Automatic extraction of agendas for action from news coverage of violent conflict

Words can make people act. Indeed, a simple phrase ‘Will you, please, open the window?’ can cause a person to do so. However, does this still hold, if the request is communicated indirectly via mass media and addresses a large group of people? Different disciplines have approached this problem from different angles, showing that there is indeed a connection between what is being called for in media and what people do. This dissertation, being an interdisciplinary work, bridges different perspectives on the problem and explains how collective mobilisation happens, using the novel term ‘agenda for action’. It also shows how agendas for action can be extracted from text in automated fashion using computational linguistics and machine learning. To demonstrate the potential of agenda for action, the analysis of The NYT and The Guardian coverage of chemical weapons crises in Syria in 2013 is performed. Katsiaryna Stalpouskaya has always been interested in applied and computational linguistics. Pursuing this interest, she joined FP7 EU-INFOCORE project in 2014, where she was responsible for automated content analysis. Katsiaryna’s work on the project resulted in a PhD thesis, which she successfully defended at Ludwig-Maximilians-Universität München in 2019. Currently, she is working as a product owner in the field of text and data analysis

Chatbol, a chatbot for the Spanish “La Liga”

Segura C., Palau À., Luque J., Costa-Jussà M.R., Banchs R.E. (2019) Chatbol, a Chatbot for the Spanish “La Liga”. In: D'Haro L., Banchs R., Li H. (eds) 9th International Workshop on Spoken Dialogue System Technology. Lecture Notes in Electrical Engineering, vol 579. Springer, SingaporeThis work describes the development of a social chatbot for the football domain. The chatbot, named chatbol, aims at answering a wide variety of questions related to the Spanish football league “La Liga”. Chatbol is deployed as a Slack client for text-based input interaction with users. One of the main Chatbol’s components, a NLU block, is trained to extract the intents and associated entities related to user’s questions about football players, teams, trainers and fixtures. The information for the entities is obtained by making sparql queries to Wikidata site in real time. Then, the retrieved data is used to update the specific chatbot responses. As a fallback strategy, a retrieval-based conversational engine is incorporated to the chatbot system. It allows for a wider variety and freedom of responses, still football oriented, for the case when the NLU module was unable to reply with high confidence to the user. The retrieval-based response database is composed of real conversations collected both from a IRC football channel and from football-related excerpts picked up across movie captions, extracted from the OpenSubtitles databasePeer ReviewedPostprint (author's final draft

Automatic extraction of agendas for action from news coverage of violent conflict

Words can make people act. Indeed, a simple phrase ‘Will you, please, open the window?’ can cause a person to do so. However, does this still hold, if the request is communicated indirectly via mass media and addresses a large group of people? Different disciplines have approached this problem from different angles, showing that there is indeed a connection between what is being called for in media and what people do. This dissertation, being an interdisciplinary work, bridges different perspectives on the problem and explains how collective mobilisation happens, using the novel term ‘agenda for action’. It also shows how agendas for action can be extracted from text in automated fashion using computational linguistics and machine learning. To demonstrate the potential of agenda for action, the analysis of The NYT and The Guardian coverage of chemical weapons crises in Syria in 2013 is performed. Katsiaryna Stalpouskaya has always been interested in applied and computational linguistics. Pursuing this interest, she joined FP7 EU-INFOCORE project in 2014, where she was responsible for automated content analysis. Katsiaryna’s work on the project resulted in a PhD thesis, which she successfully defended at Ludwig-Maximilians-Universität München in 2019. Currently, she is working as a product owner in the field of text and data analysis

On automatic emotion classification using acoustic features

In this thesis, we describe extensive experiments on the classification of emotions from speech using acoustic features. This area of research has important applications in human computer interaction. We have thoroughly reviewed the current literature and present our results on some of the contemporary emotional speech databases. The principal focus is on creating a large set of acoustic features, descriptive of different emotional states and finding methods for selecting a subset of best performing features by using feature selection methods. In this thesis we have looked at several traditional feature selection methods and propose a novel scheme which employs a preferential Borda voting strategy for ranking features. The comparative results show that our proposed scheme can strike a balance between accurate but computationally intensive wrapper methods and less accurate but computationally less intensive filter methods for feature selection. By using the selected features, several schemes for extending the binary classifiers to multiclass classification are tested. Some of these classifiers form serial combinations of binary classifiers while others use a hierarchical structure to perform this task. We describe a new hierarchical classification scheme, which we call Data-Driven Dimensional Emotion Classification (3DEC), whose decision hierarchy is based on non-metric multidimensional scaling (NMDS) of the data. This method of creating a hierarchical structure for the classification of emotion classes gives significant improvements over other methods tested. The NMDS representation of emotional speech data can be interpreted in terms of the well-known valence-arousal model of emotion. We find that this model does not givea particularly good fit to the data: although the arousal dimension can be identified easily, valence is not well represented in the transformed data. From the recognitionresults on these two dimensions, we conclude that valence and arousal dimensions are not orthogonal to each other. In the last part of this thesis, we deal with the very difficult but important topic of improving the generalisation capabilities of speech emotion recognition (SER) systems over different speakers and recording environments. This topic has been generally overlooked in the current research in this area. First we try the traditional methods used in automatic speech recognition (ASR) systems for improving the generalisation of SER in intra– and inter–database emotion classification. These traditional methods do improve the average accuracy of the emotion classifier. In this thesis, we identify these differences in the training and test data, due to speakers and acoustic environments, as a covariate shift. This shift is minimised by using importance weighting algorithms from the emerging field of transfer learning to guide the learning algorithm towards that training data which gives better representation of testing data. Our results show that importance weighting algorithms can be used to minimise the differences between the training and testing data. We also test the effectiveness of importance weighting algorithms on inter–database and cross-lingual emotion recognition. From these results, we draw conclusions about the universal nature of emotions across different languages

Ensemble deep learning: A review

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions

A Bi-Encoder LSTM Model for Learning Unstructured Dialogs

Creating a data-driven model that is trained on a large dataset of unstructured dialogs is a crucial step in developing a Retrieval-based Chatbot systems. This thesis presents a Long Short Term Memory (LSTM) based Recurrent Neural Network architecture that learns unstructured multi-turn dialogs and provides implementation results on the task of selecting the best response from a collection of given responses. Ubuntu Dialog Corpus Version 2 (UDCv2) was used as the corpus for training. Ryan et al. (2015) explored learning models such as TF-IDF (Term Frequency-Inverse Document Frequency), Recurrent Neural Network (RNN) and a Dual Encoder (DE) based on Long Short Term Memory (LSTM) model suitable to learn from the Ubuntu Dialog Corpus Version 1 (UDCv1). We use this same architecture but on UDCv2 as a benchmark and introduce a new LSTM based architecture called the Bi-Encoder LSTM model (BE) that achieves 0.8%, 1.0% and 0.3% higher accuracy for Recall@1, Recall@2 and Recall@5 respectively than the DE model. In contrast to the DE model, the proposed BE model has separate encodings for utterances and responses. The BE model also has a different similarity measure for utterance and response matching than that of the benchmark model. We further explore the BE model by performing various experiments. We also show results on experiments performed by using several similarity functions, model hyper-parameters and word embeddings on the proposed architecture

Minimal supervision for language learning: bootstrapping global patterns from local knowledge

A fundamental step in sentence comprehension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate arguments, and assign semantic roles to those constituents. Each step depends on prior lexical and syntactic knowledge. Where do children begin in solving this problem when learning their first languages? To experiment with different representations that children may use to begin understanding language, we have built a computational model for this early point in language acquisition. This system, BabySRL, learns from transcriptions of natural child-directed speech and makes use of psycholinguistically plausible background knowledge and realistically noisy semantic feedback to begin to classify sentences at the level of ``who does what to whom.'' Starting with simple, psycholinguistically-motivated representations of sentence structure, the BabySRL is able to learn from full semantic feedback, as well as a supervision signal derived from partial semantic background knowledge. In addition we combine the BabySRL with an unsupervised Hidden Markov Model part-of-speech tagger, linking clusters with syntactic categories using background noun knowledge so that they can be used to parse input for the SRL system. The results show that proposed shallow representations of sentence structure are robust to reductions in parsing accuracy, and that the contribution of alternative representations of sentence structure to successful semantic role labeling varies with the integrity of the parsing and argument-identification stages. Finally, we enable the BabySRL to improve both an intermediate syntactic representation and its final semantic role classification. Using this system we show that it is possible for a simple learner in a plausible (noisy) setup to begin comprehending simple semantics when initialized with a small amount of concrete noun knowledge and some simple syntax-semantics mapping biases, before acquiring any specific verb knowledge