Katie Wagner and David Barner
Human experience of color results from a complex interplay of perceptual and linguistic systems. At the lowest level of perception, the human visual system transforms the visible light portion of the electromagnetic spectrum into a rich, continuous three-dimensional experience of color. Despite our ability to perceptually discriminate millions of different color shades, most languages categorize color into a number of discrete color categories. While the meanings of color words are constrained by perception, perception does not fully define them. Once color words are acquired, they may in turn influence our memory and processing speed for color, although it is unlikely that language influences the lowest levels of color perception.
One approach to examining the relationship between perception and language in forming our experience of color is to study children as they acquire color language. Children produce color words in speech for many months before acquiring adult meanings for color words. Research in this area has focused on whether children’s difficulties stem from (a) an inability to identify color properties as a likely candidate for word meanings, or alternatively (b) inductive learning of language-specific color word boundaries. Lending plausibility to the first account, there is evidence that children more readily attend to object traits like shape, rather than color, as likely candidates for word meanings. However, recent evidence has found that children have meanings for some color words before they begin to produce them in speech, indicating that in fact, they may be able to successfully identify color as a candidate for word meaning early in the color word learning process. There is also evidence that prelinguistic infants, like adults, perceive color categorically. While these perceptual categories likely constrain the meanings that children consider, they cannot fully define color word meanings because languages vary in both the number and location of color word boundaries. Recent evidence suggests that the delay in color word acquisition primarily stems from an inductive process of refining these boundaries.
Haihua Pan and Yuli Feng
This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Linguistics. Please check back later for the full article.
Cross-linguistic data can add new insights to the development of semantic theories or even induce the shift of the research paradigm. The major topics in semantic studies, such as quantification, polarity items, donkey anaphora and binding principles, negation, indexicals, tense and aspects, and eventuality, are all discussed by semanticists who work on the Chinese languages. The issues of particular interest include and are not limited to:
Clinical linguistics is the branch of linguistics that applies linguistic concepts and theories to the study of language disorders. As the name suggests, clinical linguistics is a dual-facing discipline. Although the conceptual roots of this field are in linguistics, its domain of application is the vast array of clinical disorders that may compromise the use and understanding of language. Both dimensions of clinical linguistics can be addressed through an examination of specific linguistic deficits in individuals with neurodevelopmental disorders, craniofacial anomalies, adult-onset neurological impairments, psychiatric disorders, and neurodegenerative disorders. Clinical linguists are interested in the full range of linguistic deficits in these conditions, including phonetic deficits of children with cleft lip and palate, morphosyntactic errors in children with specific language impairment, and pragmatic language impairments in adults with schizophrenia.
Like many applied disciplines in linguistics, clinical linguistics sits at the intersection of a number of areas. The relationship of clinical linguistics to the study of communication disorders and to speech-language pathology (speech and language therapy in the United Kingdom) are two particularly important points of intersection. Speech-language pathology is the area of clinical practice that assesses and treats children and adults with communication disorders. All language disorders restrict an individual’s ability to communicate freely with others in a range of contexts and settings. So language disorders are first and foremost communication disorders. To understand language disorders, it is useful to think of them in terms of points of breakdown on a communication cycle that tracks the progress of a linguistic utterance from its conception in the mind of a speaker to its comprehension by a hearer. This cycle permits the introduction of a number of important distinctions in language pathology, such as the distinction between a receptive and an expressive language disorder, and between a developmental and an acquired language disorder. The cycle is also a useful model with which to conceptualize a range of communication disorders other than language disorders. These other disorders, which include hearing, voice, and fluency disorders, are also relevant to clinical linguistics.
Clinical linguistics draws on the conceptual resources of the full range of linguistic disciplines to describe and explain language disorders. These disciplines include phonetics, phonology, morphology, syntax, semantics, pragmatics, and discourse. Each of these linguistic disciplines contributes concepts and theories that can shed light on the nature of language disorder. A wide range of tools and approaches are used by clinical linguists and speech-language pathologists to assess, diagnose, and treat language disorders. They include the use of standardized and norm-referenced tests, communication checklists and profiles (some administered by clinicians, others by parents, teachers, and caregivers), and qualitative methods such as conversation analysis and discourse analysis. Finally, clinical linguists can contribute to debates about the nosology of language disorders. In order to do so, however, they must have an understanding of the place of language disorders in internationally recognized classification systems such as the 2013 Diagnostic and Statistical Manual of Mental Disorders (DSM-5) of the American Psychiatric Association.
This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Linguistics. Please check back later for the full article.
Computational semantics performs automatic meaning analysis of natural language. Research in computational semantics designs meaning representations and develops mechanisms for automatically assigning those representations and reasoning over them. Computational semantics is not a single monolithic task but consists of many subtasks, including word sense disambiguation, multi-word expression analysis, semantic role labeling, the construction of sentence semantic structure, coreference resolution, and the automatic induction of semantic information from data.
The development of manually constructed resources has been vastly important in driving the field forward. Examples include WordNet, PropBank, FrameNet, VerbNet, and TimeBank. These resources specify the linguistic structures to be targeted in automatic analysis, and they provide high quality human-generated data that can be used to train machine learning systems. Supervised machine learning based on manually constructed resources is a widely used technique.
A second core strand has been the induction of lexical knowledge from text data. For example, words can be represented through the contexts in which they appear (called distributional vectors or embeddings), such that semantically similar words have similar representations. Or semantic relations between words can be inferred from patterns of words that link them. Wide-coverage semantic analysis always needs more data, both lexical knowledge and world knowledge, and automatic induction at least alleviates the problem.
Compositionality is a third core theme: the systematic construction of structural meaning representations of larger expressions from the meaning representations of their parts. The representations typically use logics of varying expressivity, which makes them well suited to performing automatic inferences with theorem provers.
Manual specification and automatic acquisition of knowledge are closely intertwined. Manually created resources are automatically extended or merged. The automatic induction of semantic information is guided and constrained by manually specified information, which is much more reliable. And for restricted domains, the construction of logical representations is learned from data.
It is at the intersection of manual specification and machine learning that some of the current larger questions of computational semantics are located. For instance, should we build general-purpose semantic representations, or is lexical knowledge simply too domain-specific, and would we be better off learning task-specific representations every time? When performing inference, is it more beneficial to have the solid ground of a human-generated ontology, or is it better to reason directly with text snippets for more fine-grained and gradual inference? Do we obtain a better and deeper semantic analysis as we use better and deeper manually specified linguistic knowledge, or is the future in powerful learning paradigms that learn to carry out an entire task from natural language input and output alone, without pre-specified linguistic knowledge?
Connectionism is an important theoretical framework for the study of human cognition and behavior. Also known as Parallel Distributed Processing (PDP) or Artificial Neural Networks (ANN), connectionism advocates that learning, representation, and processing of information in mind are parallel, distributed, and interactive in nature. It argues for the emergence of human cognition as the outcome of large networks of interactive processing units operating simultaneously. Inspired by findings from neural science and artificial intelligence, connectionism is a powerful computational tool, and it has had profound impact on many areas of research, including linguistics. Since the beginning of connectionism, many connectionist models have been developed to account for a wide range of important linguistic phenomena observed in monolingual research, such as speech perception, speech production, semantic representation, and early lexical development in children. Recently, the application of connectionism to bilingual research has also gathered momentum. Connectionist models are often precise in the specification of modeling parameters and flexible in the manipulation of relevant variables in the model to address relevant theoretical questions, therefore they can provide significant advantages in testing mechanisms underlying language processes.
Knut Tarald Taraldsen
This article presents different types of generative grammar that can be used as models of natural languages focusing on a small subset of all the systems that have been devised. The central idea behind generative grammar may be rendered in the words of Richard Montague: “I reject the contention that an important theoretical difference exists between formal and natural languages” (“Universal Grammar,” Theoria, 36 , 373–398).
Interest in the linguistics of humor is widespread and dates since classical times. Several theoretical models have been proposed to describe and explain the function of humor in language. The most widely adopted one, the semantic-script theory of humor, was presented by Victor Raskin, in 1985. Its expansion, to incorporate a broader gamut of information, is known as the General Theory of Verbal Humor. Other approaches are emerging, especially in cognitive and corpus linguistics. Within applied linguistics, the predominant approach is analysis of conversation and discourse, with a focus on the disparate functions of humor in conversation. Speakers may use humor pro-socially, to build in-group solidarity, or anti-socially, to exclude and denigrate the targets of the humor. Most of the research has focused on how humor is co-constructed and used among friends, and how speakers support it. Increasingly, corpus-supported research is beginning to reshape the field, introducing quantitative concerns, as well as multimodal data and analyses. Overall, the linguistics of humor is a dynamic and rapidly changing field.
Noun incorporation (NI) is a grammatical construction where a nominal, usually bearing the semantic role of an object, has been incorporated into a verb to form a complex verb or predicate. Traditionally, incorporation was considered to be a word formation process, similar to compounding or cliticization. The fact that a syntactic entity (object) was entering into the lexical process of word formation was theoretically problematic, leading to many debates about the true nature of NI as a lexical or syntactic process. The analytic complexity of NI is compounded by the clear connections between NI and other processes such as possessor raising, applicatives, and classification systems and by its relation with case, agreement, and transitivity. In some cases, it was noted that no morpho-phonological incorporation is discernable beyond perhaps adjacency and a reduced left periphery for the noun. Such cases were termed pseudo noun incorporation, as they exhibit many properties of NI, minus any actual morpho-phonological incorporation. On the semantic side, it was noted that NI often correlates with a particular interpretation in which the noun is less referential and the predicate is more general. This led semanticists to group together all phenomena with similar semantics, whether or not they involve morpho-phonological incorporation. The role of cases of morpho-phonological NI that do not exhibit this characteristic semantics, i.e., where the incorporated nominal can be referential and the action is not general, remains a matter of debate. The interplay of phonology, morphology, syntax, and semantics that is found in NI, as well as its lexical overtones, has resulted in a wide range of analyses at all levels of the grammar. What all NI constructions share is that according to various diagnostics, a thematic element, usually correlating with an internal argument, functions to a lesser extent as an independent argument and instead acts as part of a predicate. In addition to cases of incorporation between verbs and internal arguments, there are also some cases of incorporation of subjects and adverbs, which remain less well understood.
Lexical semantics is the study of word meaning. Descriptively speaking, the main topics studied within lexical semantics involve either the internal semantic structure of words, or the semantic relations that occur within the vocabulary. Within the first set, major phenomena include polysemy (in contrast with vagueness), metonymy, metaphor, and prototypicality. Within the second set, dominant topics include lexical fields, lexical relations, conceptual metaphor and metonymy, and frames. Theoretically speaking, the main theoretical approaches that have succeeded each other in the history of lexical semantics are prestructuralist historical semantics, structuralist semantics, and cognitive semantics. These theoretical frameworks differ as to whether they take a system-oriented rather than a usage-oriented approach to word-meaning research but, at the same time, in the historical development of the discipline, they have each contributed significantly to the descriptive and conceptual apparatus of lexical semantics.
Natural language ontology is a branch of both metaphysics and linguistic semantics. Its aim is to uncover the ontological categories, notions, and structures that are implicit in the use of natural language, that is, the ontology that a speaker accepts when using a language. Natural language ontology is part of “descriptive metaphysics,” to use Strawson’s term, or “naive metaphysics,” to use Fine’s term, that is, the metaphysics of appearances as opposed to foundational metaphysics, whose interest is in what there really is.
What sorts of entities natural language involves is closely linked to compositional semantics, namely what the contribution of occurrences of expressions in a sentence is taken to be. Most importantly, entities play a role as semantic values of referential terms, but also as implicit arguments of predicates and as parameters of evaluation.
Natural language appears to involve a particularly rich ontology of abstract, minor, derivative, and merely intentional objects, an ontology many philosophers are not willing to accept. At the same time, a serious investigation of the linguistic facts often reveals that natural language does not in fact involve the sort of ontology that philosophers had assumed it does.
Natural language ontology is concerned not only with the categories of entities that natural language commits itself to, but also with various metaphysical notions, for example the relation of part-whole, causation, material constitution, notions of existence, plurality and unity, and the mass-count distinction.
An important question regarding natural language ontology is what linguistic data it should take into account. Looking at the sorts of data that researchers who practice natural language ontology have in fact taken into account makes clear that it is only presuppositions, not assertions, that reflect the ontology implicit in natural language.
The ontology of language may be distinctive in that it may in part be driven specifically by language or the use of it in a discourse. Examples are pleonastic entities, discourse referents conceived of as entities of a sort, and an information-based notion of part structure involved in the semantics of plurals and mass nouns. Finally, there is the question of the universality of the ontology of natural language. Certainly, the same sort of reasoning should apply to consider it universal, in a suitable sense, as has been applied for the case of (generative) syntax.