Lemmatisation is the process of finding the normalised forms of words appearing in text. It is a useful preprocessing step for a number of language engineering and text mining tasks, and especially important for languages with rich inflectional morphology. This paper presents a new lemmatisation system, LemmaGen, which was trained to generate accurate and efficient lemmatisers for twelve differ...

We have developed a prototype for sentiment analysis that is able to identify aspects of an entity being reviewed, along with the sentiment polarity associated to those aspects. Our approach relies on a core ontology of the task, augmented by a workbench for bootstrapping, expanding and maintaining semantic assets that are useful for a number of text analytics tasks. The workbench has the abili...

This paper suggests a model of the process through which a set of symbols, initially without any intrinsic meaning, acquires endogenously a conventional and socially shared meaning. This model has two related aspects. The first is the cognitive aspect, represented by the process through which each agent processes the information gathered during the interactions with other agents. In this paper,...

Text processing is a vital step in the information retrieval process, text mining, and natural language processing. It includes several stages, such as normalization, stop word removal, and stemming. Stemming is the process of reducing the lexicon to its root. Due to the different structures and rules in languages, the task of stemming is language-dependent. This research introduces a new stemm...

We present on-going work on the harmonization of existing German lexical resources in the field of opinion and sentiment mining. The input of our harmonization effort consisted in four distinct lexicons of German word forms, encoded either as lemmas or as full forms, marked up with polarity features, at distinct granularity levels. We describe how the lexical resources have been mapped onto eac...