Text data is ubiquitous these days! While computers find this knowledge difficult to interpret, people can understand it with ease. Natural Language Processing (NLP) is the science that deals with deciphering and learning from textual data. When trying to educate computers to read natural language text data, programmers face some frequent difficulties.

Let’s talk about these challenges in detail and offer some suggestions to help handling NLP easier for you.

1. Unstructured Data & Big Data

The most frequent problems in NLP are related to big data and unstructured data. Online discussions, tweets, comments, and other forms of data generation produce “big” and largely unstructured data. Processing the data and extracting meaningful information from it is a very difficult task.

The following methods can transform the big data & unstructured data into writing that is helpful or meaningful for machines:

• Processing of Data – It means removal of unwanted URLs, HTML tags, stop words, numeric and alphanumeric words, punctuation, and special characters. It also includes converting texts into lowercase.
• Data Standardization – Converting words into standard forms, such as making contractions into full words (e.g., “can’t” becomes “can not”), is known as Data Standardization. 
• Lemmatization – It is the process of reducing a word to its most basic, meaningful form. For instance, “tries” becomes “tries,”. Thus, the system will treat terms like “tried,” “tries,” and “try” as different occurrences of the same word: “try.” 
• Word Tokenization – Tokenization is the process of dividing the text into words or phrases. Tokens are these divided units. Tokenization is crucial to NLP since it makes it simple to understand a text’s main ideas through token analysis.

2. Semantic Meaning of Words

The semantic meaning of words presents another frequent difficulty. Any given language has a fairly large vocabulary, and many words have similar meanings. Thus, those words must be found by machines. Words that frequently occur in the test data but are absent from the training data are used to train an NLP model. As a result, conclusions drawn from test data might not be accurate. 

Machines must be able to comprehend the semantic meaning of words to tackle this issue. The model can interpret unknown words that show up in test data by using the semantic meaning of words it already knows as a base.

3. Dealing with Spelling Mistakes

Spelling errors are yet another frequent NLP issue. They may make it difficult for the system to comprehend words correctly, which may cause it to miss crucial information from the text.

Numerous factors, such as typos, excessive spaces between letters, or missing letters, can result in spelling errors. When a spelling error is found, one technique used to determine the proper word is Cosine Similarity.

4. Real-time Data

The speed at which datasets are growing is unsustainable. Fresh data is created every second and existing data is updated instantly. Retraining models repeatedly from scratch for fresh data is challenging. The method known as Transfer Learning saves the day.

Data has become the new oil. Every day it brings with it new opportunities and challenges. Companies, both big and small, are working hard to develop platforms and applications that can comprehend natural language in the same way that people can. These kinds of tactics are part of the basis for the day when we will just talk to all of our devices and tell them what to do.

Data Labeler: Your Companion in Overcoming NLP Labeling Challenges

Data Labeler may be your best ally in overcoming the complexities of NLP Data Labeling if you’re having trouble. Working with Data Labeler gives you the benefit of scale and speed as well as a team that is knowledgeable about the particular difficulties posed by Natural Language Processing. 

Hence, this is the end of your search for the ideal annotated datasets for your advanced NLP models.

For further queries, contact us or request a demo.