NLP Technologies Deployment
We process textual data to extract and structure information, making its meaning easily findable.
Our solutions bring intelligence to your language data, enabling more nuanced analysis and understanding of content and context.
Outcome – Better interpretation of text, making your language data simple and accessible
Semantic Search
Semantics refers to the philosophical study of meaning
Semantic search is a data searching technique that uses the intent and contextual meaning behind a search query to deliver more relevant results.
We encode the meaning of your text and match it to the meaning of your query. Semantic Search goes beyond traditional keyword matching in search engines. It understands the intent and context behind a user’s search query, not just the specific words used
User intent understanding
Semantic Matching
Going beyond keywords to match concepts, utilising vector search and machine learning for deeper query comprehension
Contextual relevance
Interoperable Formats
Using formal semantics and interoperable formats for universal data understanding, facilitating cross-system exchanges
Information Extraction
Transforming an unstructured text or a collection of texts into sets of facts (formal, machine-readable statements)
Automatically extracting structured information from unstructured and/or semi-structured machine-readable documents and other electronically represented sources to enable finding entities as well as classifying and storing them in a database
Pre-processing of text
Preparing the text for processing with the help of computational linguistics tools such as tokenization, sentence splitting, morphological analysis, etc
Unifying the concepts
Identifying relationships between the extracted concepts, and presenting them into a standard format
Finding and classifying concepts
Detecting and classifying mentions of people, things, locations, events, and other pre-specified types of concepts
Cut through the noise
Eliminating duplicate data and enriching your knowledge base, integrating the extracted knowledge into the database for future use
Automated Document Classification
Applying machine learning or other technologies to automatically classify documents results in faster, scalable, and more objective classification.
Document classification can be achieved through three fundamental techniques:
Context-based document classification
Prioritising the context, such as the creator of the data, the location where the data is created or modified, the application of the data, and other variables that affect data
Content-based document classification
Using deep inspection to examine and interpret data to identify personal, sensitive, and confidential information, before determining the appropriate classification label to apply
User-based document classification
Using the user’s discretion and knowledge for classifying sensitive data, including its creation, editing, reviewing, and dissemination. With this approach to data classification, an individual can assess the sensitivity level of each document
Named Entity Recognition (NER)
Transforming text into structured, actionable insights by identifying key elements
Identifying and classifying important elements in text, like names and places, into specific categories. It highlights key information such as people, locations, organisations, and dates, making text data more structured and understandable.
NER’s versatility supports various sectors, improving processes like information retrieval and content recommendation
Information retrieval - understanding intent and context
Obtaining information, often from large databases, which is relevant to a specific query or need. Aligning results with user intent and situational context, beyond mere keywords
Automated data entry
Replicating human actions to perform routine business tasks. While these programs aren’t related to hardware robots, they function like regular white-collar workers
Content recommendation - categorising key information
Suggesting relevant content to users based on their behaviour, preferences, and interaction history.
Categorizing identified elements into predefined groups such as names, places, and dates, turning text into organised data
Sentiment analysis enhancement
Combining statistics, NLP, and machine learning to detect and extract subjective content from text. This could include a reviewer’s emotions, opinions, or evaluations regarding a specific topic, event, or the actions of a company
Semantic Text Annotation
Tagging documents with relevant concepts
Enriching text documents and unstructured content with metadata that details relevant concepts such as people, places, organisations, and more. This process makes documents machine-readable, allowing them to be easily located, understood, merged, and repurposed
Text identification
Initial cleanup of unstructured content, followed by extraction from various formats like PDFs and videos
Extraction and connection mapping
Classifying and clarifying identified entities against a knowledge base to ensure precise meaning. Mapping the connections between identified entities to weave a network of related concepts
Text Analysis
Utilising NLP techniques to analyse text, identifying key concepts such as people, places, organisations, mentions of dates, amounts, etc
Indexing and storing
Compiling the enriched data into a semantic graph database, making it accessible and analyzable for future queries
Topic Analysis (Modelling & Classification)
Topic Modelling simplifies and organises large volumes of text by uncovering prevalent themes and subjects, aiding in content categorization and summary. This process is especially beneficial for extracting and analysing major ideas or trends from extensive text collections, like news articles or research papers
Advanced topic detection techniques
Utilising complex machine learning models to identify and extract topics from large text corpora.
Recognizing patterns and emerging trends within topics over time
Sentiment and emotion analysis
Analysing the sentiment or emotional tone associated with different topics.
Gaining insights into the emotional responses or attitudes towards certain topics
Contextual topic relevance
Examines the sentiment or emotional tone linked with various topics, offering deep insights into public perceptions or attitudes toward those subjects
Customizable topic models
Develops specialised models for specific industries or fields, ensuring that analysis remains pertinent and adaptable to new information and evolving content landscapes
Trusted by
Connect with us step-by-step
Give unstructured data meaning, from plan to implementation
Step 1
Discovery
We discover your business goals and product vision, assess essential features, and map out the project timeline
Step 1
Discovery
We discover your business goals and product vision, assess essential features, and map out the project timeline
Step 1
Discovery
We discover your business goals and product vision, assess essential features, and map out the project timeline
Step 1
Discovery
We discover your business goals and product vision, assess essential features, and map out the project timeline
Wondering if we align? Let’s build something new together