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PROBLEMS RECOMMENDED FOR EXAMS - раздел Образование, THE ROLE OF NATURAL LANGUAGE PROCESSING In This Section, Each Test Question Is Supplied With A Set Of Four Variants O...

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 processing (NPL) is important for the humankind?

A. Because NPL takes decisions in place of humans.

B. Because NPL facilitates humans to prepare, to read, and to search through many texts.

C. Because NPL permits humans not to read any texts by themselves.

D. Because NPL facilitates humans to use computers.

2. Why theoretical aspects of linguistics are necessary for computational linguistics?

A. Because they help to prepare good user’s manuals for products of computational linguistics.

B. Because they help to evaluate the performance of computational linguistics products.

C. Because they help to gather statistics of various language elements.

D. Because they help to comprehend general structure of languages.

3. How does computational linguistics (CL) coordinate with artificial intelligence (AI)?

A. CL is a part of AI.

B. AI is a part of CL.

C. CL does not coordinate with AI at all.

D. CL and AI have many tools in common.

4. How does computational linguistics (CL) coordinate with computer science (CS)?

 

A. CL is a part of CS.

B. CS is a part of CL.

C. CS is a tool for CL.

D. CL is a tool for CS.

 

5. What does general linguistics study?

A. Laws of orthography.

B. Laws and structures of languages.

C. Rules of good word usage.

D. Rules of good style.

6. What does phonology study?

A. Sounds of music.

B. Sounds uttered by animals.

C. Sounds forming words for their distinguishing.

D. Sounds of alarm.

7. What does morphology study?

A. How to combine words to sentences.

B. How to combine sounds or letters to words.

C. How to form abbreviations.

D. How to form composed words like rascacielos.

8. What does syntax study?

A. How to combine parts of words to words.

B. How to compose a text of paragraphs.

C. How to compose a paragraph of sentences.

D. How to combine words to phrases and sentences.

9. What does semantics study?

A. How humans think.

B. How humans code the meaning in their brains.

C. How humans perceive the outer world.

D. How human express their wishes.

10. What does historical linguistics study?

A. Biographies of eminent linguists.

B. Theories of language origin in the prehistoric times.

C. Evolution of different languages in the course of time.

D. History of development of grammars.

11. What does contrastive linguistics study?

A. Controversies between different linguistic theories.

B. Differences between various languages.

C. Antonyms like pequeño–grande ‘small–big’.

D. Similarities of various non-cognate languages in their structures.

12. What part of linguistics studies peculiarities of Spanish in the Yucatan peninsula?

 

A. Historical linguistics.

B. Dialectology.

C. Sociolinguistics.

D. Comparative linguistics.

 

13. What does lexicography study?

A. Rules of orthography.

B. Rules of good word usage.

C. Pronunciation of words.

D. Description of all words in languages.

14. What does computational linguistics study?

A. How to count words and other linguistic elements in texts.

B. How to create programs for automatic text processing.

C. How to teach a foreign language with the aid of a computer.

D. How to prepare correct text with the aid of a computer.

15. How is computational linguistics (CL) related with applied linguistics (AL)?

A. CL is a part of AL.

B. AL is a part of CL.

C. AL is equal to CL.

D. AL and CL are independent branches of linguistics.

16. What are constituents in linguistic description?

A. Arbitrary parts of a sentence.

B. Contiguous groups of words in a sentence.

C. Words with the most important meaning in a sentence.

D. All words in a sentence except for auxiliary ones.

17. What does a constituency tree contain as its nodes?

A. Various words.

B. Various grammatical categories.

C. Various sentences.

D. Various word endings.

18. What mathematical means did Chomsky propose?

A. Hierarchy of generative grammars.

B. Algorithms of language analysis.

C. Normative grammars for several languages.

D. Modified version of English grammar.

19. What can transformation grammars describe?

A. How to shorten context-free sentences.

B. How to repeat context-free sentences.

C. How to transit from a context-free sentence to its negative or interrogative version.

D. How to generate context-free sentences.

20. What is valency in linguistics?

A. A label at a word.

B. A link from one word to another.

C. A prepositional phrase.

D. A part of a labeled tree.

21. What is the difference between syntactic and semantic valencies?

A. Syntactic valencies link some words into pairs, while semantic valencies link other pairs.

B. Syntactic and semantic valencies link the same pairs of words, but in opposite directions.

C. Syntactic valencies describe the links between the same words as semantic valencies, but on different levels.

D. Syntactic and semantic valencies are essentially the same.

22. What is the notion of head in Head-driven Phrase Structure Grammar?

A. The principal subconstituent.

B. The center subconstituent.

C. The leftmost subconstituent.

D. The constituent that covers all its subconstituents.

23. What is unification in computational linguistics?

A. Standardization of features of wordforms.

B. Reducing wordforms to their dictionary forms.

C. Revealing similarities of features of different wordforms and uniting feature sets.

D. Uniting structures of several sentences into a common structure.

24. What is dependency tree in computational linguistics?

A. The same as constituency tree.

B. A labeled hierarchy of immediate links between wordforms in a sentence.

C. Hierarchy of meanings represented by words in a sentence.

D. Hierarchy of features assigned to wordforms in a sentence.

25. What applications of computational linguistics are the most developed now?

 

A. Grammar checking.

B. Spell checking.

C. Style checking.

D. Language understanding.

 

26. What applications of computational linguistics are the least developed now?

 

A. Grammar checking.

B. Language understanding.

C. Style checking.

D. Information retrieval.

 

27. What linguistic knowledge is used for automatic hyphenation?

A. How to use various fonts for different words.

B. What letters are vowels and consonants.

C. How to use lowercase and uppercase letters in writing.

D. How to combine usual words and numbers in a text.

28. What linguistic knowledge is used for spell checking?

A. How to use lowercase and uppercase letters in writing.

B. What are the laws of morphologic variations for words in this language.

C. What are rules of hyphenation in this language.

D. What words can stay adjacent in a text.

29. What linguistic knowledge is sufficient for grammar checking?

A. What syntactical constructions are correct in this language.

B. What words are supposedly correct in this language.

C. What phrases are commonly used in this language.

D. What words can stay adjacent in a text.

30. What linguistic knowledge is used for style checking?

A. What punctuation marks are appropriate in such a context.

B. How to arrange parts of a text in a proper sequence.

C. What words are more appropriate in a context.

D. How to split a text to adequate parts.

31. What linguistic knowledge is used in information retrieval systems?

A. Inverse files of terms.

B. Dictionaries and thesauri, consisting of terms.

C. Quick search algorithms.

D. Keyword sets at each document.

32. What are the main characteristics of information retrieval systems?

A. Response time.

B. Recall and precision.

C. Necessary size of memory for delivery.

D. User-friendliness of the interface.

33. How can we better determine automatically the theme of the document?

A. By considering the “hottest” themes for the present moment.

B. By considering the origin of the document.

C. By considering the words, which the document uses.

D. By considering the addressee of the document.

34. What is automatic text generation?

A. Deriving a text from some formal specifications.

B. Selecting entries in a preliminary prepared list of phrases.

C. Generating phrases basing on statistics of various language elements.

D. Derivation process of some generative grammar.

35. What is extraction of factual data from a text?

A. Determining what is the time of creation and the size of the text file.

B. Determining what audience this text is oriented to.

C. Determining qualitative and quantitative features of events, persons or things, which are touched upon in the text.

D. Determining linguistic means used in the text.

36. What is language understanding by a computer?

A. Transforming text into a binary code.

B. Transforming text into a graph representation.

C. Transforming text into a form that conserves the meaning and is directly usable by purposeful automata.

D. Transforming text into a table representation.

37. What are the main difficulties in creation of systems for language understanding?

A. Modern computers are insufficiently quick to solve the problem.

B. The ways of coding of meaning in texts are very complicated and are not sufficiently investigated.

C. Modern computers have insufficiently big memory to solve the problem.

D. Syntactic analysis gives too many variants.

38. What is WordNet (EuroWordNet)?

A. A usual dictionary, but in electronic form.

B. A thesaurus with a developed network of semantic links.

C. An electronic dictionary of synonyms.

D. An electronic dictionary in which we can find the part—whole links between words.

39. What linguistic knowledge does optical character recognition require?

A. How to use lowercase and uppercase letters in writing.

B. What strings of letters are correct words in writing.

C. What are rules of hyphenation in this language.

D. What words can stay adjacent in a text.

40. What linguistic knowledge does speech recognition require?

A. What variations of intonation do exist in this language.

B. What variations of logical stress do exist in this language.

C. What sequences of sounds are correct words in speech.

D. What words can stay adjacent in a speech in this language.

41. What is natural language?

A. Principal means for expressing human thoughts.

B. Principle means for text generation.

C. Bi-directional transformer Meaning Û Text.

D. Principal means of human communication.

42. What is a model in general?

A. It is an important part of the modeled system.

B. It imitates the most important features of the modeled system.

C. It includes the modeled system as the most important part.

D. It is connected with the modeled system within a system of higher rank.

43. What is the reduced model of a language?

A. It reflects all linguistic levels, but to different degree.

B. It models linguistic levels most important for the applied system.

C. It models surface linguistic levels.

D. It models deep linguistic levels.

44. What aspect of language is the least explored by modern linguistics?

 

A. Morphology.

B. Syntax.

C. Phonology.

D. Semantics.

 

45. What is a lexeme?

A. A set of letters.

B. A string of letters.

C. A set of wordforms with the same meaning.

D. A common meaning of several wordforms.

46. What entity forms the entry in a common vocabulary?

 

A. A word.

B. A wordform.

C. A lexeme.

D. A morph.

 

47. How many word occurrences are in the sentence Yo te amo, pero tú no me contesta como yo ‘I love you but you do not return me my love’?

 

A. Twelve.

B. Ten.

C. Nine.

D. Seven.

 

48. How many wordforms are in the sentence Yo te amo, pero tú no me contesta como yo ‘I love you but you do not return me my love’?

 

A. Twelve.

B. Ten.

C. Nine.

D. Seven.

 

49. How many lexemes are there in the sentence Yo te amo, pero tú no me contestas como yo ‘I love you but you do not return me my love’?

 

A. Twelve.

B. Ten.

C. Nine.

D. Seven.

 

50. What pair of the following ones consists of synonyms?

 

A. escoger, optar ‘choose, opt’.

B. tener, obtener ‘have, obtain’.

C. fuerza, debilidad ‘power, weakness’.

D. árbol, manzana ‘tree, apple’.

 

51. What are synonyms?

A. The signifieds are different, but the signifiers are equal.

B. The signifiers are different, but the signifieds are equal.

C. The signifieds are different, and the signifiers are different.

D. The signifieds are equal, and the signifiers are equal.

52. What are homonyms?

A. The signifieds are different, but the signifiers are equal.

B. The signifiers are different, but the signifieds are equal.

C. The signifieds are different, and the signifiers are different.

D. The signifieds are equal, and the signifiers are equal.

53. By what method used in order to enrich natural languages the Spanish words escuela and teatro have acquired the meaning ‘corresponding building’?

A. By metaphor.

B. By metonymy.

C. By loaning from other language.

D. By assigning a new meaning to an old word at random.

54. How many components does a linguistic sign have?

 

A. One

B. Two

C. Three

D. More than three.

 

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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

COMMON FEATURES OF MODERN MODELS OF LANGUAGE
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 &

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

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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