Text concordance

agreement in case, number and gender - согласование по падежу, числу и роду (грамм.)

annotate - аннотировать, добавлять краткие комментарии к тексту для объяснения тех или иных фрагментов (e.g. an annotated edition of ‘Othello’); отмечать, размечать

annotated corpus - размеченный корпус, аннотированный корпус

API (Application Program Interface) - интерфейс прикладных программ

application domain - область применения

automatically acquitted models -автоматически полученные модели

corpus - собрание, совокупность, множество, коллекция, корпус; pl. corpuses or corpora

crawling - сканирование, «обход» серверов с помощью программного робота (crawler, краулера, «паука») с целью помещения доступных документов в базу данных поисковой системы

disambiguated - лишенный неоднозначности, со снятой неоднозначностью

domain mismatch - несоответствие областей

hard-coded linguistic rules - жестко кодируемые лингвистические правила

hard constraints - жесткие ограничения

held-out portion - установленная выборка, тестовая выборка

graded constraints - градиентные ограничения

lemma - инвариантная (базовая) форме слова, отвлеченная от грамматических вариаций (e.g. the verb “sing” or “to sing”, in abstraction from the varying word forms sing, sings, sang, sung, singing).

lemmatization - лемматизация - приведение слова к лемме - базовой инвариантной форме

lemmatize - приводить слово к базовой инвариантной форме - лемме

long- distance dependencies - отдаленные зависимости

machine learning - машинное обучение, обучение машины (модели, системы)

natural language processing (NLP) - обработка естественного языка

nominative singular masculine noun - существительное мужского рода единственного числа в именительном падеже

pipeline - конвейер, непрерывная последовательность, цепь

pre-tokenized text - предварительно разбитый (на предложения, слова) текст

problem of unknown knowns - проблема неизвестных известных

syntactic parsing - синтаксический разбор, ~ анализ

tag - тег, маркер, метка

tag label - маркер тега

tag labeling - разметка тегов

tagger - теггер ( a piece of software that adds identifying or classifying tags to pieces of text or data)

tagging - разметка; part-of-speech tagging (POS) - частеречная разметка

training data - обучающие данные

training process - процесс обучения (модели)

training set -обучающая выборка

trigram - триграмма

DISCUSSION ISSUES

1) Fred Jelinek claimed that every time he fired a linguist the results of speech recognition went up.

What does the author mean saying that? What is the author’s opinion of linguistic knowledge functionality? Do you hold the same opinion on the issue at stake?

2) Why is computer-aided speech recognition in the mainstream of today’s research? Is this a recurrent theme in numerous scientific publications?

3) What are the two basic approaches to natural language computer processing described in the article? Which seems more efficient to you?

4) Does natural language computer processing correlate with any basics of human speech recognition? What kind of mental mechanisms prevail in human foreign language acquisition? Those based on hard-stated linguistic rules explicitly created by teachers? Those based on working through massive language corpora, both annotated and raw?

5) What are some basic procedures for designing a statistical language processing method?

Here is some info on the point.

Each procedure can be described in the following lines:

a) taking an annotated text corpus;

b) designing a simplified representation of annotations to convert the corpus into the format suitable for the learning tool to de used;

c) learning a model in several iterations to tune the learning parameters.

“Thus, in this approach the human efforts are invested into creating annotated corpora, representing data and designing machine learning algorithms, while the machine is able to learn the links between the data. In the end, linguistic knowledge is induced from annotated corpora rather than explicitly hand-crafted by linguists”. (See the text above).

Are there entirely statistical methods applied to building tools for language processing? Account for the difference between text annotation and explicit formulation of language rules. Which approach is closer to the natural process of human language acquisition?

6) According to computer experts, annotating texts implies tagging - assigning a label (tag) to each word concerned. Until the end of the 19980s this task had been usually performed by sets of carefully crafted rules for disambiguating contexts, e.g. for detecting contexts in which the form ‘known’ is a participle or a noun. Ken Church was one of the first researchers to show the possibility of abandoning the rules and relying exclusively on annotated data. In the frame of statistical approaches, tagging implies automatic derivation of decision trees or machine learning.

7) What kind of issues do researchers encounter with statistical approaches to natural language processing? What are the main advantages of using statistical methods for natural language processing?