Part of Speech Based Term Weighting for Information Retrieval

Automatic language processing tools typically assign to terms so-called weights corresponding to the contribution of terms to information content. Traditionally, term weights are computed from lexical statistics, e.g., term frequencies. We propose a new type of term weight that is computed from part of speech (POS) n-gram statistics. The proposed POS-based term weight represents how informative a term is in general, based on the POS contexts in which it generally occurs in language. We suggest five different computations of POS-based term weights by extending existing statistical approximations of term information measures. We apply these POS-based term weights to information retrieval, by integrating them into the model that matches documents to queries. Experiments with two TREC collections and 300 queries, using TF-IDF & BM25 as baselines, show that integrating our POS-based term weights to retrieval always leads to gains (up to +33.7% from the baseline). Additional experiments with a different retrieval model as baseline (Language Model with Dirichlet priors smoothing) and our best performing POS-based term weight, show retrieval gains always and consistently across the whole smoothing range of the baseline

Towards Personalized and Human-in-the-Loop Document Summarization

The ubiquitous availability of computing devices and the widespread use of the internet have generated a large amount of data continuously. Therefore, the amount of available information on any given topic is far beyond humans' processing capacity to properly process, causing what is known as information overload. To efficiently cope with large amounts of information and generate content with significant value to users, we require identifying, merging and summarising information. Data summaries can help gather related information and collect it into a shorter format that enables answering complicated questions, gaining new insight and discovering conceptual boundaries. This thesis focuses on three main challenges to alleviate information overload using novel summarisation techniques. It further intends to facilitate the analysis of documents to support personalised information extraction. This thesis separates the research issues into four areas, covering (i) feature engineering in document summarisation, (ii) traditional static and inflexible summaries, (iii) traditional generic summarisation approaches, and (iv) the need for reference summaries. We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches. The experimental results prove the efficiency of the proposed approaches compared to other state-of-the-art models. We further propose solutions to the information overload problem in different domains through summarisation, covering network traffic data, health data and business process data.Comment: PhD thesi

POLIS: a probabilistic summarisation logic for structured documents

PhDAs the availability of structured documents, formatted in markup languages such as SGML, RDF, or XML, increases, retrieval systems increasingly focus on the retrieval of document-elements, rather than entire documents. Additionally, abstraction layers in the form of formalised retrieval logics have allowed developers to include search facilities into numerous applications, without the need of having detailed knowledge of retrieval models. Although automatic document summarisation has been recognised as a useful tool for reducing the workload of information system users, very few such abstraction layers have been developed for the task of automatic document summarisation. This thesis describes the development of an abstraction logic for summarisation, called POLIS, which provides users (such as developers or knowledge engineers) with a high-level access to summarisation facilities. Furthermore, POLIS allows users to exploit the hierarchical information provided by structured documents. The development of POLIS is carried out in a step-by-step way. We start by defining a series of probabilistic summarisation models, which provide weights to document-elements at a user selected level. These summarisation models are those accessible through POLIS. The formal definition of POLIS is performed in three steps. We start by providing a syntax for POLIS, through which users/knowledge engineers interact with the logic. This is followed by a definition of the logics semantics. Finally, we provide details of an implementation of POLIS. The final chapters of this dissertation are concerned with the evaluation of POLIS, which is conducted in two stages. Firstly, we evaluate the performance of the summarisation models by applying POLIS to two test collections, the DUC AQUAINT corpus, and the INEX IEEE corpus. This is followed by application scenarios for POLIS, in which we discuss how POLIS can be used in specific IR tasks

Sequential Complexity as a Descriptor for Musical Similarity

We propose string compressibility as a descriptor of temporal structure in audio, for the purpose of determining musical similarity. Our descriptors are based on computing track-wise compression rates of quantised audio features, using multiple temporal resolutions and quantisation granularities. To verify that our descriptors capture musically relevant information, we incorporate our descriptors into similarity rating prediction and song year prediction tasks. We base our evaluation on a dataset of 15500 track excerpts of Western popular music, for which we obtain 7800 web-sourced pairwise similarity ratings. To assess the agreement among similarity ratings, we perform an evaluation under controlled conditions, obtaining a rank correlation of 0.33 between intersected sets of ratings. Combined with bag-of-features descriptors, we obtain performance gains of 31.1% and 10.9% for similarity rating prediction and song year prediction. For both tasks, analysis of selected descriptors reveals that representing features at multiple time scales benefits prediction accuracy.Comment: 13 pages, 9 figures, 8 tables. Accepted versio

Language modelling for clinical natural language understanding and generation

One of the long-standing objectives of Artificial Intelligence (AI) is to design and develop algorithms for social good including tackling public health challenges. In the era of digitisation, with an unprecedented amount of healthcare data being captured in digital form, the analysis of the healthcare data at scale can lead to better research of diseases, better monitoring patient conditions and more importantly improving patient outcomes. However, many AI-based analytic algorithms rely solely on structured healthcare data such as bedside measurements and test results which only account for 20% of all healthcare data, whereas the remaining 80% of healthcare data is unstructured including textual data such as clinical notes and discharge summaries which is still underexplored. Conventional Natural Language Processing (NLP) algorithms that are designed for clinical applications rely on the shallow matching, templates and non-contextualised word embeddings which lead to limited understanding of contextual semantics. Though recent advances in NLP algorithms have demonstrated promising performance on a variety of NLP tasks in the general domain with contextualised language models, most of these generic NLP algorithms struggle at specific clinical NLP tasks which require biomedical knowledge and reasoning. Besides, there is limited research to study generative NLP algorithms to generate clinical reports and summaries automatically by considering salient clinical information. This thesis aims to design and develop novel NLP algorithms especially clinical-driven contextualised language models to understand textual healthcare data and generate clinical narratives which can potentially support clinicians, medical scientists and patients. The first contribution of this thesis focuses on capturing phenotypic information of patients from clinical notes which is important to profile patient situation and improve patient outcomes. The thesis proposes a novel self-supervised language model, named Phenotypic Intelligence Extraction (PIE), to annotate phenotypes from clinical notes with the detection of contextual synonyms and the enhancement to reason with numerical values. The second contribution is to demonstrate the utility and benefits of using phenotypic features of patients in clinical use cases by predicting patient outcomes in Intensive Care Units (ICU) and identifying patients at risk of specific diseases with better accuracy and model interpretability. The third contribution is to propose generative models to generate clinical narratives to automate and accelerate the process of report writing and summarisation by clinicians. This thesis first proposes a novel summarisation language model named PEGASUS which surpasses or is on par with the state-of-the-art performance on 12 downstream datasets including biomedical literature from PubMed. PEGASUS is further extended to generate medical scientific documents from input tabular data.Open Acces

Modelling the Strategic Alignment of Software Requirements using Goal Graphs

This paper builds on existing Goal Oriented Requirements Engineering (GORE) research by presenting a methodology with a supporting tool for analysing and demonstrating the alignment between software requirements and business objectives. Current GORE methodologies can be used to relate business goals to software goals through goal abstraction in goal graphs. However, we argue that unless the extent of goal-goal contribution is quantified with verifiable metrics and confidence levels, goal graphs are not sufficient for demonstrating the strategic alignment of software requirements. We introduce our methodology using an example software project from Rolls-Royce. We conclude that our methodology can improve requirements by making the relationships to business problems explicit, thereby disambiguating a requirement's underlying purpose and value.Comment: v2 minor updates: 1) bitmap images replaced with vector, 2) reworded related work ref[6] for clarit

Using social semantic knowledge to improve annotations in personal photo collections

Instituto Politécnico de Lisboa (IPL) e Instituto Superior de Engenharia de Lisboa (ISEL)apoio concedido pela bolsa SPRH/PROTEC/67580/2010, que apoiou parcialmente este trabalh

Methods and Applications for Summarising Free-Text Narratives in Electronic Health Records

As medical services move towards electronic health record (EHR) systems the breadth and depth of data stored at each patient encounter has increased. This growing wealth of data and investment in care systems has arguably put greater strain on services, as those at the forefront are pushed towards greater time spent in front of computers over their patients. To minimise the use of EHR systems clinicians often revert to using free-text data entry to circumvent the structured input fields. It has been estimated that approximately 80% of EHR data is within the free-text portion. Outside of their primary use, that is facilitating the direct care of the patient, secondary use of EHR data includes clinical research, clinical audits, service improvement research, population health analysis, disease and patient phenotyping, clinical trial recruitment to name but a few.This thesis presents a number of projects, previously published and original work in the development, assessment and application of summarisation methods for EHR free-text. Firstly, I introduce, define and motivate EHR free-text analysis and summarisation methods of open-domain text and how this compares to EHR free-text. I then introduce a subproblem in natural language processing (NLP) that is the recognition of named entities and linking of the entities to pre-existing clinical knowledge bases (NER+L). This leads to the first novel contribution the Medical Concept Annotation Toolkit (MedCAT) that provides a software library workflow for clinical NER+L problems. I frame the outputs of MedCAT as a form of summarisation by showing the tools contributing to published clinical research and the application of this to another clinical summarisation use-case ‘clinical coding’. I then consider methods for the textual summarisation of portions of clinical free-text. I show how redundancy in clinical text is empirically different to open-domain text discussing how this impacts text-to-text summarisation. I then compare methods to generate discharge summary sections from previous clinical notes using methods presented in prior chapters via a novel ‘guidance’ approach.I close the thesis by discussing my contributions in the context of state-of-the-art and how my work fits into the wider body of clinical NLP research. I briefly describe the challenges encountered throughout, offer my perspectives on the key enablers of clinical informatics research, and finally the potential future work that will go towards translating research impact to real-world benefits to healthcare systems, workers and patients alike