Concepts, Frames and Cascades in Semantics, Cognition and Ontology

This open access book presents novel theoretical, empirical and experimental work exploring the nature of mental representations that support natural language production and understanding, and other manifestations of cognition. One fundamental question raised in the text is whether requisite knowledge structures can be adequately modeled by means of a uniform representational format, and if so, what exactly is its nature. Frames are a key topic covered which have had a strong impact on the exploration of knowledge representations in artificial intelligence, psychology and linguistics; cascades are a novel development in frame theory. Other key subject areas explored are: concepts and categorization, the experimental investigation of mental representation, as well as cognitive analysis in semantics. This book is of interest to students, researchers, and professionals working on cognition in the fields of linguistics, philosophy, and psychology

Meaning in Distributions : A Study on Computational Methods in Lexical Semantics

This study investigates the connection between lexical items' distributions and their meanings from the perspective of computational distributional operations. When applying computational methods in meaning-related research, it is customary to refer to the so-called distributional hypothesis, according to which differences in distributions and meanings are mutually correlated. However, making use of such a hypothesis requires critical explication of the concept of distribution and plausible arguments for why any particular distributional structure is connected to a particular meaning-related phenomenon. In broad strokes, the present study seeks to chart the major differences in how the concept of distribution is conceived in structuralist/autonomous and usage-based/functionalist theoretical families of contemporary linguistics. The two theoretical positions on distributions are studied for identifying how meanings could enter as enabling or constraining factors in them. The empirical part of the study comprises two case studies. In the first one, three pairs of antonymical adjectives (köyhä/rikas, sairas/terve and vanha/nuori) are studied distributionally. Very narrow bag-of-word vector representations of distributions show how the dimensions on which relevant distributional similarities are based already conflate unexpected and varied range of linguistic phenomena, spanning from syntax-oriented conceptual constrainment to connotations, pragmatic patterns and affectivity. Thus, the results simultaneously corroborate the distributional hypothesis and challenge its over-generalized, uncritical applicability. For the study of meaning, distributional and semantic spaces cannot be treated as analogous by default. In the second case study, a distributional operation is purposefully built for answering a research question related to historical development of Finnish social law terminology in the period of 1860–1910. Using a method based on interlinked collocation networks, the study shows how the term vaivainen (‘pauper, beggar, measly’) receded from the prestigious legal and administrative registers during the studied period. Corroborating some of the findings of the previous parts of this dissertation, the case study shows how structures found in distributional representations cannot be satisfactorily explained without relying on semantic, pragmatic and discoursal interpretations. The analysis leads to confirming the timeline of the studied word use in the given register. It also shows how the distributional methods based on networked patterns of co-occurrence highlight incomparable structures of very different nature and skew towards frequent occurrence types prevalent in the data.Nykyaikaiset laskennalliset menetelmät suorittavat suurista tekstiaineistoista koottujen tilastollisten mallien avulla lähes virheettömästi monia sanojen merkitysten ymmärtämistä edellyttäviä tehtäviä. Kielitieteellisen metodologian kannalta onkin kiinnostavaa, miten tällaiset menetelmät sopivat kiellisten rakenteiden merkitysten lingvistiseen tutkimukseen. Tämä väitöstutkimus lähestyy kysymystä sanasemantiikan näkökulmasta ja pyrkii sekä teoreettisesti että empiirisesti kuvaamaan minkälaisia merkityksen lajeja pelkkiin sanojen sekvensseihin perustuvat laskennalliset menetelmät kykenevät tavoittamaan. Väitöstutkimus koostuu kahdesta osatutkimuksesta, joista ensimmäisessä tutkitaan kolmea vastakohtaista adjektiiviparia Suomi24-aineistosta kootun vektoriavaruusmallin avulla. Tulokset osoittavat, miten jo hyvin rajatut sekvenssiympäristöt sisältävät informaatiota käsitteellisten merkitysten lisäksi myös muun muassa niiden konnotaatioista ja affektiivisuudesta. Sekvenssiympäristön tuottama kuva merkityksestä on kuitenkin kattavuudeltaan ennalta-arvaamaton ja ne kielekäyttötavat, jotka tutkimusaineistossa ovat yleisiä vaikuttavat selvästi siihen mitä merkityksen piirteitä tulee näkyviin. Toisessa osatutkimuksessa jäljitetään erään sosiaalioikeudellisen termin, vaivaisen, historiaa 1800-luvun loppupuolella Kansalliskirjaston historiallisesta digitaalisesta sanomalehtikokoelmasta. Myötäesiintymäverkostojen avulla pyritään selvittämään miten se katosi oikeuskielestä tunnistamalla aineistosta hallinnollis-juridista rekisteriä vastaava rakenne ja seuraamalla vaivaisen asemaa siinä. Menetelmänä käytetyt myötäesiintymäverkostot eivät kuitenkaan edusta puhtaasti mitään tiettyä rekisteriä, vaan sekoittavat itseensä piirteitä erilaisista kategorioista, joilla kielen käyttöä on esimerkiksi tekstintutkimuksessa kuvattu. Tiheimmät verkostot muodostuvat rekisterien, genrejen, tekstityyppien ja sanastollisen koheesion yhteisvaikutuksesta. Osatutkimuksen tulokset antavat viitteitä siitä, että tämä on yleinen piirre monissa samankaltaisissa menetelmissä, mukaan lukien yleiset aihemallit

Mental Representation of Word Family Structure: The Case of German Infinitives, Conversion Nouns and Other Morphologically Related Forms

Published: 27 July 2022This study investigates how two non-finite forms, infinitives and conversion nouns, are represented in the mind of L1 and L2 speakers and what is their relationship to other members of the corresponding word family. German native speakers and proficient German learners with Czech as L1 participated in four overt priming experiments involving a grammatical judgement task. We investigated the relationship between infinitives (Experiment 1) and conversion nouns (Experiment 2) and formally identical verbal or noun forms. We further focussed on the relationship between conversion nouns and regular nominal derivation forms with two derivational suffixes: -er and -ung (Experiments 3 and 4). Our results show that the two non-finite forms differ in their relations to other members of a word family and do not constitute a special class of non-finites as suggested in previous literature. While German infinitives seem to be closer related to finite verbal forms, conversion nouns behave in the same way as other regular nominal derivatives within the same word family. As for the German L1 and L2 contrast, no significant difference in the mental representation of the examined forms was found. This finding suggests that with respect to the explored phenomena, proficient learners rely on the same linguistic organisation as L1 speakers.This work was supported by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; grant number BO 3615/6-2 to DB) and by Universität Leipzig within the program of Open Access Publishing

The semantic transparency of English compound nouns

What is semantic transparency, why is it important, and which factors play a role in its assessment? This work approaches these questions by investigating English compound nouns. The first part of the book gives an overview of semantic transparency in the analysis of compound nouns, discussing its role in models of morphological processing and differentiating it from related notions. After a chapter on the semantic analysis of complex nominals, it closes with a chapter on previous attempts to model semantic transparency. The second part introduces new empirical work on semantic transparency, introducing two different sets of statistical models for compound transparency. In particular, two semantic factors were explored: the semantic relations holding between compound constituents and the role of different readings of the constituents and the whole compound, operationalized in terms of meaning shifts and in terms of the distribution of specifc readings across constituent families. All semantic annotations used in the book are freely available

Decompositional Semantics for Events, Participants, and Scripts in Text

This thesis presents a sequence of practical and conceptual developments in decompositional meaning representations for events, participants, and scripts in text under the framework of Universal Decompositional Semantics (UDS) (White et al., 2016a). Part I of the thesis focuses on the semantic representation of individual events and their participants. Chapter 3 examines the feasibility of deriving semantic representations of events from dependency syntax; we demonstrate that predicate- argument structure may be extracted from syntax, but other desirable semantic attributes are not directly discernible. Accordingly, we present in Chapters 4 and 5 state of the art models for predicting these semantic attributes from text. Chapter 4 presents a model for predicting semantic proto-role labels (SPRL), attributes of participants in events based on Dowty’s seminal theory of thematic proto-roles (Dowty, 1991). In Chapter 5 we present a model of event factuality prediction (EFP), the task of determining whether an event mentioned in text happened (according to the meaning of the text). Both chapters include extensive experiments on multi-task learning for improving performance on each semantic prediction task. Taken together, Chapters 3, 4, and 5 represent the development of individual components of a UDS parsing pipeline. In Part II of the thesis, we shift to modeling sequences of events, or scripts (Schank and Abelson, 1977). Chapter 7 presents a case study in script induction using a collection of restaurant narratives from an online blog to learn the canonical “Restaurant Script.” In Chapter 8, we introduce a simple discriminative neural model for script induction based on narrative chains (Chambers and Jurafsky, 2008) that outperforms prior methods. Because much existing work on narrative chains employs semantically impoverished representations of events, Chapter 9 draws on the contributions of Part I to learn narrative chains with semantically rich, decompositional event representations. Finally, in Chapter 10, we observe that corpus based approaches to script induction resemble the task of language modeling. We explore the broader question of the relationship between language modeling and acquisition of common-sense knowledge, and introduce an approach that combines language modeling and light human supervision to construct datasets for common-sense inference