8 NLP Techniques to Extract Information

There is a lot of data in the world that needs to be collected, studied, and organized on a daily basis. It is not possible to do that manually as it would be extremely hectic and time-consuming. 

 

So, organizations take the help of technology for information extraction. In this article, we learn about the techniques of machine learning and natural language processing that helps in doing so.

 

What is information extraction?

 

The process of sifting through unstructured data and extracting vital information into more editable and structured data forms is known as information extraction.

 

Working with a large volume of text data is usually stressful and time-consuming. As a result, many businesses and organizations rely on Information Extraction techniques to use clever NLP algorithms to automate manual tasks. Information extraction can save time and money by reducing human effort and making the process less error-prone and efficient.

 

Deep Learning and NLP techniques like Named Entity Recognition may be used to extract information from text input. If we're starting from scratch, though, we should evaluate the sort of data we'll be dealing with, such as bills or medical records.

 

Information Extraction System is used in a variety of NLP-based applications. For example, extracting summaries from vast collections of text like Wikipedia, conversational AI systems like chatbots, extracting stock market announcements from financial news, and so on. 

 

Indeed, current virtual assistants such as Google Assistant, Amazon's Alexa, and Apple's Siri, among others, rely on complex IE systems to extract data from massive encyclopedias.

 

How does information extraction work?

 

We must first comprehend the type of data we are dealing with to comprehend the mechanics of Information Extraction NLP techniques. This will assist us in separating the information we need from the unstructured data.

 

Despite the abundance of textual data, the entanglement of natural language makes extracting usable information from it extremely challenging. Regardless of how difficult the Information Extraction process is, practically all IE systems have a pipeline with certain similar phases.

 

Related read: Top 6 Machine Learning Techniques

 

Techniques used in information extraction

 

Let's take a look at some of the most common information extraction strategies.

 

Many natural language processing techniques are used for extracting information. Following are some of them:

 

1. Text Summarization:

 

As the name implies, NLP approaches may be used to summarise vast amounts of text. The text summary is most commonly employed in news stories and academic papers.

 

Text summarization can be done in two ways:

 

• Extraction: Extraction techniques extract elements of the text to provide a summary.

• Abstraction: Abstraction approaches provide a summary by producing new text that expresses the essence of the original content.

 

For text summarization, several methods such as LexRank, TextRank, and Latent Semantic Analysis can be utilized. To use LexRank as an example, this algorithm ranks phrases based on their similarity. When a sentence is similar to many sentences, and these sentences are similar to other sentences, it is given a higher ranking.

 

2. Tokenization:

 

If you don't know what tokenization is, computers won't be able to comprehend or interact with us. As a result, we split the language down into tokens, which are effectively words and phrases, and then feed them into the software. Tokenization is the process of breaking down language into tokens.

 

Let us consider this fragment of a sentence, “NLP information extraction is fun”. This sentence can be tokenized in the following ways, as per nanonets:

 

• One-word (sometimes called unigram token): NLP, information, extraction, is, fun.

• Two-word phrase (bigram tokens): NLP information, information extraction, extraction is, is fun, fun NLP.

• Three-word sentence (trigram tokens): NLP information extraction, information extraction is, extraction is fun.

 

3. Named Entity Recognition:

 

Extracting the entities in the text is the most fundamental and useful approach in NLP. It emphasizes the text's most important ideas and references.

 

It's one of the most time-consuming data preparation tasks. It entails identifying important information in a text and categorizing it into a set of predetermined categories. Named entity recognition (NER) extracts entities from text such as individuals, places, organizations, dates, and so on.

 

Grammar rules and supervised models are commonly used in NER. There are, however, NER systems with pre-trained and built-in NER models, such as open NLP.

 

4. Parts of Speech Tagging:

 

When it comes to extracting information from text, tagging sections of speech is critical. It will assist us in comprehending the context of the text data. Text from documents is sometimes referred to as "unstructured data," or data with no specified structure or form. 

 

As a result, we may employ POS tagging techniques to offer the context of words or tokens that are used to categorize them in certain ways.

 

All tokens in text data are classified into distinct word categories, such as nouns, verbs, adjectives, prepositions, determiners, and so on, in parts of speech tagging. 

 

This extra information associated with words allows for additional processing and analysis, such as sentiment analytics, lemmatization, or any report that allows us to examine a specific class of words in greater detail.
 

 

5. Sentiment Analysis:

 

Sentiment analysis is the most extensively used NLP approach. Sentiment analysis is especially beneficial in situations where individuals express their ideas and feedback, such as customer surveys, reviews, and social media comments.

 

The most basic sentiment analysis result is a three-point scale: positive, negative, and neutral. In more complicated circumstances, the result might be a numeric score that can be categorized into as many categories as needed.

 

Both supervised and unsupervised algorithms can be used for sentiment analysis. The Naive Bayes model is the most often used supervised model for sentiment analysis. It requires a sentiment-labelled training corpus, which is used to train a model, which is then used to identify the sentiment. 

 

Different machine learning approaches such as random forest or gradient boosting can also be used instead of Naive Bayes.

 

6. Dependency graphs:

 

A dependency graph is a data structure made up of directed graphs that represents how one thing in a system interacts with other elements in the same system. A dependency graph's underlying structure is a directed graph, in which each node points to the node on whom it depends.

 

Using directed graphs, dependency graphs allow us to uncover links between neighbouring words. This link will provide you with information about the dependence type (e.g. Subject, Object, etc.).

 

A dependency network of a brief phrase is depicted in the diagram below. The arrow pointing from the term faster indicates that faster modifies going, and the label 'advmod' attached to the arrow specifies the dependency's exact nature.

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Source: www.nanonets.com

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7. Topic Modelling:

 

Topic modelling, according to Aureus Analytics, is one of the most difficult ways for identifying natural subjects in text. Topic modelling has the benefit of being an unsupervised method. Model training and a labelled training dataset are not necessary.

 

Topic modelling may be accomplished using a variety of approaches, including:

 

• Latent Semantic Analysis (LSA)

• Latent Semantic Analysis with Probabilistic Constraints (PLSA)

• Correlated Topic Model with Latent Dirichlet Allocation (LDA) (CTM).

 

Latent Dirichlet allocation is one of the most common approaches. The foundation of LDA is that each text document is made up of numerous subjects, each of which is made up of several words. LDA just requires text documents and the predicted number of topics as input.

 

8. Aspect Mining:

 

Aspect mining is a technique for identifying the many features of a text. It pulls comprehensive information from the text when used in combination with sentiment analysis. Part-of-speech tagging is one of the simplest ways of aspect mining.

 

Conclusion

 

There is no such thing as one optimum approach to complete a task in the realm of Natural Language Processing and Machine Learning, and this is true for Information Extraction tasks as well. We taught about information extraction approaches from text data using several NLP-based methodologies in this course. 

Once the information has been retrieved from unstructured text using these approaches, it may be ingested directly or utilized to improve the accuracy and performance of clustering exercises and machine learning models.