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Through rigorous training in a variety of theoretical, empirical, and analytical approaches, students acquire the knowledge and skills they need for in-depth exploration of fundamental questions related to meaning, intention, and reference. The small size of the program enables close personal and intellectual contact between students and faculty, and allows student majors to design a course of study that reflects their individual goals and interests.

All students entering as freshmen in Fall and after will pursue coursework in the BU Hub, a general education program that is integrated into the entire undergraduate experience. BU Hub requirements are flexible and can be satisfied in many different ways, through coursework in and beyond the major. Students can satisfy up to 15 of the 26 required Hub units from courses counting for the joint major. Remaining BU Hub requirements will be satisfied by selecting from a wide range of available courses outside the major or, in some cases, co-curricular experiences.

Thirteen courses are required for the major, with at least six courses each in Linguistics and Philosophy. The lexicon is a catalogue of words and terms that are stored in a speaker's mind. The lexicon consists of words and bound morphemes , which are parts of words that can't stand alone, like affixes. In some analyses, compound words and certain classes of idiomatic expressions and other collocations are also considered to be part of the lexicon. Dictionaries represent attempts at listing, in alphabetical order, the lexicon of a given language; usually, however, bound morphemes are not included.

Lexicography , closely linked with the domain of semantics, is the science of mapping the words into an encyclopedia or a dictionary. The creation and addition of new words into the lexicon is called coining or neologization, [35] and the new words are called neologisms. It is often believed that a speaker's capacity for language lies in the quantity of words stored in the lexicon. However, this is often considered a myth by linguists. The capacity for the use of language is considered by many linguists to lie primarily in the domain of grammar, and to be linked with competence , rather than with the growth of vocabulary.

Even a very small lexicon is theoretically capable of producing an infinite number of sentences. As constructed popularly through the Sapir—Whorf hypothesis , relativists believe that the structure of a particular language is capable of influencing the cognitive patterns through which a person shapes his or her world view. Universalists believe that there are commonalities between human perception as there is in the human capacity for language, while relativists believe that this varies from language to language and person to person. While the Sapir—Whorf hypothesis is an elaboration of this idea expressed through the writings of American linguists Edward Sapir and Benjamin Lee Whorf , it was Sapir's student Harry Hoijer who termed it thus.

The 20th century German linguist Leo Weisgerber also wrote extensively about the theory of relativity. Relativists argue for the case of differentiation at the level of cognition and in semantic domains. The emergence of cognitive linguistics in the s also revived an interest in linguistic relativity. Thinkers like George Lakoff have argued that language reflects different cultural metaphors, while the French philosopher of language Jacques Derrida 's writings have been seen to be closely associated with the relativist movement in linguistics, especially through deconstruction [36] and was even heavily criticized in the media at the time of his death for his theory of relativism.

Linguistic structures are pairings of meaning and form. Any particular pairing of meaning and form is a Saussurean sign. For instance, the meaning "cat" is represented worldwide with a wide variety of different sound patterns in oral languages , movements of the hands and face in sign languages , and written symbols in written languages. Linguistic patterns have proven their importance for the knowledge engineering field especially with the ever-increasing amount of available data.

Linguists focusing on structure attempt to understand the rules regarding language use that native speakers know not always consciously. All linguistic structures can be broken down into component parts that are combined according to sub conscious rules, over multiple levels of analysis. For instance, consider the structure of the word "tenth" on two different levels of analysis. On the level of internal word structure known as morphology , the word "tenth" is made up of one linguistic form indicating a number and another form indicating ordinality.

The rule governing the combination of these forms ensures that the ordinality marker "th" follows the number "ten. Although most speakers of English are consciously aware of the rules governing internal structure of the word pieces of "tenth", they are less often aware of the rule governing its sound structure.

Linguists focused on structure find and analyze rules such as these, which govern how native speakers use language. Linguistics has many sub-fields concerned with particular aspects of linguistic structure. The theory that elucidates on these, as propounded by Noam Chomsky, is known as generative theory or universal grammar. These sub-fields range from those focused primarily on form to those focused primarily on meaning.

PHILOSOPHY - Language: Meaning and Language [HD]

They also run the gamut of level of analysis of language, from individual sounds, to words, to phrases, up to cultural discourse. Stylistics is the study and interpretation of texts for aspects of their linguistic and tonal style. Stylistic analysis entails the analysis of description of particular dialects and registers used by speech communities.

Stylistic features include rhetoric , [38] diction, stress, satire , irony , dialogue, and other forms of phonetic variations. Stylistic analysis can also include the study of language in canonical works of literature, popular fiction, news, advertisements, and other forms of communication in popular culture as well. It is usually seen as a variation in communication that changes from speaker to speaker and community to community. In short, Stylistics is the interpretation of text. One major debate in linguistics concerns the very nature of language and how it should be understood.

Some linguists hypothesize that there is a module in the human brain that allows people to undertake linguistic behaviour, which is part of the formalist approach. This " universal grammar " is considered to guide children when they learn language and to constrain what sentences are considered grammatical in any human language. Proponents of this view, which is predominant in those schools of linguistics that are based on the generative theory of Noam Chomsky , do not necessarily consider that language evolved for communication in particular.

They consider instead that it has more to do with the process of structuring human thought see also formal grammar. Another group of linguists, by contrast, use the term "language" to refer to a communication system that developed to support cooperative activity and extend cooperative networks. Such theories of grammar , called "functional", view language as a tool that emerged and is adapted to the communicative needs of its users, and the role of cultural evolutionary processes are often emphasized over that of biological evolution.

Linguistics is primarily descriptive. This is analogous to practice in other sciences: a zoologist studies the animal kingdom without making subjective judgments on whether a particular species is "better" or "worse" than another. Prescription , on the other hand, is an attempt to promote particular linguistic usages over others, often favouring a particular dialect or " acrolect ". This may have the aim of establishing a linguistic standard , which can aid communication over large geographical areas. It may also, however, be an attempt by speakers of one language or dialect to exert influence over speakers of other languages or dialects see Linguistic imperialism.

An extreme version of prescriptivism can be found among censors , who attempt to eradicate words and structures that they consider to be destructive to society. Prescription, however, may be practised appropriately in language instruction , like in ELT , where certain fundamental grammatical rules and lexical items need to be introduced to a second-language speaker who is attempting to acquire the language. The objective of describing languages is often to uncover cultural knowledge about communities.

The use of anthropological methods of investigation on linguistic sources leads to the discovery of certain cultural traits among a speech community through its linguistic features. It is also widely used as a tool in language documentation , with an endeavour to curate endangered languages. However, now, linguistic inquiry uses the anthropological method to understand cognitive, historical, sociolinguistic and historical processes that languages undergo as they change and evolve, as well as general anthropological inquiry uses the linguistic method to excavate into culture.

In all aspects, anthropological inquiry usually uncovers the different variations and relativities that underlie the usage of language. Most contemporary linguists work under the assumption that spoken data and signed data are more fundamental than written data. This is because. Nonetheless, linguists agree that the study of written language can be worthwhile and valuable.

For research that relies on corpus linguistics and computational linguistics , written language is often much more convenient for processing large amounts of linguistic data. Large corpora of spoken language are difficult to create and hard to find, and are typically transcribed and written. In addition, linguists have turned to text-based discourse occurring in various formats of computer-mediated communication as a viable site for linguistic inquiry.

The study of writing systems themselves, graphemics , is, in any case, considered a branch of linguistics. Before the 20th century, linguists analysed language on a diachronic plane, which was historical in focus. This meant that they would compare linguistic features and try to analyse language from the point of view of how it had changed between then and later. However, with Saussurean linguistics in the 20th century, the focus shifted to a more synchronic approach, where the study was more geared towards analysis and comparison between different language variations, which existed at the same given point of time.

At another level, the syntagmatic plane of linguistic analysis entails the comparison between the way words are sequenced, within the syntax of a sentence. For example, the article "the" is followed by a noun, because of the syntagmatic relation between the words. The paradigmatic plane on the other hand, focuses on an analysis that is based on the paradigms or concepts that are embedded in a given text. In this case, words of the same type or class may be replaced in the text with each other to achieve the same conceptual understanding. Western interest in the study of languages began somewhat later than in the East, [40] but the grammarians of the classical languages did not use the same methods or reach the same conclusions as their contemporaries in the Indic world.

Early interest in language in the West was a part of philosophy, not of grammatical description. The first insights into semantic theory were made by Plato in his Cratylus dialogue , where he argues that words denote concepts that are eternal and exist in the world of ideas.

This work is the first to use the word etymology to describe the history of a word's meaning. Around BC, one of Alexander the Great 's successors founded a university see Musaeum in Alexandria , where a school of philologists studied the ancient texts in and taught Greek to speakers of other languages. In the 18th century, the first use of the comparative method by William Jones sparked the rise of comparative linguistics.

The study of language was broadened from Indo-European to language in general by Wilhelm von Humboldt , of whom Bloomfield asserts: [44]. Early in the 20th century, Saussure introduced the idea of language as a static system of interconnected units, defined through the oppositions between them. By introducing a distinction between diachronic and synchronic analyses of language, he laid the foundation of the modern discipline of linguistics.

Saussure also introduced several basic dimensions of linguistic analysis that are still foundational in many contemporary linguistic theories, such as the distinctions between syntagm and paradigm , and the langue-parole distinction , distinguishing language as an abstract system langue from language as a concrete manifestation of this system parole. During the last half of the 20th century, following the work of Noam Chomsky , linguistics was dominated by the generativist school. While formulated by Chomsky in part as a way to explain how human beings acquire language and the biological constraints on this acquisition, in practice it has largely been concerned with giving formal accounts of specific phenomena in natural languages.

Generative theory is modularist and formalist in character. Chomsky built on earlier work of Zellig Harris to formulate the generative theory of language. According to this theory the most basic form of language is a set of syntactic rules universal for all humans and underlying the grammars of all human languages. This set of rules is called Universal Grammar , and for Chomsky describing it is the primary objective of the discipline of linguistics.

For this reason the grammars of individual languages are of importance to linguistics only in so far as they allow us to discern the universal underlying rules from which the observable linguistic variability is generated. In the classic formalization of generative grammars first proposed by Noam Chomsky in the s, [48] [49] a grammar G consists of the following components:. A formal description of language attempts to replicate a speaker's knowledge of the rules of their language, and the aim is to produce a set of rules that is minimally sufficient to successfully model valid linguistic forms.

Functional theories of language propose that since language is fundamentally a tool, it is reasonable to assume that its structures are best analysed and understood with reference to the functions they carry out. Functional theories of grammar differ from formal theories of grammar , in that the latter seek to define the different elements of language and describe the way they relate to each other as systems of formal rules or operations, whereas the former defines the functions performed by language and then relates these functions to the linguistic elements that carry them out.

This means that functional theories of grammar tend to pay attention to the way language is actually used, and not just to the formal relations between linguistic elements. Functional theories describe language in term of the functions existing at all levels of language. Cognitive linguistics emerged as a reaction to generativist theory in the s and s. Led by theorists like Ronald Langacker and George Lakoff , cognitive linguists propose that language is an emergent property of basic, general-purpose cognitive processes.

In contrast to the generativist school of linguistics, cognitive linguistics is non-modularist and functionalist in character. Important developments in cognitive linguistics include cognitive grammar , frame semantics , and conceptual metaphor , all of which are based on the idea that form—function correspondences based on representations derived from embodied experience constitute the basic units of language.

Cognitive linguistics interprets language in terms of concepts sometimes universal, sometimes specific to a particular tongue that underlie its form. It is thus closely associated with semantics but is distinct from psycholinguistics , which draws upon empirical findings from cognitive psychology in order to explain the mental processes that underlie the acquisition, storage, production and understanding of speech and writing. Unlike generative theory, cognitive linguistics denies that there is an autonomous linguistic faculty in the mind; it understands grammar in terms of conceptualization ; and claims that knowledge of language arises out of language use.

Historical linguists study the history of specific languages as well as general characteristics of language change. The study of language change is also referred to as "diachronic linguistics" the study of how one particular language has changed over time , which can be distinguished from "synchronic linguistics" the comparative study of more than one language at a given moment in time without regard to previous stages. Historical linguistics was among the first sub-disciplines to emerge in linguistics, and was the most widely practised form of linguistics in the late 19th century. However, there was a shift to the synchronic approach in the early twentieth century with Saussure , and became more predominant in western linguistics with the work of Noam Chomsky.

Ecolinguistics explores the role of language in the life-sustaining interactions of humans, other species and the physical environment. The first aim is to develop linguistic theories which see humans not only as part of society, but also as part of the larger ecosystems that life depends on. The second aim is to show how linguistics can be used to address key ecological issues, from climate change and biodiversity loss to environmental justice.

Sociolinguistics is the study of how language is shaped by social factors. This sub-discipline focuses on the synchronic approach of linguistics, and looks at how a language in general, or a set of languages, display variation and varieties at a given point in time. The study of language variation and the different varieties of language through dialects, registers, and ideolects can be tackled through a study of style, as well as through analysis of discourse.

Sociolinguists research on both style and discourse in language, and also study the theoretical factors that are at play between language and society. Developmental linguistics is the study of the development of linguistic ability in individuals, particularly the acquisition of language in childhood. Some of the questions that developmental linguistics looks into is how children acquire different languages, how adults can acquire a second language, and what the process of language acquisition is.

Neurolinguistics is the study of the structures in the human brain that underlie grammar and communication. Researchers are drawn to the field from a variety of backgrounds, bringing along a variety of experimental techniques as well as widely varying theoretical perspectives. Much work in neurolinguistics is informed by models in psycholinguistics and theoretical linguistics , and is focused on investigating how the brain can implement the processes that theoretical and psycholinguistics propose are necessary in producing and comprehending language.

Prior to the s, computational modeling of NLP and cognition more broadly were pursued almost exclusively within a representationalist paradigm, i. In the s, connectionist or neural models enjoyed a resurgence, and came to be seen by many as rivalling representationalist approaches. We briefly summarize these developments under two subheadings below. Some of the cognitively motivated researchers working within a representationalist paradigm have been particularly concerned with cognitive architecture , including the associative linkages between concepts, distinctions between types of memories and types of representations e.

Others have been more concerned with uncovering the actual internal conceptual vocabulary and inference rules that seem to underlie language and thought. Ross Quillian's semantic memory model, and models developed by Rumelhart, Norman and Lindsay Rumelhart et al. A common thread in cognitively motivated theorizing about semantic representation has been the use of graphical semantic memory models, intended to capture direct relations as well as more indirect associations between concepts, as illustrated in Figure This particular example is loosely based on Quillian Quillian suggested that one of the functions of semantic memory, conceived in this graphical way, was to enable word sense disambiguation through spreading activation.

In particular, the activation signals propagating from sense 1 the living-plant sense of plant would reach the concept for the stuff, water , in four steps along the pathways corresponding to the information that plants may get food from water , and the same concept would be reached in two steps from the term water , used as a verb, whose semantic representation would express the idea of supplying water to some target object. Such conceptual representations have tended to differ from logical ones in several respects. One, as already discussed, has been the emphasis by Schank and various other researchers e.

However, this involves a questionable assumption that subtle distinctions between, say, walking to the park, ambling to the park, or traipsing to the park are simply ignored in the interpretive process, and as noted earlier it neglects the possibility that seemingly insignificant semantic details are pruned from memory after a short time, while major entailments are retained for a longer time.

Another common strain in much of the theorizing about conceptual representation has been a certain diffidence concerning logical representations and denotational semantics.

Noam Chomsky

The relevant semantics of language is said to be the transduction from linguistic utterances to internal representations, and the relevant semantics of the internal representations is said to be the way they are deployed in understanding and thought. For both the external language and the internal mentalese representation, it is said to be irrelevant whether or not the semantic framework provides formal truth conditions for them.

The rejection of logical semantics has sometimes been summarized in the dictum that one cannot compute with possible worlds. However, it seems that any perceived conflict between conceptual semantics and logical semantics can be resolved by noting that these two brands of semantics are quite different enterprises with quite different purposes. Certainly it is entirely appropriate for conceptual semantics to focus on the mapping from language to symbolic structures in the head, realized ultimately in terms of neural assemblies or circuits of some sort , and on the functioning of these structures in understanding and thought.

But logical semantics, as well, has a legitimate role to play, both in considering how words and larger linguistic expressions relate to the world and how the symbols and expressions of the internal semantic representation relate to the world. This role is metatheoretic in that the goal is not to posit cognitive entities that can be computationally manipulated, but rather to provide a framework for theorizing about the relationship between the symbols people use, externally in language and internally in their thinking, and the world in which they live.

It is surely undeniable that utterances are at least sometimes intended to be understood as claims about things, properties, and relationships in the world, and as such are at least sometimes true or false. It would be hard to understand how language and thought could have evolved as useful means for coping with the world, if they were incapable of capturing truths about it. Moreover, logical semantics shows how certain syntactic manipulations lead from truths to truths regardless of the specific meanings of the symbols involved in these manipulations and these notions can be extended to uncertain inference, though this remains only very partially understood.

Thus, logical semantics provides a basis for assessing the soundness or otherwise of inference rules. While human reasoning as well as reasoning in practical AI systems often needs to resort to unsound methods abduction, default reasoning, Bayesian inference, analogy, etc. A strong indication that cognitively motivated conceptual representations of language are reconcilable with logically motivated ones is the fact that all proposed conceptual representations have either borrowed deliberately from logic in the first place in their use of predication, connectives, set-theoretic notions, and sometimes quantifiers or can be transformed to logical representations without much difficulty, despite being cognitively motivated.

As noted earlier, the s saw the re-emergence of connectionist computational models within mainstream cognitive science theory e. We have already briefly characterized connectionist models in our discussion of connectionist parsing. But the connectionist paradigm was viewed as applicable not only to specialized functions, but to a broad range of cognitive tasks including recognizing objects in an image, recognizing speech, understanding language, making inferences, and guiding physical behavior.

The emphasis was on learning, realized by adjusting the weights of the unit-to-unit connections in a layered neural network, typically by a back-propagation process that distributes credit or blame for a successful or unsuccessful output to the units involved in producing the output Rumelhart and McClelland From one perspective, the renewal of interest in connectionism and neural modeling was a natural step in the endeavor to elaborate abstract notions of cognitive content and functioning to the point where they can make testable contact with brain theory and neuroscience.

But it can also be seen as a paradigm shift, to the extent that the focus on subsymbolic processing began to be linked to a growing skepticism concerning higher-level symbolic processing as models of mind, of the sort associated with earlier semantic network-based and rule-based architectures. For example, Ramsay et al. But others have continued to defend the essential role of symbolic processing. For example, Anderson , contended that while theories of symbolic thought need to be grounded in neurally plausible processing, and while subsymbolic processes are well-suited for exploiting the statistical structure of the environment, nevertheless understanding the interaction of these subsymbolic processes required a theory of representation and behavior at the symbolic level.

What would it mean for the semantic content of an utterance to be represented in a neural network, enabling, for example, inferential question-answering? The input modifies the activity of the network and the strengths of various connections in a distributed way, such that the subsequent behavior of the network effectively implements inferential question-answering. However, this leaves entirely open how a network would learn this sort of behavior. The most successful neural net experiments have been aimed at mapping input patterns to class labels or to other very restricted sets of outputs, and they have required numerous labeled examples e.

A less radical alternative to the eliminativist position, termed the subsymbolic hypothesis , was proposed by Smolensky , to the effect that mental processing cannot be fully and accurately described in terms of symbol manipulation, requiring instead a description at the level of subsymbolic features, where these features are represented in a distributed way in the network.

Such a view does not preclude the possibility that assemblies of units in a connectionist system do in fact encode symbols and more complex entities built out of symbols, such as predications and rules. It merely denies that the behavior engendered by these assemblies can be adequately modelled as symbol manipulation. In fact, much of the neural net research over the past two or three decades has sought to understand how neural nets can encode symbolic information e.

Distributed schemes associate a set of units and their activation states with particular symbols or values. For example, Feldman proposes that concepts are represented by the activity of a cluster of neurons; triples of such clusters representing a concept, a role, and a filler value are linked together by triangle nodes to represent simple attributes of objects. Language understanding is treated as a kind of simulation that maps language onto a more concrete domain of physical action or experience, guided by background knowledge in the form of a temporal Bayesian network.

Global schemes encode symbols in overlapping fashion over all units. Propositional symbols can then be interpreted in terms of such states, and truth functions in terms of simple max-min operations and sign inversions performed on network states. See Blutner, ; however, Blutner ultimately focuses on a localist scheme in which units represent atomic propositions and connections represent biconditionals. Holographic neural network schemes e.

A distinctive characteristic of such networks is their ability to classify or reconstruct patterns from partial or noisy inputs. The status of the subsymbolic hypothesis remains an issue for debate and further research. Certainly it is unclear how symbolic approaches could match certain characteristics of neural network approaches, such as their ability to cope with novel instances and their graceful degradation in the face of errors or omissions.

Researchers more concerned with practical advances than biologically plausible modeling have also explored the possibility of hybridizing the symbolic and subsymbolic approaches, in order to gain the advantages of both e. A quite formal example of this, drawing on ideas by Dov Gabbay, is d'Avila Garcez Finally, we should comment on the view expressed in some of the cognitive science literature that mental representations of language are primarily imagistic e.

Certainly there is ample evidence for the reality and significance of mental imagery Johnson-Laird ; Kosslyn But as was previously noted, symbolic and imagistic representations may well coexist and interact synergistically. Moreover, cognitive scientists who explore the human language faculty in detail, such as Steven Pinker , or any of the representationalist or connectionist researchers cited above, all seem to reach the conclusion that the content derived from language and the stuff of thought itself is in large part symbolic—except in the case of the eliminativists who deny representations altogether.

It is not hard to see, however, how raw intuition might lead to the meanings-as-images hypothesis. It appears that vivid consciousness is associated mainly with the visual cortex, especially area V1, which is also crucially involved in mental imagery e. Consequently it is entirely possible that vast amounts of non-imagistic encoding and processing of language go unnoticed, while any evoked imagistic artifacts become part of our conscious experience.

Further, the very act of introspecting on what sort of imagery, if any, is evoked by a given sentence may promote construction of imagery and awareness thereof. In its broadest sense, statistical semantics is concerned with semantic properties of words, phrases, sentences, and texts, engendered by their distributional characteristics in large text corpora. For example, terms such as cheerful, exuberant, and depressed may be considered semantically similar to the extent that they tend to occur flanked by the same or in turn similar nearby words. For some purposes, such as information retrieval, identifying labels of documents may be used as occurrence contexts.

Through careful distinctions among various occurrence contexts, it may also be possible to factor similarity into more specific relations such as synonymy, entailment, and antonymy. One basic difference between standard logical semantic relations and relations based on distributional similarity is that the latter are a matter of degree. Further, the underlying abstractions are very different, in that statistical semantics does not relate strings to the world, but only to their contexts of occurrence a notion similar to, but narrower than, Wittgenstein's notion of meaning as use.

However, statistical semantics does admit elegant formalizations.

Various concepts of similarity and other semantic relations can be captured in terms of vector algebra, by viewing the occurrence frequencies of an expression as values of the components of a vector, with the components corresponding to the distinct contexts of occurrence. But how does this bear on meaning representation of natural language sentences and texts? In essence, the representation of sentences in statistical semantics consists of the sentences themselves.

The idea that sentences can be used directly, in conjunction with distributional knowledge, as objects enabling inference is a rather recent and surprising one, though it was foreshadowed by many years of work on question answering based on large text corpora. Recognizing textual entailment requires judgments as to whether one given linguistic string entails a second one, in a sense of entailment that accords with human intuitions about what a person would naturally infer with reliance on knowledge about word meanings, general knowledge such as that any person who works for a branch of a company also works for that company, and occasional well-known specific facts.

Some examples are intermediate; e. Initial results in the annual competitions were poor not far above the random guessing mark , but have steadily improved, particularly with the injection of some reasoning based on ontologies and on some general axioms about the meanings of words, word classes, relations, and phrasal patterns e. It is noteworthy that the conception of sentences as meaning representations echoes Montague's contention that language is logic. But research in textual entailment seems to be moving towards a similar conception, as exemplified in the work of Dagan et al.

One way of construing degrees of entailment in this framework is in terms of the entailment probabilities relating each possible logical form of the premise sentence to each possible logical form of the hypothesis in question. Having surveyed three rather different brands of semantics, we are left with the question of which of these brands serves best in computational linguistic practice. If the goal, for example, is to create a dialogue-based problem-solving system for circuit fault diagnosis, emergency response, medical contingencies, or vacation planning, then an approach based on logical or at least symbolic representations of the dialogue, underlying intentions, and relevant constraints and knowledge is at present the only viable option.

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Here it is of less importance whether the symbolic representations are based on some presumed logical semantics for language, or some theory of mental representation—as long as they are representations that can be reasoned with. The most important limitations that disqualify subsymbolic and statistical representations of meaning for such purposes are their very limited inferential reach and response capabilities.

They provide classifications or one-shot inferences rather than reasoning chains, and they do not generate plans, justifications, or extended linguistic responses. However, both neural net techniques and statistical techniques can help to improve semantic processing in dialogue systems, for example by disambiguating word senses, or recognizing which of several standard plans is being proposed or followed, on the basis of observed utterances or actions.

On the other hand, if the computational goal is to demonstrate human-like performance in a biologically plausible or biologically valid! However, to the extent that language is symbolic, and is a cognitive phenomenon, subsymbolic theories must ultimately explain how language can come about.

In the case of statistical semantics, practical applications such as question-answering based on large textual resources, retrieval of documents relevant to a query, or machine translation are at present greatly superior to logical systems that attempt to fully understand both the query or text they are confronted with and the knowledge they bring to bear on the task. But some of the trends pointed out above in trying to link subsymbolic and statistical representations with symbolic ones indicate that a gradual convergence of the various approaches to semantics is taking place.

For the next few paragraphs, we shall take semantic interpretation to refer to the process of deriving meaning representations from a word stream, taking for granted the operation of a prior or concurrent parsing phase. In other words, we are mapping syntactic trees to logical forms or whatever our meaning representation may be.

In the heyday of the proceduralist paradigm, semantic interpretation was typically accomplished with sets of rules that matched patterns to parts of syntactic trees and added to or otherwise modified the semantic representations of input sentences. The completed representations might either express facts to be remembered, or might themselves be executable commands, such as formal queries to a database or high-level instructions placing one block on another in a robot's simulated or real world.

When it became clear in the early s, however, that syntactic trees could be mapped to semantic representations by using compositional semantic rules associated with phrase structure rules in one-to-one fashion, this approach became broadly favored over pure proceduralist ones. In our earlier discussion in section 3. There we saw sample interpretive rules for a small number of phrase structure rules and vocabulary.

The interpretive rules are repeated at the tree nodes from section 3. As can be seen, the Montagovian treatment of NPs as second-order predicates leads to some complications, and these are exacerbated when we try to take account of quantifier scope ambiguity. We mentioned Montague's use of multiple parses, the Cooper-storage approach, and the unscoped-quantifier approach to this issue in section 3.

It is easy to see that multiple unscoped quantifiers will give rise to multiple permutations of quantifier order when the quantifiers are brought to the sentence level. At this point we should pause to consider some interpretive methods that do not conform with the above very common but not universally employed syntax-driven approach.

First, Schank and his collaborators emphasized the role of lexical knowledge, especially primitive actions used in verb decomposition, and knowledge about stereotyped patterns of behavior in the interpretive process, nearly to the exclusion of syntax. These ideas had considerable appeal, and led to unprecedented successes in machine understanding of some paragraph-length stories. Another approach to interpretation that subordinates syntax to semantics is one that employs domain-specific semantic grammars Brown and Burton While these resemble context-free syntactic grammars perhaps procedurally implemented in ATN-like manner , their constituents are chosen to be meaningful in the chosen application domain.

For example, an electronics tutoring system might employ categories such as measurement, hypothesis , or transistor instead of NP, and fault-specification or voltage-specification instead of VP. The importance of these approaches lay in their recognition of the fact that knowledge powerfully shapes our ultimate interpretation of text and dialogue, enabling understanding even in the presence of noisy, flawed, and partial linguistic input. Statistical NLP has only recently begun to be concerned with deriving interpretations usable for inference and question answering and as pointed out in the previous subsection, some of the literature in this area assumes that the NL text itself can and should be used as the basis for inference.

We will mention examples of this type of work, and comment on its prospects, in section 8. We noted earlier that language is potentially ambiguous at all levels of syntactic structure, and the same is true of semantic content, even for syntactically unambiguous words, phrases and sentences. For example, words like bank , recover , and cool have multiple meanings even as members of the same lexical category; nominal compounds such as ice bucket, ice sculpture, olive oil, or baby oil leave unspecified the underlying relation between the nominals such as constituency or purpose.

Many techniques have been proposed for dealing with the various sorts of semantic ambiguities, ranging from psychologically motivated principles, to knowledge-based methods, heuristics, and statistical approaches. Psychologically motivated principles are exemplified by Quillian's spreading activation model described earlier and the use of selectional preferences in word sense disambiguation.

Examples of knowledge-based disambiguation would be the disambiguation of ice sculpture to a constitutive relation based on the knowledge that sculptures may be carved or constructed from solid materials, or the disambiguation of a man with a hat to a wearing -relation based on the knowledge that a hat is normally worn on the head. The possible meanings may first be narrowed down using heuristics concerning the limited types of relations typically indicated by nominal compounding or by with -modification.

Heuristic principles used in scope disambiguation include island constraints quantifiers such as every and most cannot expand their scope beyond their local clause and differing wide-scoping tendencies for different quantifiers e. Statistical approaches typically extract various features in the vicinity of an ambiguous word or phrase that are thought to influence the choice to be made, and then make that choice with a classifier that has been trained on an annotated text corpus. The features used might be particular nearby words or their parts of speech or semantic categories, syntactic dependency relations, morphological features, etc..

Such techniques have the advantage of learnability and robustness, but ultimately will require supplementation with knowledge-based techniques. For example, the correct scoping of quantifiers in contrasting sentence pairs such as. For example,. Thus in general appears to be the implicit default adverbial. But when the quantifying adverb is present, the sentence admits both an atemporal reading according to which many purebred racehorses are characteristically skittish, as well as a temporal reading to the effect that purebred racehorses in general are subject to frequent episodes of skittishness.

If we replace purebred by at the starting gate , then only the episodic reading of skittish remains available, while often may quantify over racehorses, implying that many are habitually skittish at the starting gate, or it may quantify over starting-gate situations, implying that racehorses in general are often skittish in such situations; furthermore, making formal sense of the phrase at the starting gate evidently depends on knowledge about horse racing scenarios.

The interpretive challenges presented by such sentences are or should be of great concern in computational linguistics, since much of people's general knowledge about the world is most naturally expressed in the form of generic and habitual sentences. Systematic ways of interpreting and disambiguating such sentences would immediately provide a way of funneling large amounts of knowledge into formal knowledge bases from sources such as lexicons, encyclopedias, and crowd-sourced collections of generic claims such as those in Open Mind Common Sense e.

Many theorists assume that the logical forms of such sentences should be tripartite structures with a quantifier that quantifies over objects or situations, a restrictor that limits the quantificational domain, and a nuclear scope main clause that makes an assertion about the elements of the domain e. The challenge lies in specifying a mapping from surface structure to such a logical form. While many of the principles underlying the ambiguities illustrated above are reasonably well understood, general interpretive algorithms are still lacking.

The dividing line between semantic interpretation computing and disambiguating logical forms and discourse understanding—making sense of text—is a rather arbitrary one. Language has evolved to convey information as efficiently as possible, and as a result avoids lengthy identifying descriptions and other lengthy phrasings where shorter ones will do.

The reverse sequencing, cataphora , is seen occasionally as well. Determining the co referents of anaphors can be approached in a variety of ways, as in the case of semantic disambiguation.

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Linguistic and psycholinguistic principles that have been proposed include gender and number agreement of coreferential terms, C-command principles e. An early heuristic algorithm that employed several features of this type to interpret anaphors was that of Hobbs But selectional preferences are important as well. Another complication concerns reference to collections of entities, related entities such as parts , propositions, and events that can become referents of pronouns such as they, this, and that or of definite NPs such as this situation or the door of the house without having appeared explicitly as a noun phrase.

Like other sorts of ambiguity, coreference ambiguity has been tackled with statistical techniques. These typically take into account factors like those mentioned, along with additional features such as antecedent animacy and prior frequency of occurrence, and use these as probabilistic evidence in making a choice of antecedent e. Parameters of the model are learned from a corpus annotated with coreference relations and the requisite syntactic analyses. Coming back briefly to nominal compounds of form N N, note that unlike conventional compounds such as ice bucket or ice sculpture —ones approachable using an enriched lexicon, heuristic rules, or statistical techniques—some compounds can acquire a variety of meanings as a function of context.

For example, rabbit guy could refer to entirely different things in a story about a fellow wearing a rabbit suit, or one about a rabbit breeder, or one about large intelligent leporids from outer space. Such examples reveal certain parallels between compound nominal interpretation and anaphora resolution: At least in the more difficult cases, N N interpretation depends on previously seen material, and on having understood crucial aspects of that previous material in the current example, the concepts of wearing a rabbit suit, being a breeder of rabbits, or being a rabbit-like creature.

In other words N N interpretation, like anaphora resolution, is ultimately knowledge-dependent, whether that knowledge comes from prior text, or from a preexisting store of background knowledge. A strong version of this view is seen in the work of Fan et al.

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For example, in a chemistry context, HCL solution is assumed to require elaboration into something like: solution whose base is a chemical whose basic structural constituents are HCL molecules. Algorithms are provided and tested empirically that search for a relational path subject to certain general constraints from the modified N to the modifying N, selecting such a relational path as the meaning of the N N compound.

As the authors note, this is essentially a spreading-activation algorithm, and they suggest more general application of this method see section 5. One pervasive phenomenon of this type is of course ellipsis , as illustrated earlier in sentences 2.

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Interpreting ellipsis requires filling in of missing material; this can often be found at the surface level as a sequence of consecutive words as in the gapping and bare ellipsis examples 2. Further complications arise when the imported material contains referring expressions, as in the following variant of 5. Here the missing material may refer either to Felix's boss or my boss called the strict and sloppy reading respectively , a distinction that can be captured by regarding the logical form of the antecedent VP as containing only one, or two, occurrences of the lambda-abstracted subject variable, i.

The two readings can be thought of as resulting respectively from scoping his boss first, then filling in the elided material, and the reverse ordering of these operations Dalrymple et al. Other challenging forms of ellipsis are event ellipsis, as in 5. In applications these and some other forms of ellipsis are handled, where possible, by a making strong use of domain-dependent expectations about the types of information and speech acts that are likely to occur in the discourse, such as requests for flight information in an air travel adviser; and b interpreting utterances as providing augmentations or modifications of domain-specific knowledge representations built up so far.

Corpus-based approaches to ellipsis have so far focused mainly on identifying instances of VP ellipsis in text, and finding the corresponding antecedent material, as problems separate from that of computing correct logical forms e. Another refractory missing-material phenomenon is that of implicit arguments. For example, in the sentence. However, not all of the fillers for those slots are made explicitly available by the text—the carbon monoxide referred to provides one of the fillers, but the air in the interior of the car, and potential occupants of the car and that they rather than, say, the upholstery would be at risk are a matter of inference from world knowledge.

Finally, another form of shorthand that is common in certain contexts is metonymy , where a term saliently related to an intended referent stands for that referent. For example, in an airport context,. Like other types of underspecification, metonymy has been approached both from knowledge-based and corpus-based perspectives.

Knowledge that can be brought to bear includes selectional preferences e. Lakoff and Johnson , rules for when to conjecture such relations e. Corpus-based methods e. As for other facets of the interpretive process including parsing , use of deep domain knowledge for metonym processing can be quite effective in sufficiently narrow domains, while corpus-based, shallow methods scale better to broader domains, but are apt to reach a performance plateau falling well short of human standards. Text and spoken language do not consist of isolated sentences, but of connected, interrelated utterances, forming a coherent whole—typically, a temporally and causally structured narrative, a systematic description or explanation, a sequence of instructions, or a structured argument for a conclusion or in dialogue, as discussed later, question-answer exchanges, requests followed by acknowledgments, mixed-initiative planning, etc.

At a deeper level, we also understand that John perceived the sky to be dark with thunderclouds, and naturally assume that John took the clouds to be harbingers of an impending storm, as we ourselves would. The examples show that interpreting extended multi-clausal discourses depends on both narrative conventions and world knowledge; similarly for descriptive, instructional or argumentative text. In particular, an action sentence followed by a static observation, as in 5.

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These suggestive inferences presumably reflect the narrator's adherence to a Gricean principle of orderliness, though such an observation is little help from a computational perspective. The concrete task is to formulate coherence principles for narrative and other forms of discourses and to elucidate, in a usable form, the particular syntactico-semantic properties at various levels of granularity that contribute to coherence. Thus various types of rhetorical or coherence relations between clauses or larger discourse segments have been proposed in the literature, e.

Proposed coherence relations are ones like elaboration, exemplification, parallelism, and contrast. We defer further discussion of rhetorical structure to section 6 on language generation. I'm on point, on task, on message and off drugs… I'm in the moment, on the edge, over the top and under the radar. A high-concept, low-profile, medium-range ballistic missionary. We have already commented on processing metonymy, which is conventionally counted as a figure of speech—a word or phrase standing for something other than its literal meaning.

However, while metonymy is essentially an abridging device, other figurative modes, such as metaphor, simile, idioms, irony, personification, or hyperbole overstatement convey meanings, especially connotative ones, not easily conveyed in other ways. We focus on metaphor here, as it is in a sense a more general form of several other tropes. Moreover, it has received the most attention from computational linguists, because the argument can be made that metaphor pervades language, with no sharp demarcation between literal and metaphorical usage e.

As a way of allowing for examples of this type, Wilks offered a processing paradigm in which selectional constraints such as a physical-object constraint on the subject of drop are treated as mere preferences rather than firm requirements.

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  • However, processing metaphor requires more than relaxation of preferences; it is both context-dependent and profoundly knowledge-dependent. But to grasp the metaphorical meaning fully, including the connotation of a punishing, doomed struggle, requires a vivid conception of what a boxing match is like. In approaching metaphor computationally, some authors, e. For example, in comparing an atom to the solar system, we observe a revolves-around relation between electrons and the nucleus on the one hand and between planets and the sun on the other.

    But others have pointed out that the implicit comparison may hinge on properties reached only indirectly.