To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed.
Freedom of the press and media is of vital importance for democratically organised states and open societies.
We introduce and study a problem variant of sentiment analysis, namely the “same sentiment classification problem”, where, given a pair of texts, the task is to determine if they have the same sentiment, disregarding the actual sentiment polarity.
In total, we process nine categories and actively learn their representation in our dataset.
In the case of multi-page documents, the preservation of document contexts is a major requirement.
The iLCM project pursues the development of an integrated research environment for the analysis of structured and unstructured data in a "Software as a Service" architecture (SaaS).
The new measure of context volatility that we propose models the degree by which terms change context in a text collection over time.
In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications.
This paper presents the "Leipzig Corpus Miner", a technical infrastructure for supporting qualitative and quantitative content analysis.
In terminology work, natural language processing, and digital humanities, several studies address the analysis of variations in context and meaning of terms in order to detect semantic change and the evolution of terms.