Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications

Negation and speculation are universal linguistic phenomena that affect the performance of Natural Language Processing (NLP) applications, such as those for opinion mining and information retrieval, especially in biomedical data. In this article, we review the corpora annotated with negation and speculation in various natural languages and domains. Furthermore, we discuss the ongoing research into recent rule-based, supervised, and transfer learning techniques for the detection of negating and speculative content. Many English corpora for various domains are now annotated with negation and speculation; moreover, the availability of annotated corpora in other languages has started to increase. However, this growth is insufficient to address these important phenomena in languages with limited resources. The use of cross-lingual models and translation of the well-known languages are acceptable alternatives. We also highlight the lack of consistent annotation guidelines and the shortcomings of the existing techniques, and suggest alternatives that may speed up progress in this research direction. Adding more syntactic features may alleviate the limitations of the existing techniques, such as cue ambiguity and detecting the discontinuous scopes. In some NLP applications, inclusion of a system that is negation- and speculation-aware improves performance, yet this aspect is still not addressed or considered an essential step

Extracting health information from social media

Social media platforms with large user bases such as Twitter, Reddit, and online health forums contain a rich amount of health-related information. Despite the advances achieved in natural language processing (NLP), extracting actionable health information from social media still remains challenging. This thesis proposes a set of methodologies that can be used to extract medical concepts and health information from social media that is related to drugs, symptoms, and side-effects. We first develop a rule-based relationship extraction system that utilises a set of dictionaries and linguistic rules in order to extract structured information from patients’ posts on online health forums. We then automate the concept extraction pro-cess via; i) a supervised algorithm that has been trained with a small labelled dataset, and ii) an iterative semi-supervised algorithm capable of learning new sentences and concepts. We test our machine-learning pipeline on a COVID-19 case study that involves patient authored social media posts. We develop a novel triage and diagnostic approach to extract symptoms, severity, and prevalence of the disease rather than to provide any actionable decisions at the individual level. Finally, we extend our approach by investigating the potential benefit of incorporating dictionary information into a neural network architecture for natural language processing

Cross-Lingual and Low-Resource Sentiment Analysis

Identifying sentiment in a low-resource language is essential for understanding opinions internationally and for responding to the urgent needs of locals affected by disaster incidents in different world regions. While tools and resources for recognizing sentiment in high-resource languages are plentiful, determining the most effective methods for achieving this task in a low-resource language which lacks annotated data is still an open research question. Most existing approaches for cross-lingual sentiment analysis to date have relied on high-resource machine translation systems, large amounts of parallel data, or resources only available for Indo-European languages. This work presents methods, resources, and strategies for identifying sentiment cross-lingually in a low-resource language. We introduce a cross-lingual sentiment model which can be trained on a high-resource language and applied directly to a low-resource language. The model offers the feature of lexicalizing the training data using a bilingual dictionary, but can perform well without any translation into the target language. Through an extensive experimental analysis, evaluated on 17 target languages, we show that the model performs well with bilingual word vectors pre-trained on an appropriate translation corpus. We compare in-genre and in-domain parallel corpora, out-of-domain parallel corpora, in-domain comparable corpora, and monolingual corpora, and show that a relatively small, in-domain parallel corpus works best as a transfer medium if it is available. We describe the conditions under which other resources and embedding generation methods are successful, and these include our strategies for leveraging in-domain comparable corpora for cross-lingual sentiment analysis. To enhance the ability of the cross-lingual model to identify sentiment in the target language, we present new feature representations for sentiment analysis that are incorporated in the cross-lingual model: bilingual sentiment embeddings that are used to create bilingual sentiment scores, and a method for updating the sentiment embeddings during training by lexicalization of the target language. This feature configuration works best for the largest number of target languages in both untargeted and targeted cross-lingual sentiment experiments. The cross-lingual model is studied further by evaluating the role of the source language, which has traditionally been assumed to be English. We build cross-lingual models using 15 source languages, including two non-European and non-Indo-European source languages: Arabic and Chinese. We show that language families play an important role in the performance of the model, as does the morphological complexity of the source language. In the last part of the work, we focus on sentiment analysis towards targets. We study Arabic as a representative morphologically complex language and develop models and morphological representation features for identifying entity targets and sentiment expressed towards them in Arabic open-domain text. Finally, we adapt our cross-lingual sentiment models for the detection of sentiment towards targets. Through cross-lingual experiments on Arabic and English, we demonstrate that our findings regarding resources, features, and language also hold true for the transfer of targeted sentiment

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Problem-solving recognition in scientific text

As far back as Aristotle, problems and solutions have been recognised as a core pattern of thought, and in particular of the scientific method. Therefore, they play a significant role in the understanding of academic texts from the scientific domain. Capturing knowledge of such problem-solving utterances would provide a deep insight into text understanding. In this dissertation, I present the task of problem-solving recognition in scientific text. To date, work on problem-solving recognition has received both theoretical and computational treatment. However, theories of problem-solving put forward by applied linguists lack practical adaptation to the domain of scientific text, and computational analyses have been narrow in scope. This dissertation provides a new model of problem-solving. It is an adaptation of Hoey's (2001) model, tailored to the scientific domain. As far as modelling problems is concerned, I divided the text string expressing the statement of a problem into sub-components; this is one of my main contributions. I have mapped these sub-components to functional roles, and thus operationalised the model in such a way that it can be annotated by humans reliably. As far as the problem-solving relationship between problems and solutions is concerned, my model takes into account the local network of relationships existing between problems. In order to validate this new model, a large-scale annotation study was conducted. The annotation study shows significant agreement amongst the annotators. The model is automated in two stages using a blend of classical machine learning and state-of-the-art deep learning methods. The first stage involves the implementation of problem and solution recognisers which operate at the sentence level. The second stage is more complex in that it recognises problems and solutions jointly at the token-level, and also establishes whether there is a problem-solving relationship between each of them. One of the best performers at this stage was a Neural Relational Topic Model. The results from automation show that the model is able to recognise problem-solving utterances in text to a high degree of accuracy. My work has already shown a positive impact in both industry and academia. One start-up is currently using the model for representing academic articles, and a Japanese collaborator has received a grant to adapt my model to Japanese text

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail