Methods for taking into account linguistic content into text retrieval are receiving a growing attention [16],[14]. Text categorization is an interesting area for evaluating and quantifying the impact of linguistic information. Works in text retrieval through Internet suggest that embedding linguistic information at a suitable level within traditional quantitative approaches (e.g. sense distinc...

This paper presents a system for automatically extracting linguistic data from digitized linguistic documents using a combination of existing software packages and custom scripts. The system is designed to leverage existing resources in online digital libraries in order to bootstrap the creation of large, multi-lingual linguistic corpora, which can then be used to conduct data-driven experiment...

Although text-based ontology engineering gained much popularity in the last 10 years, very few ontology engineering platforms exploit the full potential of the connection between texts and ontologies. We propose DAFOE, a new platform for building ontologies with a terminological component using different types of linguistic entries (text corpora, results of natural language processing tools, te...

Interpretation of natural language is inherently context-sensitive. Most words in natural language are ambiguous and their meanings are heavily dependent on the linguistic context in which they are used. The study of lexical semantics can not be separated from the notion of context. This paper takes a contextual approach to lexical semantics and studies the linguistic context of lexical atoms, ...

Deep learning based language models have improved generation-based linguistic steganography, posing a huge challenge for steganalysis. The existing neural-network-based steganalysis methods are incompetent to deal with complicated text because they only extract single-granularity features such as global or local features. To fuse multi-granularity features, we present novel method on attentiona...