We analyse automatically any type of text in different languages in order to detect, classify, organise or search for non-explicit content to simplify the process for those who usually do it manually.
Natural Language Processing
At Instituto de Ingeniería del Conocimiento (IIC), Natural Language Processing (NLP) techniques include a range of linguistic tools to cover and combine in real time the different lexical, morphological and semantic processing layers, with machine learning and deep learning models, and software architectures.
These techniques, handled by expert computational linguists and data scientists, allow us offering natural language processing services with reliable results.
We analyse automatically texts in different natural languages in order to enrich the information, classify it, or grasp certain characteristics.
Automatic Sentiment Analysis Detector
Our Automatic Sentiment Analysis Detector offers a precise extraction of meaning grasping the information to analyse, among others:
- Opinion: Whether it is positive or negative regarding a topic.
- Sentiment: Up to 20 emotions analysed (happiness, despair, desire, surprise, etc.).
- Users’ Intention: Such as questions, doubts, complaints, suggestions, buying intentions…
- Awareness: For instance, whether citizens are involved in social causes or not.
- Topics classification: Customized according to 10 to 20 categories depending on the industrial sector (retail, automotive, banking, telecommunications, health, etc.).
The Topics Detector extracts the most representative subjects from a group of texts to detect automatically the most relevant ideas of each compared document.
It is a very practical service to find out and classify new ideas from the texts without the need of a manual processing or a previous taxonomy.
It is designed to help those companies with a very high percentage of changing categories in very short periods of time.
Named-Entity Recognition (NER) Detector
The Named-Entity Recognition (NER) Detector identifies different types of entities such as people, organizations, locations or trademarks using machine learning technology and linguistic rules and corpuses.
NER Detector is very useful to enrich texts and take on tasks of information retrieval or indexation.
Automatic Text Documents Classifier
Our Automatic Text Documents Classifier is provided with 2 different modules: one to predict and another one to train machine learning and deep learning models.
The model training module is based on an annotated corpus enriched with the information gathered from topics, ngrams, lemmatization and vectorization of words, etc.
As documents are processed and the taxonomy is adapted automatically, the Automatic Text Documents Classifier can be easily customized.
This service is highly useful to accomplish the tasks of processing and classification of large amounts of documents.
Similar Documents Detector
The Similar Documents Detector shows how similar two texts are even when comparing large volumes of documents.
For the comparison we set semantic criteria and we use NLP or Natural Language Processing techniques, that can even detect similarities in topics. This is especially useful for those professionals working in public institutions, schools and research centres to detect plagiarism, for instance.
This service is available in different languages.
Gender and Age Detector in Social Media
This detector allows us to infer the gender and age of social media users. The Gender and Age Detector in Social Media analyses Twitter conversations about an organization or brand.
The detector uses NLP or Natural Language Processing techniques and machine learning models to take on tasks of business profiling in order to, for example, plan or redirect marketing campaigns according to the target profiles.
Kioo Information Retrieval Platform
Our Kioo Information Retrieval Platform is designed to process information from plain texts retrieved from audios, videos and texts in different formats. Its search engine also permits to index the information and search for it.
Natural Language Processing Benefits
Computational Linguistics provides lots of benefits when dealing with text content:
When tasks are automated using linguistic technology the results of searching, classifying, and analysing information are much more thorough. Thus, compared with the manual process, this automation permits, for example, to categorise complaints and incidences more effectively.
It also allows the professional to get focused on less monotonous and more rewarding tasks.
Vast-volume Related Tasks Performance
Taking on tasks with a vast volume of information is only possible thanks to NLP or Natural Language Processing techniques. Otherwise, tasks such as reading one by one every tweet written in relation to a specific brand or topic would be out of the question.
Using linguistic technologies helps performing an automated reading in order to take advantage of all the possibilities data bring.
Automated search, classification and analyses of large volumes of texts make possible to reveal new information and uncover insights that otherwise, with a manual process, would have been difficult to detect.
Applying linguistic techniques permits unveiling patterns and hidden relations among data which add value to the task. This lets us, for instance, enrich the profile of a client for a certain product or service.
Natural Language Processing research allowed us to design a simple tool to accomplish a lexical comparative analysis of texts.
This tool permits comparing two texts and performing 3 different types of analyses to unveil their lexical variety. Try it!
Why invest in Natural Language Processing??
Automatic processing reports obvious advantages as opposed to a manual classification, especially when dealing with large volumes of texts:
- Substantial reduction of costs associated to time and staff required for a task.
- Resource planning is improved.
- Process information in real timeas it is a high-performance system for large volumes of texts.
- Speeds up decision-making processes.
- It is adjusted and personalisedto the client’s needs.
It is possible to identify and relate client behaviours to scores from users’ surveys.