COMMON FEATURES OF MODERN MODELS OF LANGUAGE - раздел Образование, THE ROLE OF NATURAL LANGUAGE PROCESSING The Modern Models Of Language Have Turned Out To Possess Several Common Featu...
The modern models of language have turned out to possess several common features that are very important for the comprehension and use of these models. One of these models is given by the Meaning Û Text Theory already mentioned. Another model is that based on the Head-Driven Phrase Structure Grammar. The Chomskian approach within the Western linguistic tradition includes various other models different from HPSG.
Here are the main common features of all these models:
· Functionality of the model. The linguistic models try to reproduce functions of language without directly reproducing the features of activity of the brain, which is the motor of human language.
· Opposition of the textual/phonetic form of language to its semantic representation. The manual [9], depicting three different well-known syntactic theories (including one of the recent variant of the theory by N. Chomsky), notices: “Language ultimately expresses a relation between sound at one end of the linguistic spectrum and meaning at the other.” Just as the diffuse notion spectrum is somehow sharpened, we have the same definition of language as in the MTT. The outer, observable form of language activity is a text, i.e., strings of phonetic symbols or letters, whereas the inner, hidden form of the same information is the meaning of this text. Language relates two these forms of the same information.
· Generalizing character of language. Separate utterances, within a speech or a text, are considered not as the language, but as samples of its functioning. The language is a theoretical generalization of the open and hence infinite set of utterances. The generalization brings in features, types, structures, levels, rules, etc., which are not directly observable. Rather these theoretical constructs are fruits of linguist's intuition and are to be repeatedly tested on new utterances and the intuition of other linguists. The generalization feature is connected with the opposition competence vs. performance in Chomskian theory and to the much earlier opposition language vs. speech in the theory of Ferdinand de Saussure.
· Dynamic character of the model. A functional model does not only propose a set of linguistic notions, but also shows (by means of rules) how these notions are used in the processing of utterances.
· Formal character of the model. A functional model is a system of rules sufficiently rigorous to be applied to any text by a person or an automaton quite formally, without intervention of the model’s author or anybody else. The application of the rules to the given text or the given meaning always produces the same result. Any part of a functional model can in principle be expressed in a strict mathematical form and thus algorithmized.[19] If no ready mathematical tool is available at present, a new tool should be created. The presupposed properties of recognizability and algorithmizability of natural language are very important for the linguistic models aimed at computer implementation.
· Non-generative character of the model. Information does not arise or generated within the model; it merely acquires a form corresponding to other linguistic level. We may thus call the correspondences between levels equative correspondences. On the contrary, in the original generative grammars by Chomsky, the strings of symbols that can be interpreted as utterances are generated from an initial symbol, which has just abstract sense of a sentence. As to transformations by Chomsky in their initial form, they may change the meaning of an utterance, and thus they were not equative correspondences.
· Independence of the model from direction of transformation. The description of a language is independent of the direction of linguistic processing. If the processing submits to some rules, these rules should be given in equative (i.e., preserving the meaning) bi-directional form, or else they should permit reversion in principle.
· Independence of algorithms from data. A description of language structures should be considered apart from algorithms using this description. Knowledge about language does not imply a specific type of algorithms. On the contrary, in many situations an algorithm implementing some rules can have numerous options. For example, the MTT describes the text level separately from the morphologic and syntactic levels of the representation of the same utterance. Nevertheless, one can imagine an algorithm of analysis that begins to construct the corresponding part of the syntactic representation just as the morphologic representation of the first word in the utterance is formed. In the cases when linguistic knowledge is presented in declarative form with the highest possible consistency, implementing algorithms proved to be rather universal, i.e., equally applicable to several languages. (Such linguistic universality has something in common with the Universal Grammar that N. Chomsky has claimed to create.) The analogous distinction between algorithms and data is used with great success in modern compilers of programming languages (cf. compiler-compilers).
· Emphasis on detailed dictionaries. The main part of the description of any language implies words of the language. Hence, dictionaries containing descriptions of separate words are considered the principal part of a rigorous language description. Only very general properties of vast classes and subclasses of lexemes are abstracted from dictionaries, in order to constitute formal grammars.
Все темы данного раздела:
THE ROLE OF NATURAL LANGUAGE PROCESSING
We live in the age of information. It pours upon us from the pages of newspapers and magazines, radio loudspeakers, TV and computer screens. The main part of this information has the form of natura
LINGUISTICS AND ITS STRUCTURE
Linguistics is a science about natural languages. To be more precise, it covers a whole set of different related sciences (see Figure I.1).
General linguistics is a nucleus [18, 36]
WHAT WE MEAN BY COMPUTATIONAL LINGUISTICS
Computational linguistics might be considered as a synonym of automatic processing of natural language, since the main task of computational linguistics is just the construction of computer
WORD, WHAT IS IT?
As it could be noticed, the term word was used in the previous sections very loosely. Its meaning seems obvious: any language operates with words and any text or utterance consists of them.
THE IMPORTANT ROLE OF THE FUNDAMENTAL SCIENCE
In the past few decades, many attempts to build language processing or language understanding systems have been undertaken by people without sufficient knowledge in theoretical linguistics. They ho
CURRENT STATE OF APPLIED RESEARCH ON SPANISH
In our books, the stress on Spanish language is made intentionally and purposefully. For historical reasons, the majority of the literature on natural languages processing is not only written in En
CONCLUSIONS
The twenty-first century will be the century of the total information revolution. The development of the tools for the automatic processing of the natural language spoken in a country or a whole gr
II. A HISTORICAL OUTLINE
A COURSE ON LINGUISTICS usually follows one of the general models, or theories, of natural language, as well as the corresponding methods of interpretation of the linguistic phenomena.
A c
THE STRUCTURALIST APPROACH
At the beginning of the twentieth century, Ferdinand de Saussure had developed a new theory of language. He considered natural language as a structure of mutually linked elements, similar or
INITIAL CONTRIBUTION OF CHOMSKY
In the 1950’s, when the computer era began, the eminent American linguist Noam Chomsky developed some new formal tools aimed at a better description of facts in various languages [12].
A SIMPLE CONTEXT-FREE GRAMMAR
Let us consider an example of a context-free grammar for generating very simple English sentences. It uses the initial symbol S of a sentence to be generated and several oth
TRANSFORMATIONAL GRAMMARS
Further research revealed great generality, mathematical elegance, and wide applicability of generative grammars. They became used not only for description of natural languages, but also for specif
THE LINGUISTIC RESEARCH AFTER CHOMSKY: VALENCIES AND INTERPRETATION
After the introduction of the Chomskian transformations, many conceptions of language well known in general linguistics still stayed unclear. In the 1980’s, several grammatical theories different f
LINGUISTIC RESEARCH AFTER CHOMSKY: CONSTRAINTS
Another very valuable idea originated within the generative approach was that of using special features assigned to the constituents, and specifying constraints to characterize agreement or
HEAD-DRIVEN PHRASE STRUCTURE GRAMMAR
One of the direct followers of the GPSG was called Head-Driven Phrase Structure Grammar (HPSG). In addition to the advanced traits of the GPSG, it has introduced and intensively used the notion of
THE IDEA OF UNIFICATION
Having in essence the same initial idea of phrase structures and their context-free combining, the HPSG and several other new approaches within Chomskian mainstream select the general and very powe
THE MEANING Û TEXT THEORY: MULTISTAGE TRANSFORMER AND GOVERNMENT PATTERNS
The European linguists went their own way, sometimes pointing out some oversimplifications and inadequacies of the early Chomskian linguistics.
In late 1960´s, a new theory, the Mean
THE MEANING Û TEXT THEORY: DEPENDENCY TREES
Another important feature of the MTT is the use of its dependency trees, for description of syntactic links between words in a sentence. Just the set of these links forms the representation
THE MEANING Û TEXT THEORY: SEMANTIC LINKS
The dependency approach is not exclusively syntactic. The links between wordforms at the surface syntactic level determine links between corresponding labeled nodes at the deep syntactic level, and
CONCLUSIONS
In the twentieth century, syntax was in the center of the linguistic research, and the approach to syntactic issues determined the structure of any linguistic theory. There are two major approaches
III. PRODUCTS OF COMPUTATIONAL LINGUISTICS: PRESENT AND PROSPECTIVE
FOR WHAT PURPOSES do we need to develop computational linguistics? What practical results does it provide for society? Before we start discus-sing the methods and techniques of computational lingui
CLASSIFICATION OF APPLIED LINGUISTIC SYSTEMS
Applied linguistic systems are now widely used in business and scientific domains for many purposes. Some of the most important ones among them are the following:
· Text preparation
AUTOMATIC HYPHENATION
Hyphenation is intended for the proper splitting of words in natural language texts. When a word occurring at the end of a line is too long to fit on that line within the accepted margins, a part o
SPELL CHECKING
The objective of spell checking is the detection and correction of typographic and orthographic errors in the text at the level of word occurrence considered out of its context.
Nob
GRAMMAR CHECKING
Detection and correction of grammatical errors by taking into account adjacent words in the sentence or even the whole sentence are much more difficult tasks for computational linguists and softwar
STYLE CHECKING
The stylistic errors are those violating the laws of use of correct words and word combinations in language, in general or in a given literary genre.
This application is the nearest in its
REFERENCES TO WORDS AND WORD COMBINATIONS
The references from any specific word give access to the set of words semantically related to the former, or to words, which can form combinations with the former in a text. This is a very importan
INFORMATION RETRIEVAL
Information retrieval systems (IRS) are designed to search for relevant information in large documentary databases. This information can be of various kinds, with the queries ranging from “Find all
TOPICAL SUMMARIZATION
In many cases, it is necessary to automatically determine what a given document is about. This information is used to classify the documents by their main topics, to deliver by Internet the documen
AUTOMATIC TRANSLATION
Translation from one natural language to another is a very important task. The amount of business and scientific texts in the world is growing rapidly, and many countries are very productive in sci
NATURAL LANGUAGE INTERFACE
The task performed by a natural language interface to a database is to understand questions entered by a user in natural language and to provide answers—usually in natural language, but sometimes a
EXTRACTION OF FACTUAL DATA FROM TEXTS
Extraction of factual data from texts is the task of automatic generation of elements of a factographic database, such as fields, or parameters, based on on-line texts. Often the flows of the curre
TEXT GENERATION
The generation of texts from pictures and formal specifications is a comparatively new field; it arose about ten years ago. Some useful applications of this task have been found in recent years. Am
SYSTEMS OF LANGUAGE UNDERSTANDING
Natural language understanding systems are the most general and complex systems involving natural language processing. Such systems are universal in the sense that they can perform nearly all the t
RELATED SYSTEMS
There are other types of applications that are not usually considered systems of computational linguistics proper, but rely heavily on linguistic methods to accomplish their tasks. Of these we will
CONCLUSIONS
A short review of applied linguistic systems has shown that only very simple tasks like hyphenation or simple spell checking can be solved on a modest linguistic basis. All the other systems should
POSSIBLE POINTS OF VIEW ON NATURAL LANGUAGE
One could try to define natural language in one of the following ways:
· The principal means for expressing human thoughts;
· The principal means for text generation;
· T
LANGUAGE AS A BI-DIRECTIONAL TRANSFORMER
The main purpose of human communication is transferring some information—let us call it Meaning[6]—from one person to the other. However, the direct transferring of thoughts is not possi
TEXT, WHAT IS IT?
The empirical reality for theoretical linguistics comprises, in the first place, the sounds of speech. Samples of speech, i.e., separate words, utterances, discourses, etc., are given to the resear
MEANING, WHAT IS IT?
Meanings, in contrast to texts, cannot be observed directly. As we mentioned above, we consider the Meaning to be the structures in the human brain which people experience as ideas and thoughts. Si
TWO WAYS TO REPRESENT MEANING
To represent the entities and relationships mentioned in the texts, the following two logically and mathematically equivalent formalisms are used:
· Predicative formulas. Logical
DECOMPOSITION AND ATOMIZATION OF MEANING
Semantic representation in many cases turns out to be universal, i.e., common to different natural languages. Purely grammatical features of different languages are not usually reflected in
NOT-UNIQUENESS OF MEANING Þ TEXT MAPPING: SYNONYMY
Returning to the mapping of Meanings to Texts and vice versa, we should mention that, in contrast to common mathematical functions, this mapping is not unique in both directions, i.e., it is of the
NOT-UNIQUENESS OF TEXT Þ MEANING MAPPING: HOMONYMY
In the opposite direction—Texts to Meanings—a text or its fragment can exhibit two or more different meanings. That is, one element of the surface edge of the mapping (i.e. text) can correspond to
MORE ON HOMONYMY
In the field of computational linguistics, homonymous lexemes usually form separate entries in dictionaries. Linguistic analyzers must resolve the homonymy automatically, by choosing the correct op
MULTISTAGE CHARACTER OF THE MEANING Û TEXT TRANSFORMER
FIGURE IV.10. Levels of linguistic representation.
TRANSLATION AS A MULTISTAGE TRANSFORMATION
FIGURE IV.13. The role of dictionaries and grammars in linguis
TWO SIDES OF A SIGN
The notion of sign, so important for linguistics, was first proposed in a science called semiotics. The sign was defined as an entity consisting of two components, the signifier
LINGUISTIC SIGN
The notion of linguistic sign was introduced by Ferdinand de Saussure. By linguistic signs, we mean the entities used in natural languages, such as morphs, lexemes, and phrases.
Lin
LINGUISTIC SIGN IN THE MMT
In addition to the two well-known components of a sign, in the Meaning Û Text Theory yet another, a third component of a sign, is considered essential: a record about its ability or inability
LINGUISTIC SIGN IN HPSG
In Head-driven Phrase Structure Grammar a linguistic sign, as usually, consists of two main components, a signifier and a signified. The signifier is defined as a phoneme string (or a sequence of s
ARE SIGNIFIERS GIVEN BY NATURE OR BY CONVENTION?
The notion of sign appeared rather recently. However, the notions equivalent to the signifier and the signified were discussed in science from the times of the ancient Greeks. For several centuries
GENERATIVE, MTT, AND CONSTRAINT IDEAS IN COMPARISON
In this book, three major approaches to linguistic description have been discussed till now, with different degree of detail: (1) generative approach developed by N. Chomsky, (2) the Meaning Û
CONCLUSIONS
The definition of language has been suggested as a transformer between the two equivalent representations of information, the Text, i.e., the surface textual representation, and the Meaning, i.e.,
V. LINGUISTIC MODELS
THROUGHOUT THE PREVIOUS CHAPTERS, you have learned, on the one hand, that for many computer applications, detailed linguistic knowledge is necessary and, on the other hand, that natural language ha
WHAT IS MODELING IN GENERAL?
In natural sciences, we usually consider the system A to be a model of the system B if A is similar to B in some important properties and exhibits somewhat simila
NEUROLINGUISTIC MODELS
Neurolinguistic models investigate the links between any external speech activity of human beings and the corresponding electrical and humoral activities of nerves in their brain.
I
PSYCHOLINGUISTIC MODELS
Psycholinguistics is a science investigating the speech activity of humans, including perception and forming of utterances, via psychological methods. After creating its hypotheses and model
FUNCTIONAL MODELS OF LANGUAGE
In terms of cybernetics, natural language is considered as a black box for the researcher. A black box is a device with observable input and output but with a completely unobservable inner s
RESEARCH LINGUISTIC MODELS
There are still other models of interest for linguistics. They are called research models. At input, they take texts in natural language, maybe prepared or formatted in a special manner befo
SPECIFIC FEATURES OF THE MEANING Û TEXT MODEL
The Meaning Û Text Model was selected for the most detailed study in these books, and it is necessary now to give a short synopsis of its specific features.
· Orientation to synth
REDUCED MODELS
We can formulate the problem of selecting a good model for any specific linguistic application as follows.
A holistic model of the language facilitates describing the language as a
DO WE REALLY NEED LINGUISTIC MODELS?
Now let us reason a little bit on whether computer scientists really need a generalizing (complete) model of language.
In modern theoretical linguistics, certain researchers study phonolog
ANALOGY IN NATURAL LANGUAGES
Analogy is the prevalence of a pattern (i.e., one rule or a small set of rules) in the formal description of some linguistic phenomena. In the simplest case, the pattern can be represented with the
EMPIRICAL VERSUS RATIONALIST APPROACHES
In the recent years, the interest to empirical approach in linguistic research has livened. The empirical approach is based on numerous statistical observations gathered purely automatically
LIMITED SCOPE OF THE MODERN LINGUISTIC THEORIES
Even the most advanced linguistic theories cannot pretend to cover all computational problems, at least at present. Indeed, all of them evidently have the following limitations:
· Only the
CONCLUSIONS
A linguistic model is a system of data (features, types, structures, levels, etc.) and rules, which, taken together, can exhibit a “behavior” similar to that of the human brain in understanding and
REVIEW QUESTIONS
THE FOLLOWING QUESTIONS can be used to check whether the reader has understood and remembered the main contents of the book. The questions are also recommended for t
PROBLEMS RECOMMENDED FOR EXAMS
IN THIS SECTION, each test question is supplied with a set of four variants of the answer, of which exactly one is correct and the others are not.
1. Why automatic natural language process
RECOMMENDED LITERATURE
1. Allen, J. Natural Language Understanding. The Benjamin / Cummings Publ., Amsterdam, Bonn, Sidney, Singapore, Tokyo, Madrid, 1995.
2. Cortés García, U., J. Bé
ADDITIONAL LITERATURE
10. Baeza-Yates, R., B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley Longman and ACM Press, 1999.
11. Beristáin, Helena. Gramática estructural de la l
GENERAL GRAMMARS AND DICTIONARIES
20. Criado de Val, M. Gramática española. Madrid, 1958.
21. Cuervo, R. J. Diccionario de construcción y régimen de la lengua castellana. Instituto
REFERENCES
34. Apresian, Yu. D. et al. Linguistic support of the system ETAP-2 (in Russian). Nauka, Moscow, Russia, 1989.
35. Beekman, G. “Una mirada a la tecnología del ma&ntild
SOME SPANISH-ORIENTED GROUPS AND RESOURCES
HERE WE PRESENT a very short list of groups working on Spanish, with their respective URLs, especially the groups in Latin America. The members of the RITOS network (emilia.dc.fi.udc.es / Ritos2) a
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