RESEARCH-DRIVEN LANGUAGE TECHNOLOGY AND AI
WE DEVELOP INNOVATIVE LANGUAGE-TECHNOLOGY AND AI-SOLUTIONS FOR YOUR ENTERPRISE
Our core areas of expterise are semantic text analytics (NLP), automated text, data and document generation (NLG), machine learning (ML) and artificial intelligence (AI). With our team of computational linguists, data scientists, software engineers and classical linguists, we’ve been operating successfully in the market place since 2011. For a comprehensive list of our clients as well as project descriptions, please click here.
European target markets in focus
INNOVATIVE LANGUAGE TECHONOLGIES AND LINGUISTIC CONSULTANCY FOR ALL OF EUROPE
LangTec focuses on the research-driven development of innovative semantic technologies. Our solutions are made for the intelligent processing of linguistic data. We also provide comprehensive linguistic consultancy and localisation services for all European target markets.
Our core competences comprise the automated processing of natural language (Natural Language Processing, NLP), Machine Learning (ML), Artificial intelligence (AI), automated text generation (NLG), computational linguistics and analytical linguistics. Our linguists have extensive hands-on experience in phonetic transcriptions, syntactic analysis, grammatical modelling, dialogue design and textual localisation. All of our language experts are all fully qualified linguists and native speakers of their respective target languages.
Text mining is the process of gaining complex information and insights from unstructured texts. To this end, text mining employs a wide range of linguistic, statistical and machine learning techniques. Data mining, in contrast, describes a range of similar techniques applied primarily to already structured input data. In contrast to text mining, the input data in data mining tends to have little or no inherent structure.
Sentiment analysis focuses on the automated detection and classification of the emotional stances and sentiments expressed in user-generated text. Typical application domains for sentiment analysis comprise the automated monitoring of e-commerce the product reviews or social media content.
The development of semantic technologies is our passion. We also know that the ultimate outcome strongly depends on the quality of linguistic resources that the system is initialised with. We have more than a decade of experience in the generation of complex linguistic resources such as dictionaries, phonetic transcriptions, grammatical models, domain-specific terminologies and language-specific dialog models.
Semantic search centres the search user experience around the actual meaning of the query rather than its syntax. Semantic search does not search for mere keywords, but retrieves all relevant results based on the query’s actual meaning. Semantic search comprises search query interpretation and expansion, the inclusion of background knowledge in result retrieval, and logical inferencing. These advanced techniques yield considerably improved relevance of the search results.
The generation of human-readable natural language text (NLG) from structured data is an area of continually increasing importance in industry today. Full texts such as reports or summaries are machine-generated from structured data in a database or data stream. NLG is becming increasingly useful in areas with high data throughput, such as weather or stock-exchange reports or sport tickers.
Ontologies are formally structured, hierarchical representations of the entities in a domain as well as the semantic relations between them. Ontologies provide a machine-readable representation of domain knowledge and thus make that knowledge available for computational processing. Ontologies are of particular importance as an additional source of domain knowledge in the deep semantic analysis of natural language text.