- •Section 2
- •Determiners, pre-determiners and pre-modifiers
- •2.1. Commentaries and explanations
- •2.1.4. Substitution and omission as basic mechanisms of discourse.
- •2.2. Assignments
- •2.2.1. A) Complete the sentences using all proper pre-determiners and determiners defined by the context (including “no articles”).
- •2.2.4. A) Complete the following utterances using hints. Make use of proper pre- and central determiners, pre-modifiers and prepositional groups.
- •2.2.5. Read and translate the following word groups into Russian distinguishing between the meanings of participles. .
- •2.2.6. Specify through participle modifiers. Use the given verbs as hints. In some examples both participles can be used; explain the difference in meaning.
- •2.2.7. Read the text and specify the meaning of be stuck by choosing the appropriate description:
- •2.2.8. Read and do tasks (a) - ( ) given below.
- •2.2.9. Specify nouns through other nouns.
- •2.2.10. Practice nouns in sequence using a dictionary if necessary:
- •2.2.11. Arrange the words modifying the marked noun into a string of pre-modifiers and make a sentence using it.
- •2.2.12. Make use of some compound modifiers according to the sample pattern.
- •2.2.13. Practice compound pre-modifiers answering the following questions and arranging a discussion.
- •2.2.14. Comment on the statements using adverbs “modifying modifiers”.
- •2.2.15. Practice substitution and omission as complementary speech mechanisms.
- •2.2.16. Analyze italicized substitutes and restore their reference. There are items marked which are not substitutes - specify them.
- •2.2.17. Analyze the following noun groups arranged according to types of pre-modifiers and give their Russian equivalents.
- •2.2.18. Read the text and discuss its basic ideas relying on analyzing the modifiers of 2.2.12 as well as the questions and vocabulary notes given below.
- •Text concordance
- •2.2.19. Practice vocabulary.
2.2.17. Analyze the following noun groups arranged according to types of pre-modifiers and give their Russian equivalents.
a) pre-determiners and determiners: every time, (with) more and more data (available), more approaches, this paradigm, (in) some cases, (in) this approach, the more probable, the less probable, (in) this sequence, another problem, each training set, (each training set has) its own peculiarities, other text genres, more training data, available data, a large number (of texts), more data, in either case,
b) adjectives: the last few decades, the proper place, the right distribution (of labor), human translators, explicit linguistic knowledge, a paradigmatic shift, computational linguistics, careful encoding, linguistic phenomena, statistical correlations, raw texts, the vast majority, major conferences, linguistic knowledge, an automatic procedure, a fast and reliable component, linguistic rules, human efforts, (in) a similar way, manual selection, hard constraints, the internal structure, statistical methods, training data, training set, particular corpora, (in) new domains, formal evaluation, a training corpus, good accuracy, the same distribution, automatic classification, a particular topic, (at) the same time, the main contribution, minimal adjustment, the entire pipeline, syntactic parsing
c) participles: annotated texts, annotated corpora, the induced rules, graded constraints, the acquitted models, data representation, available annotated data, annotated examples, unlabelled examples, the known problem, the annotated set, asked questions, pre-tokenized texts, under strained circumstances
d) other nouns: speech recognition, research mainstream, language translation, translation system, the key problem, machine learning, annotation set, a masculine noun, machine learning algorithms, a search engine, tag labeling, a tag label, a noun phrase, the application domain, text genres, domain adaptation, the source domain, the target domain, the domain mismatch, machine learning, classification decisions,
genre classification, a genre heading, learning parameters, the survey analysis, source data
e) compounds: computer-aided recognition, computer-assisted system, hand-created data, the held-out portion (of the annotated set), a rule-based tagger, rule-based systems, POS tagging = part-of-speech tagging, out-of-control temper,
f) adverbs + adjective / participle: a fairly radical stance, a completely automatic procedure, explicitly hand-created data, the automatically induced rules, the automatically acquitted models, purely statistical methods, reasonably good accuracy, frequently asked questions, for natural language processing,
g) mixed concatenations: computer-aided speech recognition, computer-assisted translation system, a proper nominative singular masculine noun, a completely automatic machine learning procedure, a fast and reliable NLP component, hard-coded linguistic rules, available annotated data, automatic genre classification,