Automatically categorize documents according to knowledge classifiers. The service can be customized to your organization's structure, terminology and processes.
Named Entity Recognition
Our recognizer successfully identifies personal names, as well as names of organizations or geographic locations, and can also extract and identify other entities.
Gather information about your users' opinions with our sentiment analysis tool. Customizable, tried-and-tested technology for a wide range of topics and in any language.
Reasons to use NLP solutions in your organization
Quality and speed thanks to our AI
Competitive pricing and customized subscriptions
We like Pangeanic's work ethos and professionalism. They actively listen to their clients - and that helps them be the best every day to provide tailored language solutions. From my point of view, that's one of their greatest qualities.
Pangeanic makes the translation process easy... And they provide a friendly, fast translation service. Creating a database for all our translations was particularly useful so we could recycle translations and re-use content in other occasions and/or similar jobs.
The quality is excellent as usual. The source has been changed many times during the translation. Pangeanic was quick to respond to the changes and it was helpful.
Leading organizations that trust Pangeanic
Natural Language Processing in Short
Natural Language Processing (NLP) is the subfield of Artificial Intelligence (AI) that focuses on how computers can understand, process and generate human language, either spoken or written. It involves the development of algorithms and statistical models that enable computers to process, understand, and generate natural language data. All developments at Pangeanic are geared towards the scalable processing of language, creating computer models to perform tasks that would normally require human-level understanding of language, such as text classification (document and email classification), sentiment analysis, machine translation, question answering, detecting personal information so it can be redacted (anonymization), summarization, etc.
Natural Language Processing includes several key tasks:
Tokenization: breaking up text into individual words or tokens.
Part-of-speech tagging: identifying the part of speech (such as noun, verb, adjective, etc.) of each word in a sentence.
Named entity recognition: identifying named entities (such as people, places, organizations, etc.) in text.
Dependency parsing: analyzing the grammatical structure of sentences and identifying the relationships between words.
Sentiment analysis: determining the emotional tone or sentiment of a piece of text.
Machine translation: translating text from one language to another.
Question answering: extracting answers to questions from a piece of text.
NLP has many applications, including:
Text classification: categorizing text documents into predefined categories (such as spam vs. non-spam emails).
Sentiment analysis: analyzing customer feedback or social media posts to determine public opinion about a product or service.
Information retrieval: searching for relevant documents or passages of text based on a query. Pangeanic offers eDiscovery and Knowledge Extraction, custom developments when you have tons of data.
Named entity recognition: Find personal details, actors, addresses, etc so they can redacted (anonymization) or even exported for post-processing.
Chatbots: creating conversational interfaces that can understand and respond to user queries.
Speech recognition: transcribing spoken language into text.
Language generation: generating natural language text from structured data or formal representations.
NLP is a rapidly evolving field, with new techniques and applications being developed all the time and recent trends in NLP include:
Deep learning: using deep neural networks to improve the accuracy of NLP tasks.
Pre-trained language models: training large language models on vast amounts of text data and fine-tuning them for specific NLP tasks.
Multimodal NLP: combining NLP with computer vision or other modalities to analyze and generate multimodal content.
Explainable AI: developing techniques to explain and interpret the decisions made by NLP systems.