no code implementations • • Adam Tsakalidis, Jenny Chim, Iman Munire Bilal, Ayah Zirikly, Dana Atzil-Slonim, Federico Nanni, Philip Resnik, Manas Gaur, Kaushik Roy, Becky Inkster, Jeff Leintz, Maria Liakata
We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of ‘Moments of Change’ in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health .
There is increasing interest to work with user generated content in social media, especially textual posts over time.
Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance.
We present DeezyMatch, a free, open-source software library written in Python for fuzzy string matching and candidate ranking.
We report its performance on candidate selection in the context of the downstream task of toponym resolution, both on existing datasets and on a new manually-annotated resource of nineteenth-century English OCR'd text.
This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text.
Debate motions (proposals) tabled in the UK Parliament contain information about the stated policy preferences of the Members of Parliament who propose them, and are key to the analysis of all subsequent speeches given in response to them.
Political scientists created resources and used available NLP methods to process textual data largely in isolation from the NLP community.
During the last fifteen years, automatic text scaling has become one of the key tools of the Text as Data community in political science.
The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source.
We present a topic-based analysis of agreement and disagreement in political manifestos, which relies on a new method for topic detection based on key concept clustering.
In this paper, we propose an approach for cross-lingual topical coding of sentences from electoral manifestos of political parties in different languages.
Political text scaling aims to linearly order parties and politicians across political dimensions (e. g., left-to-right ideology) based on textual content (e. g., politician speeches or party manifestos).
In order to create a corpus exploration method providing topics that are easier to interpret than standard LDA topic models, here we propose combining two techniques called Entity linking and Labeled LDA.