Text Analytics Solutions from the Pioneer

Our Text Analytics using Python helps produce structured data, metadata & insights by extracting data, summarizing information and classifying content (even as a sentiment analysis tool).

Text Extraction

Value Proposition

Automating text extraction from PDFs, images and websites to structure the unstructured data.

Functions (Use Cases)

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Extract tabular and peripheral data from PDFs

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Extract alternative data from websites and APIs

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Features

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No manual template designing needed. Deep Learning methods detect the tabular areas and OCR them as tabular data. Sequential text analytics in NLP detect the entities (batch number, issue date etc.) across document irrespective of their position

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Customise the extracted output to a XLSX, CSV, JSON, XML file or write to a database

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Supports a variety of languages European and Asian languages

Tech Stack

Text analytics using Python libraries are used for extraction and structuring.

Document Type

PDF

Library Used: Tabula, Camelot, Tensorflow, Keras, Pytesseract

Document Type

Images

Library Used: OpenCV, Tensorflow, Keras, Pytesseract

Document Type

Websites

Library Used: BeautifulSoup, Scrapy, Selenium

Text Summarization

Value Proposition

Get a bite-sized summary of huge TL; DR documents (books, articles, journals, reviews, tweets, comments, legislation).

Functions (Use Cases)

Extract-key-Phrases

Extract key phrases defining pain points and popular features from reviews

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Extract batch number, issue date, email ID, org name etc., irrespective of their position

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Cluster similar documents together for cataloguing purposes w/o reading them

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Extract hidden topics from the documents

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Create a knowledge graph showing the important subject, predicate and their relation

Features

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No manual keywords or topics entry to be extracted

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Choose to see the output at an aggregate level or at an individual document level

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Easy integration to a document feeder API for downstream summarization

Tech Stack

Use Case

Key Phrase Extraction

Python Library: NLTK, re, scikit-learn, Pycrfsuite, Keras

Algorithm: Dependency Parsing, POS based Grammar Chunking, TF-IDF

Use Case

Topic Modeling

Python Library: scikit-learn

Algorithm: Non-Negative Matrix Factorization

Use Case

Clustering

Python Library: scikit-learn, ELMo

Algorithm: k-Means, tSNE

Use Case

Knowledge Graph

Python Library: NetworkX, spaCy

Algorithm: Dependency Parsing, Network Analysis

Use Case

Tagging / Entity Recognition

Python Library: Pycrfsuite, Tensorflow, Keras

Algorithm: Conditional Random Field, LSTM

Text Classification

Value Proposition

Assign a category to each document (books, articles, journals, reviews, tweets, comments, legislation).

Functions (Use Cases)

tex-ai-email-data

Classify Emails / SMSs as Spam or Ham for a telecom company

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Overall sentiment analysis, intent of purchase identification for a marketing function of CPG

tex-ai-product-categorization

Hierarchical Product categorization for an E-commerce company

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Features

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Ready-made pre-processing and training pipeline for any kind of labelled text data

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As high as 10,000 categories supported for classification

Tech Stack

Use Case

Text Classification

Python Library: Genism, SpaCy, scikit-learn, Tensorflow, ELMo

Algorithm: TF-IDF, Word2Vec, Naïve Bayes, SVM, Neural Networks

Domains

Request a Demo

Schedule a demo with our experts and unlock the potential of your text!

A Few Examples of our Success

Quick Links

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Demo

See how teX.ai can help you glean insights from your existing text data.

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Brochure

For a quick glance of the advantages teX.ai can offer you, download our brochure now!

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FAQs

Our carefully curated FAQs will help address all questions you have about teX.ai!

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