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What statistical techniques are essential for natural language processing?

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Natural language processing (NLP) is a branch of data science that deals with analyzing, understanding, and generating natural language from text or speech data. NLP applications include chatbots, sentiment analysis, machine translation, text summarization, and more. To perform these tasks, NLP relies on various statistical techniques that help extract meaningful information and patterns from language data. In this article, you will learn about some of the essential statistical techniques for NLP and how they are used in different scenarios.

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1 Probability and Bayes' theorem

Probability is the measure of how likely an event is to occur, given some evidence or prior knowledge. Bayes' theorem is a formula that allows you to update your probability estimates based on new data. For example, if you want to classify an email as spam or not, you can use Bayes' theorem to calculate the probability of spam given the words in the email, and compare it with the probability of not spam. This is called a naive Bayes classifier, which is a simple and effective technique for text classification.

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Ananya Ghosh Chowdhury

Data and AI Architect at Microsoft | Responsible AI Advocate | Public Speaker | Startup Advisor | Career Mentor | Harvard Business Review Advisory Council Member | Marquis Who's Who Listee | Founder @AIBoardroom
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For spam and not spam email filtering, Na?ve Bayes also performs bag-of-words (BOW): basically classifiers identify which tokens are correlated with emails labeled as spam. In other words, the technique finds which group of words only occurs in one type of email and not in the other. For example, the words miracle cure, lose weight fast, and anti-aging may appear more frequently in spam emails about dubious health products than your regular emails.

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2 Frequency and distribution

Frequency is the number of times a word or a feature occurs in a text or a corpus. Distribution is the way the frequency of a word or a feature varies across different texts or contexts. For example, if you want to measure the similarity of two documents, you can use the frequency of words as a feature and calculate the cosine similarity between their vectors. This is called a bag-of-words model, which is a basic technique for text representation. Alternatively, you can use the distribution of words across different documents to measure their importance and relevance. This is called a term frequency-inverse document frequency (TF-IDF) model, which is a technique for text weighting.

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3 Hypothesis testing and confidence intervals

Hypothesis testing is a method of making decisions or inferences based on statistical evidence. Confidence intervals are ranges of values that contain the true parameter of interest with a certain level of confidence. For example, if you want to evaluate the performance of a sentiment analysis model, you can use hypothesis testing to compare the accuracy of the model with a baseline or a benchmark. You can also use confidence intervals to estimate the range of accuracy that the model can achieve with a given sample size and error rate.

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4 Correlation and regression

Correlation is a measure of how two variables are related or associated with each other. Regression is a method of modeling the relationship between a dependent variable and one or more independent variables. For example, if you want to predict the sentiment of a text based on its features, you can use correlation to identify the features that are most related to the sentiment. You can also use regression to build a model that can estimate the sentiment score based on the features.

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5 Clustering and dimensionality reduction

Clustering is a method of grouping similar or related data points together based on some criteria or distance measure. Dimensionality reduction is a method of reducing the number of features or dimensions of the data without losing much information or structure. For example, if you want to discover the topics or themes in a large collection of texts, you can use clustering to find the texts that share common words or concepts. You can also use dimensionality reduction to transform the high-dimensional text data into a lower-dimensional space that can capture the main variations or patterns. This is called a topic model, which is a technique for text mining and analysis.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Kaushik B.

Co-Founder, CTO & Chief Data Scientist @ InxiteOut | Ex Western Digital, Intel, AMD | IIM-B Gold Medal | US Patent | ML & AI Advisor
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There are many more techniques used in NLP. An exhaustive list is too difficult to compile, but some of the very common techniques that are not covered in this article are: Hidden Markov Model (works by assigning joint probability to paired observation and label sequence), Maximum Entropy Markov Model (works on the principle that the least biased model which considers all known facts is the one which maximizes entropy), Conditional Random Field (based on undirected graphical models to predict sequences of labels), Genetic Algorithm (an evolutionary computing technique involving interaction of agents to model complex behavior in language) etc.

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Jane Z.

Machine Learning | Data Science | Python | Neural Networks | Space Fan | Sic-Fi Author | Lifelong Learner
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Hidden Markov Models for assigning grammatical categories to each words in the sentence. Text classification to categorize text documents. Here only limited to statistical methods. Other machine learning methods are also essential parts of NLP.

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What statistical techniques are essential for natural language processing?

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3

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5

6

1 Probability and Bayes' theorem

2 Frequency and distribution

3 Hypothesis testing and confidence intervals

4 Correlation and regression

5 Clustering and dimensionality reduction

6 Here’s what else to consider

Data Science

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What statistical techniques are essential for natural language processing?

1

2

3

4

5

6

1 Probability and Bayes' theorem

2 Frequency and distribution

3 Hypothesis testing and confidence intervals

4 Correlation and regression

5 Clustering and dimensionality reduction

6 Here’s what else to consider

Data Science

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