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.
Francis Jeffry Pelletier
Most linguists have heard of semantic compositionality. Some will have heard that it is the fundamental truth of semantics. Others will have been told that it is so thoroughly and completely wrong that it is astonishing that it is still being taught. The present article attempts to explain all this. Much of the discussion of semantic compositionality takes place in three arenas that are rather insulated from one another: (a) philosophy of mind and language, (b) formal semantics, and (c) cognitive linguistics and cognitive psychology. A truly comprehensive overview of the writings in all these areas is not possible here. However, this article does discuss some of the work that occurs in each of these areas. A bibliography of general works, and some Internet resources, will help guide the reader to some further, undiscussed works (including further material in all three categories).
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.
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).
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:
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?
Elizabeth Closs Traugott
Traditional approaches to semantic change typically focus on outcomes of meaning change and list types of change such as metaphoric and metonymic extension, broadening and narrowing, and the development of positive and negative meanings. Examples are usually considered out of context, and are lexical members of nominal and adjectival word classes.
However, language is a communicative activity that is highly dependent on context, whether that of the ongoing discourse or of social and ideological changes. Much recent work on semantic change has focused, not on results of change, but on pragmatic enabling factors for change in the flow of speech. Attention has been paid to the contributions of cognitive processes, such as analogical thinking, production of cues as to how a message is to be interpreted, and perception or interpretation of meaning, especially in grammaticalization. Mechanisms of change such as metaphorization, metonymization, and subjectification have been among topics of special interest and debate. The work has been enabled by the fine-grained approach to contextual data that electronic corpora allow.
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.
Veneeta Dayal and Deepak Alok
Natural language allows questioning into embedded clauses. One strategy for doing so involves structures like the following: [CP-1 whi [TP DP V [CP-2 … ti …]]], where a wh-phrase that thematically belongs to the embedded clause appears in the matrix scope position. A possible answer to such a question must specify values for the fronted wh-phrase. This is the extraction strategy seen in languages like English. An alternative strategy involves a structure in which there is a distinct wh-phrase in the matrix clause. It is manifested in two types of structures. One is a close analog of extraction, but for the extra wh-phrase: [CP-1 whi [TP DP V [CP-2 whj [TP…tj…]]]]. The other simply juxtaposes two questions, rather than syntactically subordinating the second one: [CP-3 [CP-1 whi [TP…]] [CP-2 whj [TP…]]]. In both versions of the second strategy, the wh-phrase in CP-1 is invariant, typically corresponding to the wh-phrase used to question propositional arguments. There is no restriction on the type or number of wh-phrases in CP-2. Possible answers must specify values for all the wh-phrases in CP-2. This strategy is variously known as scope marking, partial wh movement or expletive wh questions. Both strategies can occur in the same language. German, for example, instantiates all three possibilities: extraction, subordinated, as well as sequential scope marking. The scope marking strategy is also manifested in in-situ languages. Scope marking has been subjected to 30 years of research and much is known at this time about its syntactic and semantic properties. Its pragmatics properties, however, are relatively under-studied. The acquisition of scope marking, in relation to extraction, is another area of ongoing research. One of the reasons why scope marking has intrigued linguists is because it seems to defy central tenets about the nature of wh scope taking. For example, it presents an apparent mismatch between the number of wh expressions in the question and the number of expressions whose values are specified in the answer. It poses a challenge for our understanding of how syntactic structure feeds semantic interpretation and how alternative strategies with similar functions relate to each other.
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.