Solving 90% of Challenges In NLP Processes

Textual data is everywhere!

Be it an established business or a start-up, leveraging large volumes of data to validate, improvise and expand your business needs to be done in par with all other functions. The art of extracting data is a very active field of research. This uses Natural language processing techniques-NLP.

NLP can generate new and wonderous results on a daily basis from the data that is extracted.

Some of the practical applications of implementing NLP techniques are:

• Identification of the various cohorts of customers/users
• Detecting and extracting the various categories of feedback accurately
• Classification of text in accordance with intent
• Text/Data Classification in accordance with intent

Every problem in a machine learning algorithm starts with data. Some of the sources of these text data would include- product reviews, user-generated tweets/posts, customer requests, chatlogs, and many more. the trick to avoid a machine learning error is to label the data. By labelling the data, the text extraction software can understand the parent file of the data and provide clear and accurate insights.

When your data is good, your model is good too! Analysing the data and then cleaning it up can save many inaccurate outcomes. A clean set of data can allow the model to learn without multiple matches. Clearing up your data can be done by,

• Removing irrelevant characters like non-numeric characters
• Tokenization of text by separating them into individual words.
• Removing words, phrases, symbols, etc that are not relevant.
• Converting all the characters to lowercase so that the software learns to read all the uppercase, sentence case and lowercase words the same.
• Combining misspelled words into a single word.

Machine learning models usually understand words, images, symbols, letters, etc as numerical values. Finding a way to represent the dataset in a way that is understandable by the machine learning algorithm is the key to successful NLP outcomes.

Classifying the data can simplify the machine’s learning through logical regression. The data can be split into small datasets to fit into the model for greater accuracy.

Inspection of the data that is being extracted is important in order to create a quality dataset that can be analysed to bring out business insights through NLP solutions. Inspection of data begins with the software understanding the errors and irrelevant words, letters or symbols in the extracted data.  A confusion matrix is to be created consisting of the irrelevant characters and errors which is fed into the software to understand those characters for accurate results. These irrelevant and errors need to be explained for example, words that could have been misspelled by the customer/user.

To help our machine learning model to focus on the meaningful words, a TF-IDF score can be used. A TF-IDF (Term Frequency Inverse Document Frequency) score weighs words based on their occurrence in the existing dataset, noise of the words, and discounting the words that are frequently used. Logistical regression can handle this score to provide NLP process success.

Machine learning algorithms come across words that mean the same- synonyms. These words will be classified as separate categories. To solve this, the semantics of the words must be fed to the machine. Words that mean the same or synonyms need to be classified under a single category.

It might be quite interesting to read on The Semantic Search Capabilities of teX.ai: Why It Is a Key Differentiator

Another way of solving this problem is by using pre-defined words. Pre-defined words or pre-trained words can be fed to the machine to avoid classification of similar words. For example, good, positive, excellent have similar meanings when it comes to a customer review analysis. These groups of words that are similar in meaning can be pre-defined and fed to the machine.

In some cases, while omitting order of words can result in the loss of syntactic information. To avoid this error, a sentence must be treated as a sequence of singular word vectors. CNN (Conventional Neural Networks) for sentence classification can provide the entry level machine learning architecture. CNN can train NLP approaches by identifying image data and text data which can preserve the syntax of the words and their individual meanings.

Some final notes to solve your NLP problems,

• Starting with a simple and quick model
• Explaining the possible predictions of the model
• Understanding the mistakes that are made and using that knowledge to feed the machine with relevant algorithms.

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