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Compound and complex predicates—predicates that consist of two or more lexical items and function as the predicate of a single sentence—present an important class of linguistic objects that pertain to an enormously wide range of issues in the interactions of morphology, phonology, syntax, and semantics. Japanese makes extensive use of compounding to expand a single verb into a complex one. These compounding processes range over multiple modules of the grammatical system, thus straddling the borders between morphology, syntax, phonology, and semantics. In terms of degree of phonological integration, two types of compound predicates can be distinguished. In the first type, called tight compound predicates, two elements from the native lexical stratum are tightly fused and inflect as a whole for tense. In this group, Verb-Verb compound verbs such as arai-nagasu [wash-let.flow] ‘to wash away’ and hare-agaru [sky.be.clear-go.up] ‘for the sky to clear up entirely’ are preponderant in numbers and productivity over Noun-Verb compound verbs such as tema-doru [time-take] ‘to take a lot of time (to finish).’
The second type, called loose compound predicates, takes the form of “Noun + Predicate (Verbal Noun [VN] or Adjectival Noun [AN]),” as in post-syntactic compounds like [sinsya : koonyuu] no okyakusama ([new.car : purchase] GEN customers) ‘customer(s) who purchase(d) a new car,’ where the symbol “:” stands for a short phonological break. Remarkably, loose compounding allows combinations of a transitive VN with its agent subject (external argument), as in [Supirubaagu : seisaku] no eiga ([Spielberg : produce] GEN film) ‘a film/films that Spielberg produces/produced’—a pattern that is illegitimate in tight compounds and has in fact been considered universally impossible in the world’s languages in verbal compounding and noun incorporation.
In addition to a huge variety of tight and loose compound predicates, Japanese has an additional class of syntactic constructions that as a whole function as complex predicates. Typical examples are the light verb construction, where a clause headed by a VN is followed by the light verb suru ‘do,’ as in Tomodati wa sinsya o koonyuu (sae) sita [friend TOP new.car ACC purchase (even) did] ‘My friend (even) bought a new car’ and the human physical attribute construction, as in Sensei wa aoi me o site-iru [teacher TOP blue eye ACC do-ing] ‘My teacher has blue eyes.’ In these constructions, the nominal phrases immediately preceding the verb suru are semantically characterized as indefinite and non-referential and reject syntactic operations such as movement and deletion. The semantic indefiniteness and syntactic immobility of the NPs involved are also observed with a construction composed of a human subject and the verb aru ‘be,’ as Gakkai ni wa oozei no sankasya ga atta ‘There was a large number of participants at the conference.’ The constellation of such “word-like” properties shared by these compound and complex predicates poses challenging problems for current theories of morphology-syntax-semantics interactions with regard to such topics as lexical integrity, morphological compounding, syntactic incorporation, semantic incorporation, pseudo-incorporation, and indefinite/non-referential NPs.
Pius ten Hacken
Compounding is a word formation process based on the combination of lexical elements (words or stems). In the theoretical literature, compounding is discussed controversially, and the disagreement also concerns basic issues. In the study of compounding, the questions guiding research can be grouped into four main areas, labeled here as delimitation, classification, formation, and interpretation. Depending on the perspective taken in the research, some of these may be highlighted or backgrounded.
In the delimitation of compounding, one question is how important it is to be able to determine for each expression unambiguously whether it is a compound or not. Compounding borders on syntax and on affixation. In some theoretical frameworks, it is not a problem to have more typical and less typical instances, without a precise boundary between them. However, if, for instance, word formation and syntax are strictly separated and compounding is in word formation, it is crucial to draw this borderline precisely. Another question is which types of criteria should be used to distinguish compounding from other phenomena. Criteria based on form, on syntactic properties, and on meaning have been used. In all cases, it is also controversial whether such criteria should be applied crosslinguistically.
In the classification of compounds, the question of how important the distinction between the classes is for the theory in which they are used poses itself in much the same way as the corresponding question for the delimitation. A common classification uses headedness as a basis. Other criteria are based on the forms of the elements that are combined (e.g., stem vs. word) or on the semantic relationship between the components. Again, whether these criteria can and should be applied crosslinguistically is controversial.
The issue of the formation rules for compounds is particularly prominent in frameworks that emphasize form-based properties of compounding. Rewrite rules for compounding have been proposed, generalizations over the selection of the input form (stem or word) and of linking elements, and rules for stress assignment. Compounds are generally thought of as consisting of two components, although these components may consist of more than one element themselves. For some types of compounds with three or more components, for example copulative compounds, a nonbinary structure has been proposed.
The question of interpretation can be approached from two opposite perspectives. In a semasiological perspective, the meaning of a compound emerges from the interpretation of a given form. In an onomasiological perspective, the meaning precedes the formation in the sense that a form is selected to name a particular concept. The central question in the interpretation of compounds is how to determine the relationship between the two components. The range of possible interpretations can be constrained by the rules of compounding, by the semantics of the components, and by the context of use. A much-debated question concerns the relative importance of these factors.
Computational psycholinguistics has a long history of investigation and modeling of morphological phenomena. Several computational models have been developed to deal with the processing and production of morphologically complex forms and with the relation between linguistic morphology and psychological word representations. Historically, most of this work has focused on modeling the production of inflected word forms, leading to the development of models based on connectionist principles and other data-driven models such as Memory-Based Language Processing (MBLP), Analogical Modeling of Language (AM), and Minimal Generalization Learning (MGL). In the context of inflectional morphology, these computational approaches have played an important role in the debate between single and dual mechanism theories of cognition. Taking a different angle, computational models based on distributional semantics have been proposed to account for several phenomena in morphological processing and composition. Finally, although several computational models of reading have been developed in psycholinguistics, none of them have satisfactorily addressed the recognition and reading aloud of morphologically complex forms.
Jane Chandlee and Jeffrey Heinz
Computational phonology studies the nature of the computations necessary and sufficient for characterizing phonological knowledge. As a field it is informed by the theories of computation and phonology.
The computational nature of phonological knowledge is important because at a fundamental level it is about the psychological nature of memory as it pertains to phonological knowledge. Different types of phonological knowledge can be characterized as computational problems, and the solutions to these problems reveal their computational nature. In contrast to syntactic knowledge, there is clear evidence that phonological knowledge is computationally bounded to the so-called regular classes of sets and relations. These classes have multiple mathematical characterizations in terms of logic, automata, and algebra with significant implications for the nature of memory. In fact, there is evidence that phonological knowledge is bounded by particular subregular classes, with more restrictive logical, automata-theoretic, and algebraic characterizations, and thus by weaker models of memory.
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?
The Word and Paradigm approach to morphology associates lexemes with tables of surface forms for different morphosyntactic property sets. Researchers express their realizational theories, which show how to derive these surface forms, using formalisms such as Network Morphology and Paradigm Function Morphology. The tables of surface forms also lend themselves to a study of the implicative theories, which infer the realizations in some cells of the inflectional system from the realizations of other cells.
There is an art to building realizational theories. First, the theories should be correct, that is, they should generate the right surface forms. Second, they should be elegant, which is much harder to capture, but includes the desiderata of simplicity and expressiveness. Without software to test a realizational theory, it is easy to sacrifice correctness for elegance. Therefore, software that takes a realizational theory and generates surface forms is an essential part of any theorist’s toolbox.
Discovering implicative rules that connect the cells in an inflectional system is often quite difficult. Some rules are immediately apparent, but others can be subtle. Software that automatically analyzes an entire table of surface forms for many lexemes can help automate the discovery process.
Researchers can use Web-based computerized tools to test their realizational theories and to discover implicative rules.
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.
Construction Morphology is a theory of word structure in which the complex words of a language are analyzed as constructions, that is, systematic pairings of form and meaning. These pairings are analyzed within a Tripartite Parallel Architecture conception of grammar. This presupposes a word-based approach to the analysis of morphological structure and a strong dependence on paradigmatic relations between words. The lexicon contains both words and the constructional schemas they are instantiations of. Words and schemas are organized in a hierarchical network, with intermediate layers of subschemas. These schemas have a motivating function with respect to existing complex words and specify how new complex words can be formed.
The consequence of this view of morphology is that there is no sharp boundary between lexicon and grammar. In addition, the use of morphological patterns may also depend on specific syntactic constructions (construction-dependent morphology).
This theory of lexical relatedness also provides insight into language change such as the use of obsolete case markers as markers of specific constructions, the change of words into affixes, and the debonding of word constituents into independent words. Studies of language acquisition and word processing confirm this view of the lexicon and the nature of lexical knowledge.
Construction Morphology is also well equipped for dealing with inflection and the relationships between the cells of inflectional paradigms, because it can express how morphological schemas are related paradigmatically.
Daniel Currie Hall
The fundamental idea underlying the use of distinctive features in phonology is the proposition that the same phonetic properties that distinguish one phoneme from another also play a crucial role in accounting for phonological patterns. Phonological rules and constraints apply to natural classes of segments, expressed in terms of features, and involve mechanisms, such as spreading or agreement, that copy distinctive features from one segment to another.
Contrastive specification builds on this by taking seriously the idea that phonological features are distinctive features. Many phonological patterns appear to be sensitive only to properties that crucially distinguish one phoneme from another, ignoring the same properties when they are redundant or predictable. For example, processes of voicing assimilation in many languages apply only to the class of obstruents, where voicing distinguishes phonemic pairs such as /t/ and /d/, and ignore sonorant consonants and vowels, which are predictably voiced. In theories of contrastive specification, features that do not serve to mark phonemic contrasts (such as [+voice] on sonorants) are omitted from underlying representations. Their phonological inertness thus follows straightforwardly from the fact that they are not present in the phonological system at the point at which the pattern applies, though the redundant features may subsequently be filled in either before or during phonetic implementation.
In order to implement a theory of contrastive specification, it is necessary to have a means of determining which features are contrastive (and should thus be specified) and which ones are redundant (and should thus be omitted). A traditional and intuitive method involves looking for minimal pairs of phonemes: if [±voice] is the only property that can distinguish /t/ from /d/, then it must be specified on them. This approach, however, often identifies too few contrastive features to distinguish the phonemes of an inventory, particularly when the phonetic space is sparsely populated. For example, in the common three-vowel inventory /i a u/, there is more than one property that could distinguish any two vowels: /i/ differs from /a/ in both place (front versus back or central) and height (high versus low), /a/ from /u/ in both height and rounding, and /u/ from /i/ in both rounding and place.
Because pairwise comparison cannot identify any features as contrastive in such cases, much recent work in contrastive specification is instead based on a hierarchical sequencing of features, with specifications assigned by dividing the full inventory into successively smaller subsets. For example, if the inventory /i a u/ is first divided according to height, then /a/ is fully distinguished from the other two vowels by virtue of being low, and the second feature, either place or rounding, is contrastive only on the high vowels. Unlike pairwise comparison, this approach produces specifications that fully distinguish the members of the underlying inventory, while at the same time allowing for the possibility of cross-linguistic variation in the specifications assigned to similar inventories.
Conversational implicatures (i) are implied by the speaker in making an utterance; (ii) are part of the content of the utterance, but (iii) do not contribute to direct (or explicit) utterance content; and (iv) are not encoded by the linguistic meaning of what has been uttered. In (1), Amelia asserts that she is on a diet, and implicates something different: that she is not having cake.
(1)Benjamin:Are you having some of this chocolate cake?Amelia:I’m on a diet.
Conversational implicatures are a subset of the implications of an utterance: namely those that are part of utterance content. Within the class of conversational implicatures, there are distinctions between particularized and generalized implicatures; implicated premises and implicated conclusions; and weak and strong implicatures.
An obvious question is how implicatures are possible: how can a speaker intentionally imply something that is not part of the linguistic meaning of the phrase she utters, and how can her addressee recover that utterance content? Working out what has been implicated is not a matter of deduction, but of inference to the best explanation. What is to be explained is why the speaker has uttered the words that she did, in the way and in the circumstances that she did.
Grice proposed that rational talk exchanges are cooperative and are therefore governed by a Cooperative Principle (CP) and conversational maxims: hearers can reasonably assume that rational speakers will attempt to cooperate and that rational cooperative speakers will try to make their contribution truthful, informative, relevant and clear, inter alia, and these expectations therefore guide the interpretation of utterances. On his view, since addressees can infer implicatures, speakers can take advantage of their ability, conveying implicatures by exploiting the maxims.
Grice’s theory aimed to show how implicatures could in principle arise. In contrast, work in linguistic pragmatics has attempted to model their actual derivation. Given the need for a cognitively tractable decision procedure, both the neo-Gricean school and work on communication in relevance theory propose a system with fewer principles than Grice’s. Neo-Gricean work attempts to reduce Grice’s array of maxims to just two (Horn) or three (Levinson), while Sperber and Wilson’s relevance theory rejects maxims and the CP and proposes that pragmatic inference hinges on a single communicative principle of relevance.
Conversational implicatures typically have a number of interesting properties, including calculability, cancelability, nondetachability, and indeterminacy. These properties can be used to investigate whether a putative implicature is correctly identified as such, although none of them provides a fail-safe test. A further test, embedding, has also been prominent in work on implicatures.
A number of phenomena that Grice treated as implicatures would now be treated by many as pragmatic enrichment contributing to the proposition expressed. But Grice’s postulation of implicatures was a crucial advance, both for its theoretical unification of apparently diverse types of utterance content and for the attention it drew to pragmatic inference and the division of labor between linguistic semantics and pragmatics in theorizing about verbal communication.