Business Sentiment Analysis. Concept and Method for Perceived Anticipated Effort Identification

Representing a valuable human-computer interaction interface, Sentiment Analysis (SA) is applied to a wide range of problems. In the present paper, the researchers introduce a novel concept of Business Sentiment (BS) as a measurement of a Perceived Anticipated Effort (PAE) in the context of business processes (BPs). BS is considered as an emotional component of BP task contextual complexity perceived by a process worker after reading the task text. PAE is interpreted as a business process (BP) key performance indicator predicting urgency, criticality and complexity of the BP task processing. Using qualitative evaluation, the researchers proved the workability of both BS concept and its effective application method to measure PAE. As practical contributions of the research, quantitative support in a form of statistical reports and qualitative support in a form of task prioritization recommendations and time management for a BP worker are suggested

Multi-word unit processing in machine translation. Developing and using language resources for multi-word unit processing in machine translation

2011 - 2012XI n.s

Representation and Processing of Composition, Variation and Approximation in Language Resources and Tools

In my habilitation dissertation, meant to validate my capacity of and maturity for directingresearch activities, I present a panorama of several topics in computational linguistics, linguisticsand computer science.Over the past decade, I was notably concerned with the phenomena of compositionalityand variability of linguistic objects. I illustrate the advantages of a compositional approachto the language in the domain of emotion detection and I explain how some linguistic objects,most prominently multi-word expressions, defy the compositionality principles. I demonstratethat the complex properties of MWEs, notably variability, are partially regular and partiallyidiosyncratic. This fact places the MWEs on the frontiers between different levels of linguisticprocessing, such as lexicon and syntax.I show the highly heterogeneous nature of MWEs by citing their two existing taxonomies.After an extensive state-of-the art study of MWE description and processing, I summarizeMultiflex, a formalism and a tool for lexical high-quality morphosyntactic description of MWUs.It uses a graph-based approach in which the inflection of a MWU is expressed in function ofthe morphology of its components, and of morphosyntactic transformation patterns. Due tounification the inflection paradigms are represented compactly. Orthographic, inflectional andsyntactic variants are treated within the same framework. The proposal is multilingual: it hasbeen tested on six European languages of three different origins (Germanic, Romance and Slavic),I believe that many others can also be successfully covered. Multiflex proves interoperable. Itadapts to different morphological language models, token boundary definitions, and underlyingmodules for the morphology of single words. It has been applied to the creation and enrichmentof linguistic resources, as well as to morphosyntactic analysis and generation. It can be integratedinto other NLP applications requiring the conflation of different surface realizations of the sameconcept.Another chapter of my activity concerns named entities, most of which are particular types ofMWEs. Their rich semantic load turned them into a hot topic in the NLP community, which isdocumented in my state-of-the art survey. I present the main assumptions, processes and resultsissued from large annotation tasks at two levels (for named entities and for coreference), parts ofthe National Corpus of Polish construction. I have also contributed to the development of bothrule-based and probabilistic named entity recognition tools, and to an automated enrichment ofProlexbase, a large multilingual database of proper names, from open sources.With respect to multi-word expressions, named entities and coreference mentions, I pay aspecial attention to nested structures. This problem sheds new light on the treatment of complexlinguistic units in NLP. When these units start being modeled as trees (or, more generally, asacyclic graphs) rather than as flat sequences of tokens, long-distance dependencies, discontinu-ities, overlapping and other frequent linguistic properties become easier to represent. This callsfor more complex processing methods which control larger contexts than what usually happensin sequential processing. Thus, both named entity recognition and coreference resolution comesvery close to parsing, and named entities or mentions with their nested structures are analogous3to multi-word expressions with embedded complements.My parallel activity concerns finite-state methods for natural language and XML processing.My main contribution in this field, co-authored with 2 colleagues, is the first full-fledged methodfor tree-to-language correction, and more precisely for correcting XML documents with respectto a DTD. We have also produced interesting results in incremental finite-state algorithmics,particularly relevant to data evolution contexts such as dynamic vocabularies or user updates.Multilingualism is the leitmotif of my research. I have applied my methods to several naturallanguages, most importantly to Polish, Serbian, English and French. I have been among theinitiators of a highly multilingual European scientific network dedicated to parsing and multi-word expressions. I have used multilingual linguistic data in experimental studies. I believethat it is particularly worthwhile to design NLP solutions taking declension-rich (e.g. Slavic)languages into account, since this leads to more universal solutions, at least as far as nominalconstructions (MWUs, NEs, mentions) are concerned. For instance, when Multiflex had beendeveloped with Polish in mind it could be applied as such to French, English, Serbian and Greek.Also, a French-Serbian collaboration led to substantial modifications in morphological modelingin Prolexbase in its early development stages. This allowed for its later application to Polishwith very few adaptations of the existing model. Other researchers also stress the advantages ofNLP studies on highly inflected languages since their morphology encodes much more syntacticinformation than is the case e.g. in English.In this dissertation I am also supposed to demonstrate my ability of playing an active rolein shaping the scientific landscape, on a local, national and international scale. I describemy: (i) various scientific collaborations and supervision activities, (ii) roles in over 10 regional,national and international projects, (iii) responsibilities in collective bodies such as program andorganizing committees of conferences and workshops, PhD juries, and the National UniversityCouncil (CNU), (iv) activity as an evaluator and a reviewer of European collaborative projects.The issues addressed in this dissertation open interesting scientific perspectives, in whicha special impact is put on links among various domains and communities. These perspectivesinclude: (i) integrating fine-grained language data into the linked open data, (ii) deep parsingof multi-word expressions, (iii) modeling multi-word expression identification in a treebank as atree-to-language correction problem, and (iv) a taxonomy and an experimental benchmark fortree-to-language correction approaches

A comprehensive description of Pangkhua: An endangered Tibeto-Burman language of Bangladesh

This thesis offers a description of Pangkhua, an underdocumented and underdescribed Tibeto-Burman language spoken by about 2000 people in Rangamati district, Chittagong Hill Tracts, Bangladesh. This description is based on a corpus that was created during a total of twelve months of original fieldwork in Pangkhua Para village in Rangamati district, Chittagong Hill Tracts. Written in a broadly functional-typological standpoint, the thesis consists of twelve chapters covering phonology, morphology, syntax, and discourse. Some of the typologically notable features in Pangkhua include lexical tones, sesquisyllabicity, absence of a fully dedicated word class of ‘adjectives’, elaborate person marking on verbs, verb stem alternation, and the grammaticalization of t̪i ‘say’ verb as a marker of future time. As the first ever attempt at a comprehensive description of Pangkhua, this work will not only put Pangkhua on the linguistic map but will also contribute positively to comparative Tibeto-Burman linguistics by laying the groundwork for follow-on studies locating Pangkhua in its genealogical, areal, and typological contexts. As a reference grammar, it will serve linguists, more specifically language typologists and historical comparative linguists. It will also serve as a foundation from which further documentary and pedagogical materials may be developed to aid in the maintenance and revitalization of Pangkhua language and culture. Lastly, it will be a reliable source for interdisciplinary studies including linguistic anthropology, ethnic studies, cultural studies, and phylogenetics

Deep learning with knowledge graphs for fine-grained emotion classification in text

This PhD thesis investigates two key challenges in the area of fine-grained emotion detection in textual data. More specifically, this work focuses on (i) the accurate classification of emotion in tweets and (ii) improving the learning of representations from knowledge graphs using graph convolutional neural networks.The first part of this work outlines the task of emotion keyword detection in tweets and introduces a new resource called the EEK dataset. Tweets have previously been categorised as short sequences or sentence-level sentiment analysis, and it could be argued that this should no longer be the case, especially since Twitter increased its allowed character limit. Recurrent Neural Networks have become a well-established method to classify tweets over recent years, but have struggled with accurately classifying longer sequences due to the vanishing and exploding gradient descent problem. A common technique to overcome this problem has been to prune tweets to a shorter sequence length. However, this also meant that often potentially important emotion carrying information, which is often found towards the end of a tweet, was lost (e.g., emojis and hashtags). As such, tweets mostly face also problems with classifying long sequences, similar to other natural language processing tasks. To overcome these challenges, a multi-scale hierarchical recurrent neural network is proposed and benchmarked against other existing methods. The proposed learning model outperforms existing methods on the same task by up to 10.52%. Another key component for the accurate classification of tweets has been the use of language models, where more recent techniques such as BERT and ELMO have achieved great success in a range of different tasks. However, in Sentiment Analysis, a key challenge has always been to use language models that do not only take advantage of the context a word is used in but also the sentiment it carries. Therefore the second part of this work looks at improving representation learning for emotion classification by introducing both linguistic and emotion knowledge to language models. A new linguistically inspired knowledge graph called RELATE is introduced. Then a new language model is trained on a Graph Convolutional Neural Network and compared against several other existing language models, where it is found that the proposed embedding representations achieve competitive results to other LMs, whilst requiring less pre-training time and data. Finally, it is investigated how the proposed methods can be applied to document-level classification tasks. More specifically, this work focuses on the accurate classification of suicide notes and analyses whether sentiment and linguistic features are important for accurate classification

A Bird’s Eye View of Human Language Evolution

Comparative studies of linguistic faculties in animals pose an evolutionary paradox: language involves certain perceptual and motor abilities, but it is not clear that this serves as more than an input–output channel for the externalization of language proper. Strikingly, the capability for auditory–vocal learning is not shared with our closest relatives, the apes, but is present in such remotely related groups as songbirds and marine mammals. There is increasing evidence for behavioral, neural, and genetic similarities between speech acquisition and birdsong learning. At the same time, researchers have applied formal linguistic analysis to the vocalizations of both primates and songbirds. What have all these studies taught us about the evolution of language? Is the comparative study of an apparently species-specific trait like language feasible? We argue that comparative analysis remains an important method for the evolutionary reconstruction and causal analysis of the mechanisms underlying language. On the one hand, common descent has been important in the evolution of the brain, such that avian and mammalian brains may be largely homologous, particularly in the case of brain regions involved in auditory perception, vocalization, and auditory memory. On the other hand, there has been convergent evolution of the capacity for auditory–vocal learning, and possibly for structuring of external vocalizations, such that apes lack the abilities that are shared between songbirds and humans. However, significant limitations to this comparative analysis remain. While all birdsong may be classified in terms of a particularly simple kind of concatenation system, the regular languages, there is no compelling evidence to date that birdsong matches the characteristic syntactic complexity of human language, arising from the composition of smaller forms like words and phrases into larger ones

A Socio-mathematical and Structure-Based Approach to Model Sentiment Dynamics in Event-Based Text

Natural language texts are often meant to express or impact the emotions of individuals. Recognizing the underlying emotions expressed in or triggered by textual content is essential if one is to arrive at an understanding of the full meaning that textual content conveys. Sentiment analysis (SA) researchers are becoming increasingly interested in investigating natural language processing techniques as well as emotion theory in order to detect, extract, and classify the sentiments that natural language text expresses. Most SA research is focused on the analysis of subjective documents from the writer’s perspective and their classification into categorical labels or sentiment polarity, in which text is associated with a descriptive label or a point on a continuum between two polarities. Researchers often perform sentiment or polarity classification tasks using machine learning (ML) techniques, sentiment lexicons, or hybrid-based approaches. Most ML methods rely on count-based word representations that fail to take word order into account. Despite the successful use of these flat word representations in topic-modelling problems, SA problems require a deeper understanding of sentence structure, since the entire meaning of words can be reversed through negations or word modifiers. On the other hand, approaches based on semantic lexicons are limited by the relatively small number of words they contain, which do not begin to embody the extensive and growing vocabulary on the Internet. The research presented in this thesis represents an effort to tackle the problem of sentiment analysis from a different viewpoint than those underlying current mainstream studies in this research area. A cross-disciplinary approach is proposed that incorporates affect control theory (ACT) into a structured model for determining the sentiment polarity of event-based articles from the perspectives of readers and interactants. A socio-mathematical theory, ACT provides valuable resources for handling interactions between words (event entities) and for predicting situational sentiments triggered by social events. ACT models human emotions arising from social event terms through the use of multidimensional representations that have been verified both empirically and theoretically. To model human emotions regarding textual content, the first step was to develop a fine-grained event extraction algorithm that extracts events and their entities from event-based textual information using semantic and syntactic parsing techniques. The results of the event extraction method were compared against a supervised learning approach on two human-coded corpora (a grammatically correct and a grammatically incorrect structured corpus). For both corpora, the semantic-syntactic event extraction method yielded a higher degree of accuracy than the supervised learning approach. The three-dimensional ACT lexicon was also augmented in a semi-supervised fashion using graph-based label propagation built from semantic and neural network word embeddings. The word embeddings were obtained through the training of commonly used count-based and neural-network-based algorithms on a single corpus, and each method was evaluated with respect to the reconstruction of a sentiment lexicon. The results show that, relative to other word embeddings and state-of-the-art methods, combining both semantic and neural word embeddings yielded the highest correlation scores and lowest error rates. Using the augmented lexicon and ACT mathematical equations, human emotions were modelled according to different levels of granularity (i.e., at the sentence and document levels). The initial stage involved the development of a proposed entity-based SA approach that models reader emotions triggered by event-based sentences. The emotions are modelled in a three-dimensional space based on reader sentiment toward different entities (e.g., subject and object) in the sentence. The new approach was evaluated using a human-annotated news-headline corpus; the results revealed the proposed method to be competitive with benchmark ML techniques. The second phase entailed the creation of a proposed ACT-based model for predicting the temporal progression of the emotions of the interactants and their optimal behaviour over a sequence of interactions. The model was evaluated using three different corpora: fairy tales, news articles, and a handcrafted corpus. The results produced by the proposed model demonstrate that, despite the challenging sentence structure, a reasonable agreement was achieved between the estimated emotions and behaviours and the corresponding ground truth

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

Superseded: Grammatical theory: From transformational grammar to constraint-based approaches. Second revised and extended edition.

This book is superseded by the third edition, available at http://langsci-press.org/catalog/book/255. This book introduces formal grammar theories that play a role in current linguistic theorizing (Phrase Structure Grammar, Transformational Grammar/Government & Binding, Generalized Phrase Structure Grammar, Lexical Functional Grammar, Categorial Grammar, Head-​Driven Phrase Structure Grammar, Construction Grammar, Tree Adjoining Grammar). The key assumptions are explained and it is shown how the respective theory treats arguments and adjuncts, the active/passive alternation, local reorderings, verb placement, and fronting of constituents over long distances. The analyses are explained with German as the object language. The second part of the book compares these approaches with respect to their predictions regarding language acquisition and psycholinguistic plausibility. The nativism hypothesis, which assumes that humans posses genetically determined innate language-specific knowledge, is critically examined and alternative models of language acquisition are discussed. The second part then addresses controversial issues of current theory building such as the question of flat or binary branching structures being more appropriate, the question whether constructions should be treated on the phrasal or the lexical level, and the question whether abstract, non-visible entities should play a role in syntactic analyses. It is shown that the analyses suggested in the respective frameworks are often translatable into each other. The book closes with a chapter showing how properties common to all languages or to certain classes of languages can be captured. The book is a translation of the German book Grammatiktheorie, which was published by Stauffenburg in 2010. The following quotes are taken from reviews: With this critical yet fair reflection on various grammatical theories, Müller fills what was a major gap in the literature. Karen Lehmann, Zeitschrift für Rezen­sio­nen zur ger­man­is­tis­chen Sprach­wis­senschaft, 2012 Stefan Müller’s recent introductory textbook, Gram­matik­the­o­rie, is an astonishingly comprehensive and insightful survey for beginning students of the present state of syntactic theory. Wolfgang Sternefeld und Frank Richter, Zeitschrift für Sprach­wissen­schaft, 2012 This is the kind of work that has been sought after for a while [...] The impartial and objective discussion offered by the author is particularly refreshing. Werner Abraham, Germanistik, 2012   This book is a new edition of http://langsci-press.org/catalog/book/25

Superseded: Grammatical theory: From transformational grammar to constraint-based approaches. Second revised and extended edition.

This book is superseded by the third edition, available at http://langsci-press.org/catalog/book/255. This book introduces formal grammar theories that play a role in current linguistic theorizing (Phrase Structure Grammar, Transformational Grammar/Government & Binding, Generalized Phrase Structure Grammar, Lexical Functional Grammar, Categorial Grammar, Head-​Driven Phrase Structure Grammar, Construction Grammar, Tree Adjoining Grammar). The key assumptions are explained and it is shown how the respective theory treats arguments and adjuncts, the active/passive alternation, local reorderings, verb placement, and fronting of constituents over long distances. The analyses are explained with German as the object language. The second part of the book compares these approaches with respect to their predictions regarding language acquisition and psycholinguistic plausibility. The nativism hypothesis, which assumes that humans posses genetically determined innate language-specific knowledge, is critically examined and alternative models of language acquisition are discussed. The second part then addresses controversial issues of current theory building such as the question of flat or binary branching structures being more appropriate, the question whether constructions should be treated on the phrasal or the lexical level, and the question whether abstract, non-visible entities should play a role in syntactic analyses. It is shown that the analyses suggested in the respective frameworks are often translatable into each other. The book closes with a chapter showing how properties common to all languages or to certain classes of languages can be captured. The book is a translation of the German book Grammatiktheorie, which was published by Stauffenburg in 2010. The following quotes are taken from reviews: With this critical yet fair reflection on various grammatical theories, Müller fills what was a major gap in the literature. Karen Lehmann, Zeitschrift für Rezen­sio­nen zur ger­man­is­tis­chen Sprach­wis­senschaft, 2012 Stefan Müller’s recent introductory textbook, Gram­matik­the­o­rie, is an astonishingly comprehensive and insightful survey for beginning students of the present state of syntactic theory. Wolfgang Sternefeld und Frank Richter, Zeitschrift für Sprach­wissen­schaft, 2012 This is the kind of work that has been sought after for a while [...] The impartial and objective discussion offered by the author is particularly refreshing. Werner Abraham, Germanistik, 2012   This book is a new edition of http://langsci-press.org/catalog/book/25