Weakly supervised sentiment analysis and opinion extraction

In recent years, online reviews have become the foremost medium for users to express their satisfaction, or lack thereof, about products and services. The proliferation of user-generated reviews, combined with the rapid growth of e-commerce, results in vast amounts of opinionated text becoming available to consumers, manufacturers, and researchers alike. This has fuelled an increased focus on automated methods that attempt to discover, analyze, and distill opinions found in text. This thesis tackles the tasks of fine-grained sentiment analysis and aspect extraction, and presents a unified framework for the summarization of opinions from multiple user reviews. Two core concepts form the basis of our methodology. Firstly, the use of neural networks, whose ability to learn continuous feature representations from data, without recourse to preprocessing tools or linguistic annotations, has advanced the state-of-the-art of numerous Natural Language Processing tasks. Secondly, our belief that opinion mining systems applied to real-life applications cannot rely on expensive human annotations and should mostly take advantage of freely available review data. Specifically, the main contributions of this thesis are: (i) The creation of OPOSUM, a new Opinion Summarization corpus which contains over one million reviews from multiple domains. To test our methods, we annotated a subset of the data with fine-grained sentiment and aspect labels, as well as extractive gold-standard opinion summaries. (ii) The development of two weakly-supervised hierarchical neural models for the detection and extraction of sentiment-heavy expressions in reviews. Our first model composes segment representations hierarchically and uses an attention mechanism to differentiate between opinions and neutral statements. Our second model is based on Multiple Instance Learning (MIL), and can detect user opinions of potentially opposing polarity. Experiments demonstrate significant benefits from our MIL-based architecture. (iii) The introduction of a neural model for aspect extraction, which requires minimal human involvement. Our proposed formulation uses aspect keywords to help the model target specific aspects, and a multi-tasking objective to further improve its accuracy. (iv) A unified summarization framework which combines our sentiment and aspect detection methods, while taking redundancy into account to produce useful opinion summaries from multiple reviews. Automatic evaluation, on our opinion summarization dataset, shows significant improvements over other summarization systems in terms of extraction accuracy and similarity to reference summaries. A large-scale judgement elicitation study indicates that our summaries are also preferred by human judges

Mining arguments in scientific abstracts: Application to argumentative quality assessment

Argument mining consists in the automatic identification of argumentative structures in natural language, a task that has been recognized as particularly challenging in the scientific domain. In this work we propose SciARG, a new annotation scheme, and apply it to the identification of argumentative units and relations in abstracts in two scientific disciplines: computational linguistics and biomedicine, which allows us to assess the applicability of our scheme to different knowledge fields. We use our annotated corpus to train and evaluate argument mining models in various experimental settings, including single and multi-task learning. We investigate the possibility of leveraging existing annotations, including discourse relations and rhetorical roles of sentences, to improve the performance of argument mining models. In particular, we explore the potential offered by a sequential transfer- learning approach in which supplementary training tasks are used to fine-tune pre-trained parameter-rich language models. Finally, we analyze the practical usability of the automatically-extracted components and relations for the prediction of argumentative quality dimensions of scientific abstracts.Agencia Nacional de Investigación e InnovaciónMinisterio de Economía, Industria y Competitividad (España

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

Housing quality and lost (public) space in Croatia

IN ENGLISH: In the post-socialist period and within the current social transition context, urban and rural Croatia has, just like other transition countries, experienced many changes in the social structure and space. One example is the housing quality which is a replica of the situation in the Croatian society and has also undergone some major changes. Socially oriented housing construction co-financed by the state and the cities is in an unfavourable position compared to private housing construction. In the last twenty years the amount of the social housing construction has been only a minor part of the total contruction work in the country. For instance, out of nine newly planned residential housing developments in Zagreb, the capital city, only three have been completed and the work on the rest of them has stopped and is unlikely to continue. Private construction work prevails especially on the edge of the city and is characterised by high density housing. This type of housing construction doesn't benefit the majority of citizens in search of accommodation (price per square meter is too high, low-quality building). There is also a big problem of the community facilities (primary and secondary infrastructure, schools, kindergartens, playgrounds, green areas, sidewalks, public transport etc.). The existing globalisation-transition circumstances of the Croatian society corroborate the fact which experts of various profiles often point out: ignoring the process of (urban) planning will irreparably damage the space. The city transformation shows the absence of comprehensive urban planning which results in an ever increasing number of random buildings which do not fit in the surroundings. This leads up to yet another important issue – the shrinking and, in some cases, disappearance of public space which becomes the “lost space“. In recent years there has been a lot of building in the city core and on the edge which does not quite fit in the existing urban structure, image or the skyline of the city. The current situation in the process of planning can be characterized as a conflict and imbalance between the powerful actors (mostly political and economic) and less powerful actors (mostly professional and civil). The actors who have the political power and influence and the ones who possess the capital are forming an “alliance” between two important layers of the social structure. The lack of civil and professional actors, “lost spatial actors”, and therefore of civic aggregation is also present and that is also the cause of public space “disappearance” and undermined process of public participation. --------------- IN CROATIAN: U postsocijalističkom razdoblju i trenutnom tranzicijskom kontekstu urbana i ruralna Hrvatska su, kao i ostale tranzicijske zemlje, doživjele mnoge promjene u društvenoj strukturi i samom prostoru. Na primjeru kvalitete stanovanja kao replike stanja u hrvatskom društvu mogu se vidjeti značajne promjene. Društveno usmjerena stambena izgradnja sufinancirana od strane države i gradova je stoga rjeđa i u nepovoljnijoj je situaciji prema privatnoj stanogradnji. Zadnjih dvadeset godina udjel socijalne stambene gradnje je zanemariv u ukupnoj izgradnji na razini zemlje. Primjerice, od devet planiranih stambenih naselja izgrađenih po modelu POS-a u Zagrebu samo su tri i završena. Na ostalima je proces gradnje zastao i ne čini se da će se privesti kraju. Privatna je gradnje prisutnija, posebno na rubovima grada, a obilježava je visoka gustoća gradnje. Ovakav tip gradnje ne odgovara većini stanovnika koji su u procesu potražnje stambene nekretnine (visoka cijena kvadratnog metra, a slaba kvaliteta gradnje). Postoji također i problem nedostatne opremljenosti susjedstva (primarna i sekundarna infrastruktura, škole, vrtići, igrališta, zelene površine, pješačke staze, javni transport itd.). Navedene globalizacijsko-tranzicijske okolnosti hrvatskog društva potvrđuju ono što eksperti različitih profila ističu, a to je da će ignoriranje procesa (urbanog) planiranja nepovratno uništiti prostor gradova. Ovakve transformacije pokazuju nedostatak sustavnog urbanog planiranja što rezultira sve većim brojem zgrada koje se ne uklapaju u neposrednu okolinu. To nadalje dovodi do drugog važnog aspekta – smanjivanja i u nekim slučajevima, nestanka javnog prostora koji postaje „izgubljeni prostor“. Posljednjih je godina izgrađen velik broj zgrada, i u središtu i na rubovima grada, koje se ne uklapaju u postojeću urbanu strukturu, izgled ili vizuru grada. Ovakvu situaciju obilježavaju sukob i neravnoteža između moćnijih društvenih aktera (većinom političkih i ekonomskih) i onih manje moćnih (većinom profesionalnih i civilnih). Politički i ekonomski akteri se često povezuju u „savez“ dvaju najjačih u društvenoj strukturi. S druge strane nedostatak utjecaja civilnih i profesionalnih aktera kao „izgubljenih prostornih aktera“ dovodi do „nestanka“ javnih prostora te smanjenja važnosti procesa participacije (sudjelovanja javnosti)