Topic detection using paragraph vectors to support active learning in systematic reviews

AbstractSystematic reviews require expert reviewers to manually screen thousands of citations in order to identify all relevant articles to the review. Active learning text classification is a supervised machine learning approach that has been shown to significantly reduce the manual annotation workload by semi-automating the citation screening process of systematic reviews. In this paper, we present a new topic detection method that induces an informative representation of studies, to improve the performance of the underlying active learner. Our proposed topic detection method uses a neural network-based vector space model to capture semantic similarities between documents. We firstly represent documents within the vector space, and cluster the documents into a predefined number of clusters. The centroids of the clusters are treated as latent topics. We then represent each document as a mixture of latent topics. For evaluation purposes, we employ the active learning strategy using both our novel topic detection method and a baseline topic model (i.e., Latent Dirichlet Allocation). Results obtained demonstrate that our method is able to achieve a high sensitivity of eligible studies and a significantly reduced manual annotation cost when compared to the baseline method. This observation is consistent across two clinical and three public health reviews. The tool introduced in this work is available from https://nactem.ac.uk/pvtopic/

Text Classification

There is an abundance of text data in this world but most of it is raw. We need to extract information from this data to make use of it. One way to extract this information from raw text is to apply informative labels drawn from a pre-defined fixed set i.e. Text Classification. In this thesis, we focus on the general problem of text classification, and work towards solving challenges associated to binary/multi-class/multi-label classification. More specifically, we deal with the problem of (i) Zero-shot labels during testing; (ii) Active learning for text screening; (iii) Multi-label classification under low supervision; (iv) Structured label space; (v) Classifying pairs of words in raw text i.e. Relation Extraction. For (i), we use a zero-shot classification model that utilizes independently learned semantic embeddings. Regarding (ii), we propose a novel active learning algorithm that reduces problem of bias in naive active learning algorithms. For (iii), we propose neural candidate-selector architecture that starts from a set of high-recall candidate labels to obtain high-precision predictions. In the case of (iv), we proposed an attention based neural tree decoder that recursively decodes an abstract into the ontology tree. For (v), we propose using second-order relations that are derived by explicitly connecting pairs of words via context token(s) for improved relation extraction. We use a wide variety of both traditional and deep machine learning tools. More specifically, we used traditional machine learning models like multi-valued linear regression and logistic regression for (i, ii), deep convolutional neural networks for (iii), recurrent neural networks for (iv) and transformer networks for (v)

Multi-Document Summarization using Distributed Bag-of-Words Model

As the number of documents on the web is growing exponentially, multi-document summarization is becoming more and more important since it can provide the main ideas in a document set in short time. In this paper, we present an unsupervised centroid-based document-level reconstruction framework using distributed bag of words model. Specifically, our approach selects summary sentences in order to minimize the reconstruction error between the summary and the documents. We apply sentence selection and beam search, to further improve the performance of our model. Experimental results on two different datasets show significant performance gains compared with the state-of-the-art baselines

Prioritising references for systematic reviews with RobotAnalyst: A user study

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract-level decisions. The number of references that needed to be screened to identify 95% of the abstract-level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large-scale study using technology-assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings

Supporting systematic reviews using LDA-based document representations

BACKGROUND: Identifying relevant studies for inclusion in a systematic review (i.e. screening) is a complex, laborious and expensive task. Recently, a number of studies has shown that the use of machine learning and text mining methods to automatically identify relevant studies has the potential to drastically decrease the workload involved in the screening phase. The vast majority of these machine learning methods exploit the same underlying principle, i.e. a study is modelled as a bag-of-words (BOW). METHODS: We explore the use of topic modelling methods to derive a more informative representation of studies. We apply Latent Dirichlet allocation (LDA), an unsupervised topic modelling approach, to automatically identify topics in a collection of studies. We then represent each study as a distribution of LDA topics. Additionally, we enrich topics derived using LDA with multi-word terms identified by using an automatic term recognition (ATR) tool. For evaluation purposes, we carry out automatic identification of relevant studies using support vector machine (SVM)-based classifiers that employ both our novel topic-based representation and the BOW representation. RESULTS: Our results show that the SVM classifier is able to identify a greater number of relevant studies when using the LDA representation than the BOW representation. These observations hold for two systematic reviews of the clinical domain and three reviews of the social science domain. CONCLUSIONS: A topic-based feature representation of documents outperforms the BOW representation when applied to the task of automatic citation screening. The proposed term-enriched topics are more informative and less ambiguous to systematic reviewers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13643-015-0117-0) contains supplementary material, which is available to authorized users