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Text Mining Techniques for NLP: Preprocessing and Tokenization

Information retrieval, Natural Language Processing (NLP), and Text Mining require preprocessing as a crucial stage in the process (IR). Data preprocessing is used in the field of text mining to draw out valuable and complex information from unstructured text data. The main task of information retrieval (IR) is to select the documents from a collection that should be retrieved in order to meet a user’s informational needs.

A query or profile, which includes one or more search phrases and extra information, such as the words’ weight, represents the user’s need for information. Thus, the retrieval choice is determined by contrasting the query’s terms with the index terms (critical words or phrases) present in the actual document.

The choice may be binary (retrieve/reject), or it may entail determining how relevant the material is to the query. Sadly, there are numerous structural variations among the terms that occur in documents and queries.

In order to lower the size of the target data set and improve the performance of the IR System, data preparation techniques are used before information extraction from documents. This study’s goal is to examine the challenges surrounding text document preprocessing techniques such as tokenization, stop word removal, and stemming. Keywords: text mining, NLP, IR, stemming.

Unstructured and noisy text data is produced from natural language. Preprocessing involves converting text into a clear and consistent format that can be fed into a model for additional analysis and learning.

Text preprocessing methods can be general so that they can be used in a variety of applications, or they can be tailored for a particular goal. The techniques for handling user comments on social media, for instance, can be very dissimilar from those used for processing scientific articles with equations and other mathematical symbols.

Here’s what you need to know about text preprocessing to improve your natural language processing (NLP)!

  1. The NLP Preprocessing Pipeline

Text is read, processed, analyzed, and interpreted by a system that uses natural language processing. The system first goes through a number of stages to preprocess the text in a more structured manner. Preprocessing pipelines are so-called because the output from one stage serves as the input for the next.

Sentence segmentation, word tokenization, lowercasing, stemming or lemmatization, stop word removal, and spelling correction are some examples of NLP pipeline processes that may be used to classify documents. In typical NLP systems, some or all of these regularly used text preprocessing phases are employed; however, the sequence may change depending on the application.

  1. Segmentation

Text is segmented by dividing it into related sentences. Although it can appear simple, there are a few obstacles. For instance, a period generally marks the end of a sentence in the English language. Still, numerous abbreviations, such as “Inc.,” “Calif.”, “Mr.” and “Ms,” as well as all fractional numbers, contain periods and cause ambiguity unless the end-of-sentence rules take these exceptions into account.

  1. Tokenization

Tokenization is breaking down a sentence into a stream of words, or “tokens.” The fundamental building blocks on which analysis and other techniques are based are tokens.

Several NLP toolkits allow users to enter various parameters that are used to establish word boundaries.

To tell if one-word ends and the following one begins, for instance, you can use whitespace or punctuation. Again, these rules may not always apply. For instance, words like don’t, it’s, etc., contain punctuation on their own and must be handled independently.

4.   Stemming

The base or root form of a word is referred to as the word stem, a phrase that was adopted from linguistics. For instance, the base word learn serves as the basis for its variations learn, learns, learning, and learning.

Stemming is the process of reducing each word to its stem or fundamental form. The term and its accompanying stem are often found using a lookup table. Several search engines employ stemming from getting documents that match user searches. For applications like emotion recognition and text categorization, stemming is also used during the preprocessing phase.

Wrapping Up

So, this was all you needed to know about NLP’s text mining techniques. Being a subsidiary of Sambodhi Research and Communications Pvt. Ltd., Education Nest is a global knowledge exchange platform that empowers learners with data-driven decision making skills.

If you wish to explore more about NLP, then our highly comprehensive set of courses can help polish your skills seamlessly.

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