Initial strategies in the machine processing of natural language
Traditional methods in NLP consist of text preprocessing, which is synonymous with text preparation, which is then followed by applying ML methods. Preprocessing text is an essential step in NLP and ML applications. It involves cleaning and transforming the original text data into a form that can be easily understood and analyzed by ML algorithms. The goal of preprocessing is to remove noise and inconsistencies and standardize the data, making it more suitable for advanced NLP and ML methods.
One of the key benefits of preprocessing is that it can significantly improve the performance of ML algorithms. For example, removing stop words, which are common words that do not carry much meaning, such as “the” and “is,” can help reduce the dimensionality of the data, making it easier for the algorithm to identify patterns.
Take the following sentence as an example:
I am going to the store to buy some milk and bread.
After removing the stop words, we have the following:
going store buy milk bread.
In the example sentence, the stop words “I,” “am,” “to,” “the,” “some,” and “and” do not add any additional meaning to the sentence and can be removed without changing the overall meaning of the sentence. It should be emphasized that the removal of stop words needs to be tailored to the specific objective, as the omission of a particular word might be trivial in one context but detrimental in another.
Additionally, stemming and lemmatization, which reduce words to their base forms, can help reduce the number of unique words in the data, making it easier for the algorithm to identify relationships between them, which will be explained completely in this book.
Take the following sentence as an example:
The boys ran, jumped, and swam quickly.
After applying stemming, which reduces each word to its root or stem form, disregarding word tense or derivational affixes, we might get:
The boy ran, jump, and swam quick.
Stemming simplifies the text to its base forms. In this example, “ran,” “jumped,” and “swam” are reduced to “ran,” “jump,” and “swam,” respectively. Note that “ran” and “swam” do not change, as stemming often results in words that are close to their root form but not exactly the dictionary base form. This process helps reduce the complexity of the text data, making it easier for machine learning algorithms to match and analyze patterns without getting bogged down by variations of the same word.
Take the following sentence as an example:
The boys ran, jumped, and swam quickly.
After applying lemmatization, which considers the morphological analysis of the words, aiming to return the base or dictionary form of a word, known as the lemma, we get:
The boy run, jump, and swim quickly.
Lemmatization accurately converts “ran,” “jumped,” and “swam” to “run,” “jump,” and “swim.” This process takes into account the part of speech of each word, ensuring that the reduction to the base form is both grammatically and contextually appropriate. Unlike stemming, lemmatization provides a more precise reduction to the base form, ensuring that the processed text remains meaningful and contextually accurate. This enhances the performance of NLP models by enabling them to understand and process language more effectively, reducing the dataset’s complexity while maintaining the integrity of the original text.
Two other important aspects of preprocessing are data normalization and data cleaning. Data normalization includes converting all text to lowercase, removing punctuation, and standardizing the format of the data. This helps to ensure that the algorithm does not treat different variations of the same word as separate entities, which can lead to inaccurate results.
Data cleaning includes removing duplicate or irrelevant data and correcting errors or inconsistencies in the data. This is particularly important in large datasets, where manual cleaning is time-consuming and error-prone. Automated preprocessing tools can help to quickly identify and remove errors, making the data more reliable for analysis.
Figure 1.1 portrays a comprehensive preprocessing pipeline. We will cover this code example in Chapter 4:
Figure 1.1 – Comprehensive preprocessing pipeline
In conclusion, preprocessing text is a vital step in NLP and ML applications; it improves the performance of ML algorithms by removing noise and inconsistencies and standardizing the data. Additionally, it plays a crucial role in data preparation for NLP tasks and in data cleaning. By investing time and resources in preprocessing, one can ensure that the data is of high quality and is ready for advanced NLP and ML methods, resulting in more accurate and reliable results.
As our text data is prepared for further processing, the next step typically involves fitting an ML model to it.
