First published in 1983, this book represents an effort to lay the groundwork for a general approach to lexical semantics that pays heed to the needs of a theory of discourse interpretation, a theory of compositional semantics, and a theory of lexical rules. The first chapter proposes a basic framework in which to undertake lexical description and a lexical semantic analogue to the classical syntactic distinction between subcategorized for complement and adjunct. This apparatus for lexical description is expanded in the second chapter. A theory of the semantics of nuclear terms along with a proposed implementation is presented in chapter three. The fourth chapter argues that a number of regular, semantically governed valence alternations could be captured in frame representations that give rise to various kinds of realisation options. The final chapter examines interaction of these phenomena with a general account of prediction or control along with the general framework of lexical representation.

Within the framework of cognitive grammar, investigates the distribution of infinitival and finite complements--indicative and subjunctive--in the French language, emphasizing the causation/perception, modal, conceptualizing subject and impersonal constructions. Presents a fairly large array of constructions that have received considerable attention in the literature, but rather than attempting comprehensiveness, seeks only to demonstrate that French complementation can be considered in a global fashion. Achard believes the findings can be applied to other languages as well. Annotation copyrighted by Book News, Inc., Portland, OR

We are interested in the computational modeling of lexico-conceptual and narrative knowledge (e.g. how to represent the meaning of cat to reflect facts such as: it is similar to a dog, and it is typically larger than a mouse; how to characterize story, and how to identify different narratives on the same topic). On the lexico-conceptual front, we learn lexical representations with strong interpretability, as well as integrate commonsense knowledge into lexical representations. For narrative modeling, we study how to identify, extract, and generate narratives/stories acceptable to human intuition. As a methodological framework we apply the methods of Distributional Semantics (DS) — “a subfield of Natural Language Processing that learns meaning from word usages” (Herbelot, 2019) — where semantic representations (on any levels such as word, phrases, sentences, etc.) are learned at scale from data through machine learning models (Erk and Padó, 2008; Baroni and Lenci, 2010; Mikolov et al., 2013; Pennington et al., 2014). To infuse interpretability and commonsense into semantic representations (specifically lexical and event), which are typically lacking in previous works (Doran et al., 2017; Gusmao et al., 2018; Carvalho et al., 2019), we complement the data-driven scalability with a minimal amount of human knowledge annotation on a selected set of tasks and have obtained empirical evidence in support of our techniques. For narrative modeling, we draw insights from the rich body of work on scripts and narratives started from Schank and Abelson (1977) and Mooney and DeJong (1985) to Chambers and Jurafsky (2008, 2009), and proposed distributional models for the tasks narrative identification, extraction, and generation which produced state-of-the-art performance. Symbolic approaches to lexical semantics (Wierzbicka, 1996; Goddard and Wierzbicka, 2002) and narrative modeling (Schank and Abelson, 1977; Mooney and DeJong, 1985) have been fruitful on the front of theoretical studies. For example, in theoretical linguistics, Wierzbicka defined a small set of lexical semantic primitives from which complex meaning can be built compositionally; in Artificial Intelligence, Schank and Abelson formulated primitive acts which are conceptualized into semantic episodes (i.e. scripts) understandable by humans. Our focus, however, is primarily on computational approaches that need wide lexical coverage, for which DS provides a better toolkit, especially in practical applications. In this thesis, we innovate by building on the “vanilla” DS techniques (Landauer and Dumais, 1997; Mikolov et al., 2013) to address the issues listed above. Specifically, we present empirical evidence that • On the building block level, with the framework of DS, it is possible to learn highly interpretable lexical and event representations at scale and introduce human commonsense knowledge at low cost. • On the narrative level, well-designed DS modeling offers a balance of precision and scalability, solutions which are empirically stronger to complex narrative modeling questions (e.g. narrative identification, extraction and generation). Further, conducting case-studies on lexical and narrative modeling, we showcase the viability of integrating DS with traditional methods in complementation to retain the strengths of both approaches Concretely, the contributions of this thesis are summarized as follows: • Evidence from analyzing/modeling a small set of common concepts which indicates that interpretable representations can be learned for lexical concepts with minimal human annotation to realize one/few-shot learning. • Commonsense integration in lexical semantics: with carefully designed crowdsourcing, and combined with distributional methods, it is possible to substantially improve inference related to physical knowledge of the world. • Neural distributional methods perform strongly in complex narrative modeling tasks, where we demonstrate that the following techniques are particularly useful: 1) human intuition inspired iterative algorithms; 2) integration of graphical and distributional modeling; pre-trained large-scale language models

Discover vital research on the lexical and cognitive meanings of words. In this exciting book from a team of world-class researchers, in-depth articles explain a wide range of topics, including thematic roles, sense relation, ambiguity and comparison. The authors focus on the cognitive and conceptual structure of words and their meaning extensions such as coercion, metaphors and metonymies. The book features highly cited material – available in paperback for the first time since its publication – and is an essential starting point for anyone interested in lexical semantics, especially where it meets other cognitive and conceptual research.

Gives an account of the English verbal lexicon which not only systematizes the meanings of lexemes within a hierarchical framework, but also demonstrates the principled connections between meaning and highlights the syntactic complementation patterns of verbs and the patterns of conceptualization in the human mind. Explains lexical patterning and its relationship with meaning, syntax, and cognition.

Now in paperback for the first time since its original publication, the material gathered here is perfect for anyone who needs a detailed and accessible introduction to the important semantic theories. Designed for a wide audience, it will be of great value to linguists, cognitive scientists, philosophers, and computer scientists working on natural language. The book covers theories of lexical semantics, cognitively oriented approaches to semantics, compositional theories of sentence semantics, and discourse semantics. This clear, elegant explanation of the key theories in semantics research is essential reading for anyone working in the area.