Formal properties of now revisited

The traditional view is that now is a pure indexical, denoting the utterance time. Yet, despite its initial appeal, the view has faced criticism. A range of data reveal now allows for discourse-bound (i.e., anaphoric) uses, and can occur felicitously with the past tense. The reaction to this has typically been to treat now as akin to a true demonstrative, selecting the prominent time supplied by the non-linguistic context or prior discourse. We argue this is doubly mistaken. The first mistake concerns the semantic value of now which is not a time, but a state --- the consequent state of a prominent event. The second is that now is a pure indexical after all, insofar as its interpretation is determined without recourse to extra-linguistic supplementation. Specifically, we argue that any occurrence of now selects the consequent state of the most prominent event, where event-prominence is linguistically maintained through prominence-affecting updates contributed by coherence relations. Our analysis accounts straightforwardly for a wide range of discourse initial and discourse bound uses of now while giving it a simple indexical meaning

Data and Methods for Reference Resolution in Different Modalities

One foundational goal of artificial intelligence is to build intelligent agents which interact with humans, and to do so, they must have the capacity to infer from human communication what concept is being referred to in a span of symbols. They should be able, like humans, to map these representations to perceptual inputs, visual or otherwise. In NLP, this problem of discovering which spans of text are referring to the same real-world entity is called Coreference Resolution. This dissertation expands this problem to go beyond text and maps concepts referred to by text spans to concepts represented in images. This dissertation also investigates the complex and hard nature of real world coreference resolution. Lastly, this dissertation expands upon the definition of references to include abstractions referred by non-contiguous text distributions. A central theme throughout this thesis is the paucity of data in solving hard problems of reference, which it addresses by designing several datasets. To investigate hard text coreference this dissertation analyses a domain of coreference heavy text, namely questions present in the trivia game of quiz bowl and creates a novel dataset. Solving quiz bowl questions requires robust coreference resolution and world knowledge, something humans possess but current models do not. This work uses distributional semantics for world knowledge. Also, this work addresses the sub-problems of coreference like mention detection. Next, to investigate complex visual representations of concepts, this dissertation uses the domain of paintings. Mapping spans of text in descriptions of paintings to regions of paintings being described by that text is a non-trivial problem because paintings are sufficiently harder than natural images. Distributional semantics are again used here. Finally, to discover prototypical concepts present in distributed rather than contiguous spans of text, this dissertation investigates a source which is rich in prototypical concepts, namely movie scripts. All movie narratives, character arcs, and character relationships, are distilled to sequences of interconnected prototypical concepts which are discovered using unsupervised deep learning models, also using distributional semantics. I conclude this dissertation by discussing potential future research in downstream tasks which can be aided by discovery of referring multi-modal concepts

Latent semantic indexing eta ikasketa automatikoa hizkuntzaren prozesamenduaren arloan: testu-sailkatzea, hitzen adiera-desanbiguatzea eta korreferentzia-ebaztea SVD bidezko dimentsio murrizketa eta multi-sailkatzailea konbinatuz

220 p

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