Sophia Ananiadou

The University of Manchester · School of Computer Science

Given the overwhelming and rapidly increasing volumes of the published biomedical literature, automatic biomedical text summarization has long been a highly important task. Recently, great advances in the performance of biomedical text summarization have been facilitated by pre-trained language models (PLMs) based on fine-tuning. However, existing...

The context-aware emotional reasoning ability of AI systems, especially in conversations, is of vital importance in applications such as online opinion mining from social media and empathetic dialogue systems. Due to the implicit nature of conveying emotions in many scenarios, commonsense knowledge is widely utilized to enrich utterance semantics a...

Early identification of depression is beneficial to public health surveillance and disease treatment. There are many models that mainly treat the detection as a binary classification task, such as detecting whether a user is depressed. However, identifying users' depression severity levels from posts on social media is more clinically useful for fu...

Existing NTMs with contrastive learning suffer from the sample bias problem owing to the word frequency-based sampling strategy, which may result in false negative samples with similar semantics to the prototypes. In this paper, we aim to explore the efficient sampling strategy and contrastive learning in NTMs to address the aforementioned issue. W...

Depressive symptoms identification on social media aims to identify posts from social media expressing symptoms of depression. This can be beneficial for developing mental health support systems and for understanding the symptoms of depression. The Patient Health Questionnaire-9 (PHQ-9) is an instrument that healthcare professionals widely use to a...

In Emotion Recognition in Conversations (ERC), the emotions of target utterances are closely dependent on their context. Therefore, existing works train the model to generate the response of the target utterance, which aims to recognise emotions leveraging contextual information. However, adjacent response generation ignores long-range dependencies...

Pretrained language models have been used in various natural language processing applications. In the mental health domain, domain-specific language models are pretrained and released, which facilitates the early detection of mental health conditions. Social posts, e.g., on Reddit, are usually long documents. However, there are no domain-specific p...

Mental illnesses are one of the most prevalent public health problems worldwide, which negatively influence people's lives and society's health. With the increasing popularity of social media, there has been a growing research interest in the early detection of mental illness by analysing user-generated posts on social media. According to the corre...

The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), provides a big challenge for clinicians and researchers to access clinical information efficiently. To address the problem, biomedical text summarization has been proposed to support clinical information retrieval and management, aiming at...

The goal of temporal relation extraction is to infer the temporal relation between two events in the document. Supervised models are dominant in this task. In this work, we investigate ChatGPT's ability on zero-shot temporal relation extraction. We designed three different prompt techniques to break down the task and evaluate ChatGPT. Our experimen...

Automated mental health analysis shows great potential for enhancing the efficiency and accessibility of mental health care, whereas the recent dominant methods utilized pre-trained language models (PLMs) as the backbone and incorporated emotional information. The latest large language models (LLMs), such as ChatGPT, exhibit dramatic capabilities o...

Automatic extraction of patient medication histories from free-text clinical notes can increase the amount of relevant information to clinicians for developing treatment plans. In addition to detecting medication events, clinical text mining systems must also be able to predict event context, such as negation, uncertainty, and time of occurrence, i...

The performance of abstractive text summarization has been greatly boosted by pre-trained language models recently. The main concern of existing abstractive summarization methods is the factual inconsistency problem of their generated summary. To alleviate the problem, many efforts have focused on developing effective factuality evaluation metrics...

The information bottleneck (IB) principle has been proven effective in various NLP applications. The existing work, however, only used either generative or information compression models to improve the performance of the target task. In this paper, we propose to combine the two types of IB models into one system to enhance Named Entity Recognition...

A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) which uses categorical emotion labels as supervision signals and contrasts in high-dimensional semantic space. However, categorical labels fail to provide quantitative information b...

A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) which uses categorical emotion labels as supervision signals and contrasts in high-dimensional semantic space. However, categorical labels fail to provide quantitative information b...

The citation graph is essential for generating high-quality summaries of scientific papers, in which references of a scientific paper and their correlations provide extra knowledge for understanding its background and main contributions. Despite the promising role of the citation graph, effectively incorporating it still remains a big challenge, gi...

In Emotion Recognition in Conversations (ERC), the emotions of target utterances are closely dependent on their context. Therefore, existing works train the model to generate the response of the target utterance, which aims to recognise emotions leveraging contextual information. However, adjacent response generation ignores long-range dependencies...

Mental illnesses are one of the most prevalent public health problems worldwide, which negatively influence people’s lives and society’s health. With the increasing popularity of social media, there has been a growing research interest in the early detection of mental illness by analysing user-generated posts on social media. According to the corre...

Automatic extraction of relations between gene mutations and cancer entities occurring in the cancer literature using text mining can rapidly provide vital information to support precision cancer medicine. However, mutation-cancer relation extraction is more challenging than general relation extraction from free text, since it is often not possible...

Different from general documents, it is recognised that the ease with which people can understand a biomedical text is eminently varied, owing to the highly technical nature of biomedical documents and the variance of readers' domain knowledge. However, existing biomedical document summarization systems have paid little attention to readability con...

Background: In recent years, the COVID-19 pandemic has brought great changes to public health, society, and the economy. Social media provide a platform for people to discuss health concerns, living conditions, and policies during the epidemic, allowing policymakers to use this content to analyze the public emotions and attitudes for decision-makin...

Minimising accident risk for new construction projects requires a thorough analysis of previous accidents, including an examination of circumstances and reasons for their occurrence, their consequences, and measures used for future mitigation. Such information is often recorded only within the huge amounts of unstructured textual documentation that...

Labelled data for training sequence labelling models can be collected from multiple annotators or workers in crowdsourcing. However, these labels could be noisy because of the varying expertise and reliability of annotators. In order to ensure high quality of data, it is crucial to infer the correct labels by aggregating noisy labels. Although labe...

Recently, neural topic models (NTMs) have been incorporated into pre-trained language models (PLMs), to capture the global semantic information for text summarization. However, in these methods, there remain limitations in the way they capture and integrate the global semantic information. In this paper, we propose a novel model, the graph contrast...

The evolution of the Exposome concept revolutionised the research in exposure assessment and epidemiology by introducing the need for a more holistic approach on the exploration of the relationship between the environment and disease. At the same time, further and more dramatic changes have also occurred on the working environment, adding to the al...

Biomedical text summarization is a critical task for comprehension of an ever-growing amount of biomedical literature. Pre-trained language models (PLMs) with transformer-based architectures have been shown to greatly improve performance in biomedical text mining tasks. However, existing methods for text summarization generally fine-tune PLMs on th...

Background: In recent years, the COVID-19 pandemic has brought great changes to public health, society and the economy. Social media provides a platform for people to discuss health concerns, living conditions and policies during the epidemic, which allows policy makers to use its contents to analyse the public emotions and attitudes for decision...

Background Nested and overlapping events are particularly frequent and informative structures in biomedical event extraction. However, state-of-the-art neural models either neglect those structures during learning or use syntactic features and external tools to detect them. To overcome these limitations, this paper presents and compares two neural...

Introduction: Suicide is a global health concern. Sociocultural factors have an impact on self-harm and suicide rates. In Pakistan, both self-harm and suicide are considered as criminal offence's and are condemned on both religious and social grounds. The proposed intervention 'Youth Culturally Adapted Manual Assisted Problem Solving Training (YCM...

Stress and depression detection on social media aim at the analysis of stress and identification of depression tendency from social media posts, which provide assistance for the early detection of mental health conditions. Existing methods mainly model the mental states of the post speaker implicitly. They also lack the ability to mentalise for com...

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people's life with impact on society's health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations express...

Negation and uncertainty modeling are long-standing tasks in natural language processing. Linguistic theory postulates that expressions of negation and uncertainty are semantically independent from each other and the content they modify. However, previous works on representation learning do not explicitly model this independence. We therefore attem...

Gender bias is an important problem that affects models of natural language, and the propagation of such biases could be harmful. Much research focuses on gender biases in word embeddings, and there are also some works on gender biases in subsequent tasks. However, very limited prior work has been done on gender issues in emotion detection tasks. I...

Recently, Transformer model, which has achieved great success in many artificial intelligence fields, has demonstrated its great potential in modeling graph-structured data. Till now, a great variety of Transformers has been proposed to adapt to the graph-structured data. However, a comprehensive literature review and systematical evaluation of the...

Click-Through Rate (CTR) prediction, is an essential component of online advertising. The mainstream techniques mostly focus on feature interaction or user interest modeling, which rely on users' directly interacted items. The performance of these methods are usally impeded by inactive behaviours and system's exposure, incurring that the features e...

Recent work in Natural Language Processing has increasingly focused on detecting suicidal intent in textual data, where the main aim is to detect expressions in a binary setting. However, previous research has shown that search results and other mentions of suicide online are not only limited to expressions of suicidal intent. Therefore, previously...

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades 1,2 , the most dramatic advances in MR have followed in the wake of critical corpus development ³ . Large, well-annotated corpora have been associated with punctuated advances in MR methodology and aut...

Large scale pre-trained language models (PLMs) have advanced state-of-the-art (SOTA) performance on various biomedical text mining tasks. The power of such PLMs can be combined with the advantages of deep generative models. These are examples of these combinations. However, they are trained only on general domain text, and biomedical models are sti...

The COVID-19 pandemic resulted in an unprecedented production of scientific literature spanning several fields. To facilitate navigation of the scientific literature related to various aspects of the pandemic, we developed an exploratory search system. The system is based on automatically identified technical terms, document citations, and their vi...

Within the EXPOSOME PROJECT FOR HEALTH AND OCCUPATIONAL RESEARCH (EPHOR) project we aim to develop a protocol to enable efficient update of job exposure matrices so that they can include the latest available information of highest quality possible. The protocol will include methods for searching and collecting new data from literature (assisted by...

Research in Text Simplification (TS) has relied mostly on the Wikipedia-based datasets and the SARI evaluation metric, as the preferred means for creating and evaluating new simplification methods. Previous studies have pointed out the flaws of data evaluation resources, including incorrect alignment of simple/complex sentence pairs, sentences with...

Predicting and understanding how various mental health conditions present online in textual social media data has become an increasingly popular task. The main aim of using this type of data lies in utilising its findings to prevent future harm as well as to provide help. In this paper, we describe our approach and findings in participating in sub-...

Textual Emotion Recognition (TER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications. Prior research has tackled the automatic classification of emotion expressions in text by maximising the probability of the correct emotion class using cross-entropy loss. However, this approach does not ac...

Modern text simplification (TS) heavily relies on the availability of gold standard data to build machine learning models. However, existing studies show that parallel TS corpora contain inaccurate simplifications and incorrect alignments. Additionally, evaluation is usually performed by using metrics such as BLEU or SARI to compare system output t...

Modern text simplification (TS) heavily relies on the availability of gold standard data to build machine learning models. However, existing studies show that parallel TS corpora contain inaccurate simplifications and incorrect alignments. Additionally, evaluation is usually performed by using metrics such as BLEU or SARI to compare system output t...

To interpret the genetic profile present in a patient sample, it is necessary to know which mutations have important roles in the development of the corresponding cancer type. Named entity recognition is a core step in the text mining pipeline which facilitates mining valuable cancer information from the scientific literature. However, due to the s...

Suicide is one of the leading causes of death worldwide. At the same time, the widespread use of social media has led to an increase in people posting their suicide notes online. Therefore, designing a learning model that can aid the detection of suicide notes online is of great importance. However, current methods cannot capture both local and glo...

We propose a multi-task, probabilistic approach to facilitate distantly supervised relation extraction by bringing closer the representations of sentences that contain the same Knowledge Base pairs. To achieve this, we bias the latent space of sentences via a Variational Autoencoder (VAE) that is trained jointly with a relation classifier. The late...

Background A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especial...

Semantic search engines, which integrate the output of text mining (TM) methods, can significantly increase the ease and efficiency of finding relevant documents and locating important information within them. We present a novel search engine for the construction industry, HSEarch (http://www.nactem.ac.uk/hse/), which uses TM methods to provide sem...

Semantic search engines, which integrate the output of text mining (TM) methods, can significantly increase the ease and efficiency of finding relevant documents and locating important information within them. We present a novel search engine for the construction industry, HSEarch (http://www.nactem.ac.uk/hse/), which uses TM methods to provide sem...

Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and security. Current approaches to ER, mainly classify emotions independently without considering that emotions can co-exist. Such approaches overloo...

Machine reading is essential for unlocking valuable knowledge contained in the millions of existing biomedical documents. Over the last two decades 1,2 , the most dramatic advances in machine-reading have followed in the wake of critical corpus development ³ . Large, well-annotated corpora have been associated with punctuated advances in machine re...

Objective To determine how the representation of women’s health has changed in clinical studies over the course of 70 years. Design Observational study of 71 866 research articles published between 1948 and 2018 in The BMJ . Main outcome measures The incidence of women-specific health topics over time. General linear, additive and segmented regre...

Motivation: Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools. Results: We propose an end-to-end neural nested event extraction m...

Most deep language understanding models depend only on word representations, which are mainly based on language modelling derived from a large amount of raw text. These models encode distributional knowledge without considering syntactic information, although several studies have shown benefits of including such information. Therefore, we propose n...

We present our approach for the identification of cited text spans in scientific literature, using pre-trained encoders (BERT) in combination with different neural networks. We further experiment to assess the impact of using these cited text spans as input in BERT-based extractive summarisation methods. Inspired and motivated by the CL-SciSumm sha...

Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form. However, we demonstrate that by using only named e...