Surfing the modeling of pos taggers in low-resource scenarios

The recent trend toward the application of deep structured techniques has revealed the limits of huge models in natural language processing. This has reawakened the interest in traditional machine learning algorithms, which have proved still to be competitive in certain contexts, particularly in low-resource settings. In parallel, model selection has become an essential task to boost performance at reasonable cost, even more so when we talk about processes involving domains where the training and/or computational resources are scarce. Against this backdrop, we evaluate the early estimation of learning curves as a practical mechanism for selecting the most appropriate model in scenarios characterized by the use of non-deep learners in resource-lean settings. On the basis of a formal approximation model previously evaluated under conditions of wide availability of training and validation resources, we study the reliability of such an approach in a different and much more demanding operational environment. Using as a case study the generation of pos taggers for Galician, a language belonging to the Western Ibero-Romance group, the experimental results are consistent with our expectations.Ministerio de Ciencia e Innovación | Ref. PID2020-113230RB-C21Ministerio de Ciencia e Innovación | Ref. PID2020-113230RB-C22Xunta de Galicia | Ref. ED431C 2020/1

Hitting the target: stopping active learning at the cost-based optimum

Active learning allows machine learning models to be trained using fewer labels while retaining similar performance to traditional supervised learning. An active learner selects the most informative data points, requests their labels, and retrains itself. While this approach is promising, it raises the question of how to determine when the model is ‘good enough’ without the additional labels required for traditional evaluation. Previously, different stopping criteria have been proposed aiming to identify the optimal stopping point. Yet, optimality can only be expressed as a domain-dependent trade-off between accuracy and the number of labels, and no criterion is superior in all applications. As a further complication, a comparison of criteria for a particular real-world application would require practitioners to collect additional labelled data they are aiming to avoid by using active learning in the first place. This work enables practitioners to employ active learning by providing actionable recommendations for which stopping criteria are best for a given real-world scenario. We contribute the first large-scale comparison of stopping criteria for pool-based active learning, using a cost measure to quantify the accuracy/label trade-off, public implementations of all stopping criteria we evaluate, and an open-source framework for evaluating stopping criteria. Our research enables practitioners to substantially reduce labelling costs by utilizing the stopping criterion which best suits their domain

Effort-driven Fact Checking

The Web constitutes a valuable source of information. In recent years, it fostered the construction of large-scale knowledge bases, such as Freebase, YAGO, and DBpedia, each storing millions of facts about society in general, and specific domains, such as politics or medicine. The open nature of the Web, with content potentially being generated by everyone, however, leads to inaccuracies and misinformation, such as fake news and exaggerated claims. Construction and maintenance of a knowledge base thus relies on fact checking, assessing the credibility of facts. Due to the inherent lack of ground truth information, fact checking cannot be done in a purely automated manner, but requires human involvement. In this paper, we propose a framework to guide users in the validation of facts, striving for a minimisation of the invested effort. Specifically, we present a probabilistic model to identify the facts for which manual validation is most beneficial. As a consequence, our approach yields a high-quality knowledge base, even if only a sample of a collection of facts is validated. Our experiments with three large-scale datasets demonstrate the efficiency and effectiveness of our approach, reaching levels of above 90\% precision of the knowledge base with only a third of the validation effort required by baseline techniques

Modeling of learning curves with applications to POS tagging

An algorithm to estimate the evolution of learning curves on the whole of a training data base, based on the results obtained from a portion and using a functional strategy, is introduced. We approximate iteratively the sought value at the desired time, independently of the learning technique used and once a point in the process, called prediction level, has been passed. The proposal proves to be formally correct with respect to our working hypotheses and includes a reliable proximity condition. This allows the user to fix a convergence threshold with respect to the accuracy finally achievable, which extends the concept of stopping criterion and seems to be effective even in the presence of distorting observations. Our aim is to evaluate the training effort, supporting decision making in order to reduce the need for both human and computational resources during the learning process. The proposal is of interest in at least three operational procedures. The first is the anticipation of accuracy gain, with the purpose of measuring how much work is needed to achieve a certain degree of performance. The second relates the comparison of efficiency between systems at training time, with the objective of completing this task only for the one that best suits our requirements. The prediction of accuracy is also a valuable item of information for customizing systems, since we can estimate in advance the impact of settings on both the performance and the development costs. Using the generation of part-of-speech taggers as an example application, the experimental results are consistent with our expectations.Ministerio de Economía y Competitividad | Ref. FFI2014-51978-C2-1-

User Guidance for Efficient Fact Checking

The Web constitutes a valuable source of information. In recent years, it fostered the construction of large-scale knowledge bases, such as Freebase, YAGO, and DBpedia. The open nature of the Web, with content potentially being generated by everyone, however, leads to inaccuracies and misinformation. Construction and maintenance of a knowledge base thus has to rely on fact checking, an assessment of the credibility of facts. Due to an inherent lack of ground truth information, such fact checking cannot be done in a purely automated manner, but requires human involvement. In this paper, we propose a comprehensive framework to guide users in the validation of facts, striving for a minimisation of the invested effort. Our framework is grounded in a novel probabilistic model that combines user input with automated credibility inference. Based thereon, we show how to guide users in fact checking by identifying the facts for which validation is most beneficial. Moreover, our framework includes techniques to reduce the manual effort invested in fact checking by determining when to stop the validation and by supporting efficient batching strategies. We further show how to handle fact checking in a streaming setting. Our experiments with three real-world datasets demonstrate the efficiency and effectiveness of our framework: A knowledge base of high quality, with a precision of above 90\%, is constructed with only a half of the validation effort required by baseline techniques

Stopping criteria for active learning of named entity recognition

Active learning is a proven method for reducing the cost of creating the training sets that are necessary for statistical NLP. However, there has been little work on stopping criteria for active learning. An operational stopping criterion is necessary to be able to use active learning in NLP applications. We investigate three different stopping criteria for active learning of named entity recognition (NER) and show that one of them, gradient-based stopping, (i) reliably stops active learning, (ii) achieves nearoptimal NER performance, (iii) and needs only about 20 % as much training data as exhaustive labeling.

Active Learning for Text Classification

Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to choose the most informative examples for manually labelling. One is an approach using an advanced aggregated confidence measurement instead of the direct output of classifiers to measure the confidence of the prediction and choose the examples with least confidence for querying. The other is a simple but effective exploration guided active learning selection strategy which uses only the notions of density and diversity, based on similarity, in its selection strategy. Second, we propose new methods of using deterministic clustering algorithms to help bootstrap the active learning process. We first illustrate the problems of using non-deterministic clustering for selecting initial training sets, showing how non-deterministic clustering methods can result in inconsistent behaviour in the active learning process. We then compare various deterministic clustering techniques and commonly used non-deterministic ones, and show that deterministic clustering algorithms are as good as non-deterministic clustering algorithms at selecting initial training examples for the active learning process. More importantly, we show that the use of deterministic approaches stabilises the active learning process. Our third direction is in the area of visualising the active learning process. We demonstrate the use of an existing visualisation technique in understanding active learning selection strategies to show that a better understanding of selection strategies can be achieved with the help of visualisation techniques. Finally, to evaluate the practicality and usefulness of active learning as a general dataset labelling methodology, it is desirable that actively labelled dataset can be reused more widely instead of being only limited to some particular classifier. We compare the reusability of popular active learning methods for text classification and identify the best classifiers to use in active learning for text classification. This thesis is concerned using active learning methods to label large unlabelled textual datasets. Our domain of interest is text classification, but most of the methods proposed are quite general and so are applicable to other domains having large collections of data with high dimensionality

Automating the anonymisation of textual corpora

[EU] Gaur egun, testu berriak etengabe sortzen doaz sare sozialetako mezu, osasun-txosten, dokumentu o zial eta halakoen ondorioz. Hala ere, testuok informazio pertsonala baldin badute, ezin dira ikerkuntzarako edota beste helburutarako baliatu, baldin eta aldez aurretik ez badira anonimizatzen. Anonimizatze hori automatikoki egitea erronka handia da eta askotan hutsetik anotatutako domeinukako datuak behar dira, ez baita arrunta helburutzat ditugun testuinguruetarako anotatutako corpusak izatea. Hala, tesi honek bi helburu ditu: (i) Gaztelaniazko elkarrizketa espontaneoz osatutako corpus anonimizatu berri bat konpilatu eta eskura jartzea, eta (ii) sortutako baliabide hau ustiatzea informazio sentiberaren identi kazio-teknikak aztertzeko, helburu gisa dugun domeinuan testu etiketaturik izan gabe. Hala, lehenengo helburuari lotuta, ES-Port izeneko corpusa sortu dugu. Telekomunikazio-ekoizle batek ahoz laguntza teknikoa ematen duenean sortu diren 1170 elkarrizketa espontaneoek osatzen dute corpusa. Ordezkatze-tekniken bidez anonimizatu da, eta ondorioz emaitza testu irakurgarri eta naturala izan da. Hamaika anonimizazio-kategoria landu dira, eta baita hizkuntzakoak eta hizkuntzatik kanpokoak diren beste zenbait anonimizazio-fenomeno ere, hala nola, kode-aldaketa, barrea, errepikapena, ahoskatze okerrak, eta abar. Bigarren helburuari lotuta, berriz, anonimizatu beharreko informazio sentibera identi katzeko, gordailuan oinarritutako Ikasketa Aktiboa erabili da, honek helburutzat baitu ahalik eta testu anotatu gutxienarekin sailkatzaile ahalik eta onena lortzea. Horretaz gain, emaitzak hobetzeko, eta abiapuntuko hautaketarako eta galderen hautaketarako estrategiak aztertzeko, Ezagutza Transferentzian oinarritutako teknikak ustiatu dira, aldez aurretik anotatuta zegoen corpus txiki bat oinarri hartuta. Emaitzek adierazi dute, lan honetan aukeratutako metodoak egokienak izan direla abiapuntuko hautaketa egiteko eta kontsulta-estrategia gisa iturri eta helburu sailkapenen zalantzak konbinatzeak Ikasketa Aktiboa hobetzen duela, ikaskuntza-kurba bizkorragoak eta sailkapen-errendimendu handiagoak lortuz iterazio gutxiagotan.[EN] A huge amount of new textual data are created day by day through social media posts, health records, official documents, and so on. However, if such resources contain personal data, they cannot be shared for research or other purposes without undergoing proper anonymisation. Automating such task is challenging and often requires labelling in-domain data from scratch since anonymised annotated corpora for the target scenarios are rarely available. This thesis has two main objectives: (i) to compile and provide a new corpus in Spanish with annotated anonymised spontaneous dialogue data, and (ii) to exploit the newly provided resource to investigate techniques for automating the sensitive data identification task, in a setting where initially no annotated data from the target domain are available. Following such aims, first, the ES-Port corpus is presented. It is a compilation of 1170 spontaneous spoken human-human dialogues from calls to the technical support service of a telecommunications provider. The corpus has been anonymised using the substitution technique, which implies the result is a readable natural text, and it contains annotations of eleven different anonymisation categories, as well as some linguistic and extra-linguistic phenomena annotations like code-switching, laughter, repetitions, mispronunciations, and so on. Next, the compiled corpus is used to investigate automatic sensitive data identification within a pool-based Active Learning framework, whose aim is to obtain the best possible classifier having to annotate as little data as possible. In order to improve such setting, Knowledge Transfer techniques from another small available anonymisation annotated corpus are explored for seed selection and query selection strategies. Results show that using the proposed seed selection methods obtain the best seeds on which to initialise the base learner's training and that combining source and target classifiers' uncertainties as query strategy improves the Active Learning process, deriving in steeper learning curves and reaching top classifier performance in fewer iterations