Graph Theory and Universal Grammar

Tese arquivada ao abrigo da Portaria nº 227/2017 de 25 de Julho-Registo de Grau EstrangeiroIn the last few years, Noam Chomsky (1994; 1995; 2000; 2001) has gone quite far in the direction of simplifying syntax, including eliminating X-bar theory and the levels of D-structure and S-structure entirely, as well as reducing movement rules to a combination of the more primitive operations of Copy and Merge. What remain in the Minimalist Program are the operations Merge and Agree and the levels of LF (Logical Form) and PF (Phonological form). My doctoral thesis attempts to offer an economical theory of syntactic structure from a graph-theoretic point of view (cf. Diestel, 2005), with special emphases on the elimination of category and projection labels and the Inclusiveness Condition (Chomsky 1994). The major influences for the development of such a theory have been Chris Collins’ (2002) seminal paper “Eliminating labels”, John Bowers (2001) unpublished manuscript “Syntactic Relations” and the Cartographic Paradigm (see Belletti, Cinque and Rizzi’s volumes on OUP for a starting point regarding this paradigm). A syntactic structure will be regarded here as a graph consisting of the set of lexical items, the set of relations among them and nothing more

The language-cognition interface in bilinguals: an evaluation of the conceptual transfer hypothesis

Praca podejmuje temat wpływu języka na kategorie konceptualne u osób dwujęzycznych. Poruszana problematyka omawiana jest na podstawie najnowszych teorii pamięci bilingwalnej oraz stworzonej na ich kanwie hipotezy transferu konceptualnego autorstwa Scotta Jarvisa i Anety Pavlenko. Część teoretyczna przedstawia strukturę pamięci bilingwalnej, zwanej również słownikiem wewnętrznym, modele sfery konceptualnej oraz istniejące pomiędzy poziomem językowym i konceptualnym zależności. Te ostatnie rozpatrywane są przez pryzmat teorii względności językowej i jej zmodyfikowanych wersji: teorii „myślenie dla mowy” (ang. Thinking for Speaking) Dana Slobina, jak również hipotezy Christiane von Stutterheim. Ostatnim elementem dyskusji jest prezentacja hipotezy transferu konceptualnego oraz jej ocena pod kątem merytorycznym i empirycznym. Część badawcza przedstawia dwa projekty zrealizowane zgodnie z zaleceniami autorów hipotezy transferu konceptualnego. Projekt 1. dotyczy kategoryzacji semantycznej oraz niewerbalnej. Badane kategorie semantyczne oparte są na eksplikacjach Anny Wierzbickiej i dotyczą relacji międzyludzkich (przyjaciel, friend, kolega itd.). Projekt 2. to analiza ram konceptualizacyjnych pod kątem wydarzeń przedstawiających ruch ukierunkowany oraz konstrukcji narracji w pisemnych relacjach z obejrzanego filmu animowanego. Uzyskane dane w języku polskim i angielskim stanowią podstawę wniosków, które zaprezentowano w ostatnim rozdziale pracy. Badania przeprowadzono w Polsce i krajach anglojęzycznych (w Anglii i Irlandii). W skład badanych populacji weszli monolingwalni Polacy i rodzimi użytkownicy języka angielskiego (ang. native speakers) oraz Polacy posługujący się językiem angielskim w warunkach naturalnych (emigranci) i szkolnych (studenci filologii angielskiej). Każda z grup monolingwalnych uczestniczyła w sesjach badawczych dotyczących odpowiednio języka polskiego i angielskiego. Osoby dwujęzyczne testowane były w obydwu językach. Dane zebrano za pomocą scenariuszy sytuacyjnych, kwestionariuszy, oceny podobieństwa, a także opisu narracyjnego krótkometrażowego filmu animowanego pt. Katedra w reżyserii Tomasza Bagińskiego

Proceedings

Proceedings of the NODALIDA 2011 Workshop Constraint Grammar Applications. Editors: Eckhard Bick, Kristin Hagen, Kaili Müürisep, Trond Trosterud. NEALT Proceedings Series, Vol. 14 (2011), vi+69 pp. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/19231

Cancer Hallmark Text Classification Using Convolutional Neural Networks

Methods based on deep learning approaches have recently achieved state-of-the-art performance in a range of machine learning tasks and are increasingly applied to natural language processing (NLP). Despite strong results in various established NLP tasks involving general domain texts, here is only limited work applying these models to biomedical NLP. In this paper, we consider a Convolutional Neural Network (CNN) approach to biomedical text classification. Evaluation using a recently introduced cancer domain dataset involving the categorization of documents according to the well-established hallmarks of cancer shows that a basic CNN model can achieve a level of performance competitive with a Support Vector Machine (SVM) trained using complex manually engineered features optimized to the task. We further show that simple modifications to the CNN hyperparameters, initialization, and training process allow the model to notably outperform the SVM, establishing a new state of the art result at this task. We make all of the resources and tools introduced in this study available under open licenses from https://cambridgeltl.github.io/cancer-hallmark-cnn/ .The first author is funded by the Commonwealth Scholarship and the Cambridge Trust. This work is supported by Medical Research Council grant MR/M013049/1 and the Google Faculty Award

Isomorphy and Syntax-Prosody Relations in English

abstract: This dissertation investigates the precise degree to which prosody and syntax are related. One possibility is that the syntax-prosody mapping is one-to-one (“isomorphic”) at an underlying level (Chomsky & Halle 1968, Selkirk 1996, 2011, Ito & Mester 2009). This predicts that prosodic units should preferably match up with syntactic units. It is also possible that the mapping between these systems is entirely non-isomorphic, with prosody being influenced by factors from language perception and production (Wheeldon & Lahiri 1997, Lahiri & Plank 2010). In this work, I argue that both perspectives are needed in order to address the full range of phonological phenomena that have been identified in English and related languages, including word-initial lenition/flapping, word-initial segment-deletion, and vowel reduction in function words, as well as patterns of pitch accent assignment, final-pronoun constructions, and the distribution of null complementizer allomorphs. In the process, I develop models for both isomorphic and non-isomorphic phrasing. The former is cast within a Minimalist syntactic framework of Merge/Label and Bare Phrase Structure (Chomsky 2013, 2015), while the latter is characterized by a stress-based algorithm for the formation of phonological domains, following Lahiri & Plank (2010).Dissertation/ThesisDoctoral Dissertation English 201

Measuring associational thinking through word embeddings

[EN] The development of a model to quantify semantic similarity and relatedness between words has been the major focus of many studies in various fields, e.g. psychology, linguistics, and natural language processing. Unlike the measures proposed by most previous research, this article is aimed at estimating automatically the strength of associative words that can be semantically related or not. We demonstrate that the performance of the model depends not only on the combination of independently constructed word embeddings (namely, corpus- and network-based embeddings) but also on the way these word vectors interact. The research concludes that the weighted average of the cosine-similarity coefficients derived from independent word embeddings in a double vector space tends to yield high correlations with human judgements. Moreover, we demonstrate that evaluating word associations through a measure that relies on not only the rank ordering of word pairs but also the strength of associations can reveal some findings that go unnoticed by traditional measures such as Spearman's and Pearson's correlation coefficients.s Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], the Spanish ¿Agencia Estatal de Investigación¿ [grant number PID2020-112827GB-I00 / AEI / 10.13039/501100011033], and the European Union¿s Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Periñán-Pascual, C. (2022). Measuring associational thinking through word embeddings. Artificial Intelligence Review. 55(3):2065-2102. https://doi.org/10.1007/s10462-021-10056-62065210255