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Found 16 papers, 8 papers with code
Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts
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 .
Creation and evaluation of timelines for longitudinal user posts
There is increasing interest to work with user generated content in social media, especially textual posts over time.
Identifying Moments of Change from Longitudinal User Text
Identifying changes in individuals' behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance.
DeezyMatch: A Flexible Deep Learning Approach to Fuzzy String Matching
We present DeezyMatch, a free, open-source software library written in Python for fuzzy string matching and candidate ranking.
A Deep Learning Approach to Geographical Candidate Selection through Toponym Matching
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.
Living Machines: A study of atypical animacy
This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text.
Policy Preference Detection in Parliamentary Debate Motions
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.
Computational Analysis of Political Texts: Bridging Research Efforts Across Communities
Political scientists created resources and used available NLP methods to process textual data largely in isolation from the NLP community.
Political Text Scaling Meets Computational Semantics
During the last fifteen years, automatic text scaling has become one of the key tools of the Text as Data community in political science.
Event-based Access to Historical Italian War Memoirs
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.
Topic-Based Agreement and Disagreement in US Electoral Manifestos
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.
Cross-Lingual Classification of Topics in Political Texts
In this paper, we propose an approach for cross-lingual topical coding of sentences from electoral manifestos of political parties in different languages.
Unsupervised Cross-Lingual Scaling of Political Texts
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).
Entities as topic labels: Improving topic interpretability and evaluability combining Entity Linking and Labeled LDA
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.
