5 Use Cases of Semantic Analysis in Natural Language Processing


5 Use Cases of Semantic Analysis in Natural Language Processing

Using semantic analysis in natural language processing (NLP) offers many benefits. Read how we use these technologies in our products.

Applying semantic analysis in natural language processing can bring many benefits to your business, regardless of its size or industry. If you wonder if it is the right solution for you, this article may come in handy. 

In this article, you will find answers to many popular questions related to this topic, such as:

  • What is semantic analysis? 
  • What is it used for?
  • How does it work?
  • How can you use semantic analysis in your company?

Semantic analysis – what is it, and what is it used for?

Semantic analysis is the process of finding the meaning of content in natural language. This method allows artificial intelligence algorithms to understand the context and interpret the text by analysing its grammatical structure and finding relationships between individual words, regardless of language they’re written in.

The critical role here goes to the statement's context, which allows assigning the appropriate meaning to the sentence. It is particularly important in the case of homonyms, i.e. words which sound the same but have different meanings. For example, when we say "I listen to rock music" in English, we know very well that 'rock' here means a musical genre, not a mineral material.

However, this is not so obvious for computers. Therefore, they need to be taught the correct interpretation of sentences depending on the context. And that is precisely what semantic analysis is for.

What is natural language processing?

Natural language processing (NLP) is a field of artificial intelligence that focuses on creating interactions between computers and human language. It aims to facilitate communication between humans and machines by teaching computers to read, process, understand and perform actions based on natural language.

To learn more about NLP, read this article on our blog: Natural language processing – our means of work at WEBSENSA.

For now, let's try to answer the question: What is the connection between semantic analysis and NLP? Well, natural language processing is a broad field consisting of many processes. Semantic analysis is one of the critical tasks of natural language processing, responsible for the correct interpretation of a text. Other NLP tasks include language modeling, text classification, text generation, optical character recognition and many more.

The technological basis of semantic analysis

One of the simplest and most popular methods of finding meaning in text used in semantic analysis is the so-called Bag-of-Words approach. This approach ignores the order of words and sums them up in the whole text. Thanks to that, we can obtain a numerical vector, which tells us how many times a particular word has appeared in a given text.

Igor Kołakowski, Data Scientist at WEBSENSA

As Igor Kołakowski, Data Scientist at WEBSENSA points out, this representation is easily interpretable for humans. It is also accepted by classification algorithms like SVMs or random forests. Therefore, this simple approach is a good starting point when developing text analytics solutions.

Below you will find an example of how the summation of given words in a text using this method looks like:

Technologiczne podstawy analizy semantycznej – podejście Bag-of-Words

As you can see, this approach does not take into account the meaning or order of the words appearing in the text. Moreover, in the step of creating classification models, you have to specify the vocabulary that will occur in the text. This way, when new words appear in the text, they will be ignored. — Additionally, the representation of short texts in this format may be useless to classification algorithms since most of the values of the representing vector will be 0 — adds Igor Kołakowski.

So, how to improve the performance? The solution could be static word representations, generated by models such as Word2Vec created by Google in 2013, as well as subsequent iterations of this idea, which are GloVe or FastText. How does it work?

Static word representations

In these solutions, each word is assigned a specific vector representation. The assignment of meaning to terms is based on what other words usually occur in their close vicinity. To create such representations, you need many texts as training data, usually Wikipedia articles, books and websites.

Static representations allow checking the similarity of words. Based on them, the classification model can learn to generalise the classification to words that have not previously occurred in the training set.

When using static representations, words are always represented in the same way. For example, if the word "rock" appears in a sentence, it gets an identical representation, regardless of whether we mean a music genre or mineral material. The word is assigned a vector that reflects its average meaning over the training corpus.

If you want to achieve better accuracy in word representation, you can use context-sensitive solutions. Such models include BERT or GPT, which are based on the Transformer architecture.

5 examples of semantic analysis in natural language processing:

1. Translators

Innovative online translators are developed based on artificial intelligence algorithms using semantic analysis. As you know, some words in one context may have several other meanings. So understanding the entire context of an utterance is extremely important in such tools.

"Time flies like an arrow" – example of a sentence with more than one meaning

For example, the phrase "Time flies like an arrow" can have more than one meaning. If the translator does not use semantic analysis, it may not recognise the proper meaning of the sentence in the given context.

One of the most advanced translators on the market using semantic analysis is DeepL Translator, a machine translation system created by the German company DeepL.

2. Content management tools

An interesting example of such tools is Content Moderation Platform created by WEBSENSA team. It supports moderation of users' comments published on the Polish news portal called Wirtualna Polska. This tool analyses the comments to precisely classify users' statements. In particular, it aims at finding comments containing offensive words and hate speech.

Thanks to the fact that the system can learn the context and sense of the message, it can determine whether a given comment is appropriate for publication. This tool has significantly supported human efforts to fight against hate speech on the Internet.

3. Media Monitoring Tools

Semantic analytics is critical in marketing, where it is crucial to take care of a company's image and consumers' opinions. Media monitoring providers invest in advanced AI solutions and use sentiment analysis algorithms to analyse text and identify negative and positive words. Then, their software tags them according to one group: positive, negative or neutral. 

However, the challenge is to understand the entire context of a statement to categorise it properly. For example, we are looking for mentions of a painkiller. A customer writes, "This painkiller helped me cure my pain". In that case there is a risk that analysing the specific words without understanding the context may come wrong. It is possible because the terms "pain" and "killer" are likely to be classified as "negative". Semantic analysis can be beneficial here because it is based on the whole context of the statement, not just the words used.

4. Stock trading platforms

Stock trading companies scour the internet for the latest news about the market. In this case, AI algorithms based on semantic analysis can detect companies with positive reviews of articles or other mentions on the web. 

These solutions will help make better investment choices, as there is an excellent chance that people will buy more stocks of companies with good reviews. In addition, having access to such information allows organisations to make quick decisions and stay ahead of the competition.

5. Customer service

A critical and popular application of NLP is chatbots. In many companies, these automated assistants are the first source of contact with customers. The most advanced ones use semantic analysis to understand customer needs and more. 

For example, chatbots can detect callers' emotions and make real-time decisions. If the system detects that a customer's message has a negative context and could result in his loss, chatbots can connect the person to a human consultant who will help them with their problem.  

Build your own semantic analysis tool

Semantic analysis is a solution that can make your business more effective and help you understand your customers' needs and intentions. Moreover, systems using this technology can constantly learn from the provided training data, so they will still become more and more accurate.

There are many possible applications for this method, depending on the specific needs of your business. If you are looking for a dedicated solution using semantic analysis, contact us. We will be more than happy to talk about your business needs and expectations.

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