Search Results for author: Glorianna Jagfeld

Found 8 papers, 2 papers with code

Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis

1 code implementation NAACL (CLPsych) 2021 Glorianna Jagfeld, Fiona Lobban, Paul Rayson, Steven H. Jones

Recently, research on mental health conditions using public online data, including Reddit, has surged in NLP and health research but has not reported user characteristics, which are important to judge generalisability of findings.

A computational linguistic study of personal recovery in bipolar disorder

no code implementations ACL 2019 Glorianna Jagfeld

Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools.

Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity

no code implementations WS 2018 Glorianna Jagfeld, Sabrina Jenne, Ngoc Thang Vu

We present a comparison of word-based and character-based sequence-to-sequence models for data-to-text natural language generation, which generate natural language descriptions for structured inputs.

Text Generation

Comparing Attention-based Convolutional and Recurrent Neural Networks: Success and Limitations in Machine Reading Comprehension

1 code implementation CONLL 2018 Matthias Blohm, Glorianna Jagfeld, Ekta Sood, Xiang Yu, Ngoc Thang Vu

We propose a machine reading comprehension model based on the compare-aggregate framework with two-staged attention that achieves state-of-the-art results on the MovieQA question answering dataset.

Machine Reading Comprehension Question Answering

Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking

no code implementations WS 2017 Glorianna Jagfeld, Ngoc Thang Vu

This paper presents our novel method to encode word confusion networks, which can represent a rich hypothesis space of automatic speech recognition systems, via recurrent neural networks.

Automatic Speech Recognition dialog state tracking +1

Evaluating Compound Splitters Extrinsically with Textual Entailment

no code implementations ACL 2017 Glorianna Jagfeld, Patrick Ziering, Lonneke van der Plas

Traditionally, compound splitters are evaluated intrinsically on gold-standard data or extrinsically on the task of statistical machine translation.

Information Retrieval Machine Translation +3

The Grammar of English Deverbal Compounds and their Meaning

no code implementations WS 2016 Gianina Iord{\u{a}}chioaia, Lonneke van der Plas, Glorianna Jagfeld

We present an interdisciplinary study on the interaction between the interpretation of noun-noun deverbal compounds (DCs; e. g., task assignment) and the morphosyntactic properties of their deverbal heads in English.

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