TIARA 2.0: an interactive tool for annotating discourse structure and text improvement

Discourse structure annotation aims at analysing how discourse units (e.g. sentences or clauses) relate to each other and what roles they play in the overall discourse. Several annotation tools for discourse structure have been developed. However, they often only support specific annotation schemes, making their usage limited to new schemes. This article presents TIARA 2.0, an annotation tool for discourse structure and text improvement. Departing from our specific needs, we extend an existing tool to accommodate four levels of annotation: discourse structure, argumentative structure, sentence rearrangement and content alteration. The latter two are particularly unique compared to existing tools. TIARA is implemented on standard web technologies and can be easily customised. It deals with the visual complexity during the annotation process by systematically simplifying the layout and by offering interactive visualisation, including clutter-reducing features and dual-view display. TIARA’s text-view allows annotators to focus on the analysis of logical sequencing between sentences. The tree-view allows them to review their analysis in terms of the overall discourse structure. Apart from being an annotation tool, it is also designed to be useful for educational purposes in the teaching of argumentation; this gives it an edge over other existing tools.

Broad coverage emotion annotation

In this paper we present the emotion annotation of 1.5 billion words Portuguese corpora, publicly available. We motivate the annotation process and detail the decisions made. The resource is evaluated, being applied to different areas: to study Lusophone literature, to obtain paraphrases, and to do genre comparison.

DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels

Nowadays, there are many applications of text mining over corpora from different languages. However, most of them are based on texts in prose, lacking applications that work with poetry texts. An example of an application of text mining in poetry is the usage of features derived from their individual words in order to capture the lexical, sublexical and interlexical meaning, and infer the General Affective Meaning (GAM) of the text. However, even though this proposal has been proved as useful for poetry in some languages, there is a lack of studies for both Spanish poetry and for highly-structured poetic compositions such as sonnets. This article presents a study over an annotated corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words for predicting their GAM. The purpose of this is to model sonnets at an affective level. The article also analyses the relationship between the GAM of the sonnets and the content itself. For this, we consider the content from a psychological perspective, identifying with tags when a sonnet is related to a specific term. Then, we study how GAM changes according to each of those psychological terms. The corpus used contains 274 Spanish sonnets from authors of different centuries, from fifteenth to nineteenth. This corpus was annotated by different domain experts. The experts annotated the poems with affective and lexico-semantic features, as well as with domain concepts that belong to psychology. Thanks to this, the corpus of sonnets can be used in different applications, such as poetry recommender systems, personality text mining studies of the authors, or the usage of poetry for therapeutic purposes.