Natural Language Processing

Applications

What do we do?

• Quantify the opinion trend and detect the polarity of the opinion
• Present computational methods to analyze and summarize opinions
• Determine the user’s intention from logs or other forms of interaction with the computer (intention mining)
• Identify instances of fake opinion and prevent the analysis from being compromised by such fake opinions (fake-opinion detection)

What do we do?

• Automatic translation from one language to another
• Conduct vigorous research on machine translation approaches depending on the unique needs of the client and domain specificity
• Run strictly text string-based machine translation backed by Deep Learning framework such as RNN—a mostly probabilistic approach not accounting for semantic components
• Run the competing machine translation model using phrase-based or syntax based approaches based on the latest computational linguistics theories

What do we do?

• Discover hidden or embedded topics that are present in a collection of natural language documents
• Use Dirichlet Allocation (LDA) or other unsupervised learning methodologies to identify some key topics on syntactic or semantic or frequency basis
• Automatically impose the structure on unstructured text bodies accurately enough and fast enough

What do we do?

• Identify what image points to capture and Divide the image into distinct partitions for more detailed image representation
• Extracting the stored data sources images and shapes
• Automatically interpret and organize the extracted contents and convert them to numbers for analytics practices
• Improve and Enhance Image quality

What do we do?

• Create a robust model for recognizing human speech considering all different possibilities of noise, physical environment, languages, speakers, and speaking styles
• Incorporate solid academic understanding and researches on phonetics—from the human aspect—and acoustic—from physical properties of the signal
• Feature normalization to take care of removing convolutional and channel distortions
• Rapid model adaptation to reduce uncertainties on various perspectives
• Can be integrated with other NLP tasks (e.g. Chatbot, speech-to-text conversions, etc.)

What do we do?

• Understand the audience and business
• Understand what the user is seeking
• Integrate all aspects of NLP task to classify the intent of the customer
• Build a response mechanism based on the engagement with the customer
• Engage with appropriate emotion, confidence, and friendliness
• Using rosa_nul, rosa_core, python, tensorflow, keras libraries

Data Architecture

Architecture

• Define the logical layers and components of the existing data architecture.
• Understand the atomic patterns.
• Understand composite (or mixed) patterns for data solution.
• Choose a solution pattern and the right implementation tool.
• Devise a Data Strategy, outlining business aims and objectives for improved collection and use of data.
• Design Data Integration, Data Warehousing, and Reporting strategies

Data Pre-Processing

How JMI Approaches the unstructured data?

Why is Data Preprocessing Important to your business?

Data Preprocessing is required because of the enormous unformatted real world data. The real-world data is mostly composed of

• Missing Data – There are lots of reasons why there is lots of missing data. Some of them include mistake in a manual data entry or a technical problem with biometrics and much more.
• Noisy Data – One the main reason why there is noisy data is could be due to a technical problem of a gadget or a human error due to manual data entry.
• Inconsistent Data – Inconsistency of data are due the existence of duplication within data, mistakes in a name or code, human error due to manual entry, violation a data and much more

Services we offer

• Data Import - Data Importation from structured and unstructured sources
• Web Scrapping - Web Scrapping of public data, Images and text extractions, excel rows and columns
• Big Data Processing - Big Data Processing using Hadoop Ecosystem, Apache Spark, Rapid Miner, and Cassandra
• Data Scrubbing - Data Scrubbing and Cleaning take care of missing values, inconsistent data, anomalous data, and outliers.
• Data Transformation - Data Transformations like extractions, parsing, joining, standardizing, augmenting, cleansing, consolidating and filtering to create desired wrangling outputs. We even perform feature engineering to obtain new predictors.

Reporting & Dashboarding

Services we offer

• Consolidating facts and figures in a document with data snippets and visuals.
• Identifying the audience and creating the outline based that in mind will be the first step.
• Gathering data, writing the analysis, highlighting the key points
• Reports can be made to showcase high-level facts (for the management) and detailed description (Technical).

Our Expertise in Reporting and Dashboarding

Data Visualization – Organizing and arranging data for visual communication. It can be articulated in Time Series, Ranking, Part-to-whole, Deviation, Frequency Distribution, Nominal comparison, and Geospatial distribution. We provide visualization:

• Displaying data in more sophisticated ways such as infographics, geographic maps, sparklines, heat maps, detailed bar charts, and pie charts
• Creating visually appealing dashboards with all key performance indicators (KPIs) relevant to the business objective.
• Creating Data Dashboards to answer important questions and project real-time data continuously.

Reporting – Consolidating facts and figures in a document with data snippets and visuals.
We provide a report with high-level facts for the management and provide a detailed description for Technical Understanding.