Text contains a wealth of information, but it mostly comes in unstructured form. In this age of unprecedented data growth, more and more companies are turning to Natural Language Processing to make sense of their textual data or to interact with their clients in new and exciting ways. NLP helps them determine the topics that are covered in a text, the people and places that are mentioned, the sentiment that is expressed, and so on.
NLP Town combines a decade of research and development in Natural Language Processing with over ten years of experience in software engineering. After having learned the tricks of the trade at five universities in as many countries, we are now applying our passion and expertise to numerous NLP projects in industry. If you would like to make the most of your textual data, you’ve come to the right place.
Information Extraction is the task of identifying useful information in unstructured documents. Depending on your task, this information can vary from entities (such as people, organizations and locations) in newspaper articles, to definitions in legislation, career information in CVs or judgments in court proceedings. Whenever information is expressed in a consistent manner, we can develop methods to find it automatically.
Text categorization is at the heart of many tools that have become indispensable to our lives. E-mail software uses spam filters to distinguish between useful e-mails and spam, media organizations rely on computational semantics to detect the topics of news articles, and customer-centric companies apply sentiment analysis to tweets and reviews to determine what their customers are cheering or complaining about.
Today users expect more from a search engine than a system that simply returns a list of documents with the words they typed in. Modern search engines scan their text collection for synonyms, learn the topics in all texts, or identify entities and the relations between them. In this way they aim to truly understand the needs of their users, and present them with more relevant and personalized search results.