Tasks of Natural Language Processing

NLP has a multitude of real-world applications. A good NLP system is that which performs many NLP tasks. When you search for today's weather on Google or use Google Translate to find out how to say, "How are you?" in French, you rely on a subset of such tasks in NLP. We will list some of the most ubiquitous tasks here, and this book covers most of these tasks:

• Tokenization: Tokenization is the task of separating a text corpus into atomic units (for example, words). Although it may seem trivial, tokenization is an important task. For example, in the Japanese language, words are not delimited by spaces nor punctuation marks.
• Word-sense Disambiguation (WSD): WSD is the task of identifying the correct meaning of a word. For example, in the sentences, The dog barked at the mailman, and Tree bark is sometimes used as a medicine, the word bark has two different meanings. WSD is critical for tasks such as question answering.
• Named Entity Recognition (NER): NER attempts to extract entities (for example, person, location, and organization) from a given body of text or a text corpus. For example, the sentence, John gave Mary two apples at school on Monday will be transformed to [John]_name gave [Mary]_name [two]_number apples at [school]_organization on [Monday.]_time. NER is an imperative topic in fields such as information retrieval and knowledge representation.
• Part-of-Speech (PoS) tagging: PoS tagging is the task of assigning words to their respective parts of speech. It can either be basic tags such as noun, verb, adjective, adverb, and preposition, or it can be granular such as proper noun, common noun, phrasal verb, verb, and so on.
• Sentence/Synopsis classification: Sentence or synopsis (for example, movie reviews) classification has many use cases such as spam detection, news article classification (for example, political, technology, and sport), and product review ratings (that is, positive or negative). This is achieved by training a classification model with labeled data (that is, reviews annotated by humans, with either a positive or negative label).
• Language generation: In language generation, a learning model (for example, neural network) is trained with text corpora (a large collection of textual documents), which predict new text that follows. For example, language generation can output an entirely new science fiction story by using existing science fiction stories for training.
• Question Answering (QA): QA techniques possess a high commercial value, and such techniques are found at the foundation of chatbots and VA (for example, Google Assistant and Apple Siri). Chatbots have been adopted by many companies for customer support. Chatbots can be used to answer and resolve straightforward customer concerns (for example, changing a customer's monthly mobile plan), which can be solved without human intervention. QA touches upon many other aspects of NLP such as information retrieval, and knowledge representation. Consequently, all this makes developing a QA system very difficult.
• Machine Translation (MT): MT is the task of transforming a sentence/phrase from a source language (for example, German) to a target language (for example, English). This is a very challenging task as, different languages have highly different morphological structures, which means that it is not a one-to-one transformation. Furthermore, word-to-word relationships between languages can be one-to-many, one-to-one, many-to-one, or many-to-many. This is known as the word alignment problem in MT literature.

Finally, to develop a system that can assist a human in day-to-day tasks (for example, VA or a chatbot) many of these tasks need to be performed together. As we saw in the previous example where the user asks, "Can you show me a good Italian restaurant nearby?" several different NLP tasks, such as speech-to-text conversion, semantic and sentiment analyses, question answering, and machine translation, need to be completed. In Figure 1.1, we provide a hierarchical taxonomy of different NLP tasks categorized into several different types. We first have two broad categories: analysis (analyzing existing text) and generation (generating new text) tasks. Then we divide analysis into three different categories: syntactic (language structure-based tasks), semantic (meaning-based tasks), and pragmatic (open problems difficult to solve):

Figure 1.1: A taxonomy of the popular tasks of NLP categorized under broader categories

Having understood the various tasks in NLP, let us now move on to understand how we can solve these tasks with the help of machines.