emotions as positive, negative and neutral. TextSense™ uses natural language processing and machine learning techniques to assign weighted scores to sentences in given text.
The TextSense™ API can be used to analyze public opinion, conduct market research, understand customer experiences and monitor social media.
Entity relation modeling
Named Entity Recognition ( NER ) identifies named entities that are present in the given text and classifies them as people, organizations, geographical locations, dates etc.
TextSense's NER API can be used in various domains. Examples - It can proof-read news articles, classify news content, build knowledge graph, understand customer sentiment etc
Unstructured to structured data
TextSense™ uses NLP techniques to convert un-structured data to structured data. It can be customized to solve a particular customer need.
Our out of box solutions can be used to convert resumes, medical reports, path-lab reports to JSON/XML formats.
Automatic identification of terms that best describe the subject of a document.
Marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context. E.g: nouns, verbs, adjectives, adverbs, etc.
A process to automatically identify topics present in a text object and to derive hidden patterns exhibited by a text corpus.
A task of finding all expressions that refer to the same entity in a text. Eg. "I voted for Modi because he[Modi] was most aligned with my values", she said.
Searching which not only aims to find keywords, but to determine the intent and contextual meaning of the words a person is using for search.
Grouping the documents retrieved from search results into a list of meaningful categories.
TextSense™ Client's Applications
Lab Report Parsing
Uncover data from unstructured pdf for data analytics.
Make sense of medical text by identifying signs & symptoms, anatomical sights, diseases, procedures and medicines.
Generating Patient Discharge Summary from Electronic Health Records using NLP.
Title Generation service generate the suitable title for given news articles in japanese language.
ThaiOCR extracts text from thai images and get data in structured format.
Signature verification service that compares signatures and checks for authenticity. Returns the confidence score against the signature to be verified.
At AlgoAnalytics, our team of experts, meet challenges in the field of ML and AI with a zeal and passion to find simple solutions. We work across domains like healthcare, retail, finance and the legal space. Our expertise covers text, image and video analytics.
Our Text analytics platform, TextSense™ is powered by NLP that enables a wide variety of applications across the various domains.
Healthcare domain - TextSense™ enables for easier streamlining of work for doctors providing for Discharge summaries, unburdening the tedious administrative work.
Legal - TextSense™ platform provides a more digital and faster way to access the database. With precision, our platform delivers the most recent and relevant information.
Media & Research: TextSense™ platform summarizes long news reports and research articles, enabling better readability of the core matter.
With TextSense™ we thus bridge the gap between Textual content and Structured knowledge. Together, we now “Make more Sense with TextSense™”.