THE ROLE OF LINGUISTIC CORPORA IN DETERMINING WORD FREQUENCY

Аннотация

N-gram modeling is a fundamental technique in corpus linguistics and natural language processing (NLP), used to analyze the statistical patterns of language based on contiguous sequences of words or characters. By representing text as a series of N-grams (unigrams, bigrams, trigrams), researchers and engineers can model language probabilities, detect linguistic trends, and develop tools such as predictive text, spell-checkers, and machine translation systems. The method relies on frequency analysis and conditional probability to predict the likelihood of word sequences, often applying smoothing techniques to manage data sparsity. Despite its simplicity and effectiveness, N-gram models have limitations in capturing long-range dependencies and semantic context, which newer neural models address. Nevertheless, N-gram approaches remain valuable for baseline models, resource-limited languages, and corpus-driven linguistic research. Keywords: N-gram, technique, corpus, translation, linguistics, tools, text, language, statistics. Аннотация. Моделирование N-грамм-это один из основных методов корпусной лингвистики и обработки естественного языка (NLP), который используется для статистического анализа языковых структур. Данный подход представляет текст в виде последовательностей из N элементов (например, униграммы, биграммы, триграммы), позволяя определять частоту их появления и вычислять условные вероятности. Модели на основе N-грамм применяются в таких задачах, как предсказание текста, проверка орфографии, машинный перевод и другие языковые технологии. Для устранения проблемы нехватки данных используются методы сглаживания. Несмотря на то что N-граммные модели не способны учитывать дальние зависимости и семантический контекст, они остаются эффективными и простыми в реализации, особенно при работе с языками с ограниченными ресурсами и в корпусных лингвистических исследованиях. Ключевые слова: N-граммы, метод, корпус, перевод, лингвистика, инструменты, текст, язык, статистика. 49 ACADEMIC RESEARCH IN MODERN SCIENCE International scientific-online conference N-gram is a statistical method used to analyze text or speech by breaking it into contiguous sequences of N items (usually words or characters). It's widely used in natural language processing (NLP), language modeling, machine translation, and text prediction. In corpus linguistics, an N-gram is a continuous sequence of N items (typically words or characters) from a given text or speech sample. These sequences help analyze linguistic patterns statistically. The training set is the data we use to learn the parameters of our model; for simple N-gram language models it’s the corpus from which we get the counts that we normalize into the probabilities of the n-gram language model. The test set is a different, held-out set of data, not overlapping with the training set, that we use to evaluate the model. We need a separate test set to give us an unbiased estimate of how well the model we trained can generalize when we apply it to some new unknown dataset. A machine learning model that perfectly captured the training data, but performed terribly on any other data, wouldn’t be much use when it comes time to apply it to any new data or problem! We thus measure the quality of an n-gram model by its performance on this unseen test set or test corpus. An n-gram is a sequence of n words: a 2-gram (which we’ll call bigram) is a two-word sequence of words like The water, or water of, and a 3-gram (a trigram) is a three-word sequence of words like The water of, or water of Walden. But we also (in a bit of terminological ambiguity) use the word ‘n gram’ to mean a probabilistic model that can estimate the probability of a word given the n-1 previous words, and thereby also to assign probabilities to entire sequences. ( T. M. Cover. 1988.) For example: “I am going to school.” • Unigrams: ["I", "am", "going", "to", "school"] • Bigrams: [("I", "am"), ("am", "going"), ("going", "to"), ("to", "school")] • Trigrams: [("I", "am", "going"), ("am", "going", "to"), ("going", "to", "school")] Language models are widely used in text generation, and they also help identify the relationships between words. Statistical analysis is also involved, meaning it helps determine which units are used most frequently. A statistical model is a mathematical model which is modified or trained by the input of data points. Statistical models are often but not always probabilistic. Where the distinction is important we will be careful not just use "statistical" but to use the following another terms: A mathematical model specifies a relation among variables, either in functional form that maps inputs 50 ACADEMIC RESEARCH IN MODERN SCIENCE International scientific-online conference to outputs (e.g. y = m x + b) or in relation form (e.g. the following (x, y) pairs are part of the relation). A probabilistic model specifies a probability distribution over possible values of random variables, e.g., P(x, y), rather