The field of Natural Language Processing has undergone a dramatic change in the last few years. Natural Language Processing, or NLP, is a sub-branch of Artificial Intelligence that deals with providing the computer the ability to understand human languages. Specifically, Natural Language Processing deals with the understanding of the text and spoken words by the computer. Natural Language Processing is a vast multidisciplinary field and consists of several methodologies. This article aims to provide a general introduction to the NLP field and an overview of how it works.
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Natural Language Processing
NLP can be defined as the automatic processing of natural languages. The goal of NLP is to enable computers to understand the language spoken by humans. This field includes all the aspects that any language has, including phonology, morphology, syntax, semantics, and pragmatics. NLP combines the linguistics rule with statistical modeling and deep learning models. In the Computer Science field, NLP refers to formal language theory, compiler techniques, and human-to-machine interaction.
Challenges in Natural Language Processing:
Human language is inherently complex and ambiguous. This is why it is often challenging for machines to solve NLP problems. Therefore, the machines require better representation and learning capability to understand language tasks. Also, as humans, we rely on a substantial amount of contextual information and background knowledge. It includes implicit references, implicit memory, and colloquialism. Hence, machines require a detailed representation for extracting semantics from language.
Natural Language Processing in the Real World:
NLP has become a crucial part of our daily lives. Several NLP applications have been implemented in the real world to make lives easier. Some of the most widely used applications are:
- Email platforms, for example, Gmail, Outlook, etc., use NLP to perform tasks such as spam classification, priority inbox, and auto-complete text.
- Personal voice-based assistants, such as Siri, Cortana, and Alex, employ a wide range of NLP techniques to communicate with the users and comprehend their needs.
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- Modern Search Engines, including Google and Bing, also rely heavily on NLP techniques, such as querying the data, answering the user questions, information retrieval and search result ranking.
- Machine Translation services, for instance, Google Translate, also uses NLP to solve a wide range of translation scenarios.
- Ecommerce platforms, such as Amazon, widely use NLP to solve a variety of business use cases which includes extraction of information from product descriptions and analyzing sentiments from user reviews.
How Does Natural Language Processing Work
Natural Language Processing models require the text to go through a series of steps that makes it easier for the machine to process textual sequenced data. A generic NLP Pipeline consists of the following steps to make the data model ready:
- Text Segmentation: Text Segmentation is the task of splitting a chunk of text into logical decipherable units.
- Case Conversion: The next step is to convert the segments into lower case. It makes it easier for the machine to not get confused with the uppercase and lowercase letters.
- Tokenization of Words: In this step, the segmented units are then split into words. Individual words will help the machine in understanding the syntactic and semantic information of each word.
- Removing Punctuation Marks and Special Characters: Removing punctuation marks and special characters from the text is also a crucial step in preparing the data for the model as machines do not need it for understanding human language.
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- Parts of Speech Tagging: This is the process of finding the parts of speech of a word based on its context in a sentence.
- Lemmatization / Stemming: This process involves reducing the word to their base form by chopping their suffixes and performing morphological analysis to find the root word.
- Embedding and Encoding: Now that the data has been preprocessed, the last step is to encode it to prepare for the machine. As computers can only understand numerical vectors, the textual data should be embedded and encoded to be passed through the model. One of the most basic techniques is Bag of Words. Other techniques include Integer Encoding, Word Embedding, N-grams, TF-IDF, Word2Vec, and FastText.
Conclusion
Natural Language Processing models have previously used statistics to solve various problems. However, with the advancement in neural word embeddings and deep learning techniques, the NLP models have become much easier to design. The article has provided a basic guide on Natural Language Processing, its challenges, and its working.
Happy learning, friends!