Tokenization: Tokenization is the process of breaking a string of text into smaller units called tokens. These tokens are usually words, numbers, or punctuation marks. The nltk module provides several tokenizers that can be used for tokenizing text. For example, the word_tokenize() function uses simple regex-based rules to tokenize a string into words. The sent_tokenize() function splits a text into a list of sentences.
Part-of-Speech (POS) Tagging: POS tagging involves assigning part-of-speech tags like noun, verb, adjective etc. to each token in a sentence. This helps in syntactic parsing and many other tasks. The nltk.pos_tag() function takes tokenized text as input and returns the same text with each token tagged with its part-of-speech. It uses probabilistic taggers trained on large corpora.
Named Entity Recognition (NER): NER is the task of locating and classifying named entities like persons, organizations, locations etc. mentioned in unstructured text into pre-defined categories. The nltk.ne_chunk() method recognizes named entities using optional regexes and can output grammatical structures. This information helps in applications like information extraction.
Stemming: Stemming is the process of reducing words to their root/stem form. For example, reducing “studying”, “studied” to the root word “stud”. Nltk provides a PorterStemmer class that performs morphological stemmer for English words. It removes common morphological and inflectional endings from words. Stemming helps in reducing data sparsity for applications like text classification.
Lemmatization: Lemmatization goes beyond stemming and brings words to their base/dictionary form. For example, it reduces “studying”, “studied” to the lemma “study”. It takes into account morphological analysis of words and tries to remove inflectional endings. Nltk provides WordNetLemmatizer which performs morphological analysis and returns the lemmatized form of words. Lemmatization helps improve Information Retrieval tasks.
Text Classification: Text classification involves classifying documents or sentences into predefined categories based on their content. Using features extracted from documents and machine learning algorithms like Naive Bayes Classifier, documents can be classified. Nltk provides functions to extract features like word counts,presence/absence of words etc. from texts that can be used for classification.
Sentiment Analysis: Sentiment analysis determines whether the sentiment expressed in a document or a sentence is positive, negative or neutral. This helps in understanding peoples opinions and reactions. Nltk has several pre-trained sentiment classifiers like Naive Bayes Classifier that can be used to determine sentiment polarity at document or sentence level. Features like presence of positive/negative words, emoticons etc are used for classification.
Language Identification: Identifying the language that a text is written in is an important subtask of many NLP applications. Nltk provides language identification functionality using n-gram character models. Functions like detect() can identify languages given a text sample. This helps in routing texts further processing based on language.
Text Summarization: Automatic text summarization involves condensing a text document into a shorter version preserving its meaning and most important ideas. Summary generation works by identifying important concepts and sentences in a document using features like word/sentence frequency, dialogue etc. Techniques like centroid-based summarization can be implemented using Nltk to generate summaries of documents.
Information Extraction: IE is the task of extracting structured information like entities, relationships between entities etc from unstructured text. Using methods like regex matching, entity clustering, open IE techniques and parsers, key information can be extracted from texts. Nltk provides functionalities and wrappers around open source IE tools that can be leveraged for tasks like building knowledge bases from documents.
Named Entity Translation: Translating named entities like person names, locations etc accurately across languages is a challenging task. Nltk provides methods and data to transliterate named entities from one language to another phonetically or by mapping entity with same meaning across languages. This helps in cross-lingual applications like question answering over multi-lingual data.
Topic Modeling: Topic modeling is a statistical modeling technique to discover abstract “topics” that occur in a collection of documents. It involves grouping together words that co-occur frequently to form topics. Using algorithms like Latent Dirichlet Allocation(LDA) implemented methods in Nltk, topics can be automatically discovered from document collections that best explains the co-occurrence of words.
These are some of the common NLP tasks that can be accomplished using the Python modules – string, re and nltk. Nltk provides a comprehensive set of utilities and data for many NLP tasks right from basic text processing like tokenization, stemming, parsing to higher level tasks like sentiment analysis, text classification, topic modeling etc. The regular expression module (re) helps in building custom patterns for tasks like named entity recognition, normalization etc. These Python libraries form a powerful toolkit for rapid development of NLP applications.