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Introduction
The era of IT-technologies has begun. Most of the nowadays’ work in the field of IT requires processing tons of information. In this case, Natural Language Processing (NLP) applications would come in handy for a couple of reasons: firstly, these tools assist in writing competently and wisely. Auto-correction improves the quality of the text by checking grammar, spelling mistakes or errors in the structure of sentences: such grammar checking software detects misspelled words, replaces some words or phrases by more complicated synonyms.
The usage
NLP applications are commonly used by search engines. Let’s take Google as an example: whenever you type a word/sentence trying to search something, it always offers all the possible search terms and options. Even if you stopped typing a word halfway through, the NLP application will still complete it and also show you the relevant result. Or if you misspelled a word, Google’s integrated application will correct it and find you what you have tried to search. This is called “auto-correction”, one of the main goals of application programs like this.
The main issue
Such IT-technology is obviously useful, however, there is a major problem associated with interpreting any language in NLP: the thing is that converting English language into program code is quite a challenging task, its structures are absolutely different. The English language consists of a lot more than just a strict and structured system, unlike program code that is orderly composed. For example, we often use homonyms or sarcasm in our speech and don’t even pay attention.
A sarcasm
The last thing mentioned can become an actual obstacle, because if the computer takes that at face value, it will cause some problems, because the sarcasm is saying something with different and often opposite, not literal meaning. Also, this literary device is usually used as a form of a joke/a mockery/a satire/an ironic remark, whatever rooted with humor. Let’s take a look at the example: “I’d agree with you, but then we’d both be wrong”, in this case by stating one idea but meaning another, the sentence expresses a feeling of disappointment combined with a little humor.
A homonym
The second thing that can become a weak spot for an application is homonyms. This may happen because homonyms are the words that are spelled and pronounced absolutely the same, however, they have dissimilar meanings. Let’s examine an example: “I’d like to fish” and “I’d like to eat a fish”. As you may notice, these sentences have about the same consequent word order, nevertheless, the main word here is “fish”, which has two slightly different meanings: the first one is a verb while the second one is a noun. In case a program identifies a word wrong, the main point of the sentence will change, thereby the determining process will be ruined.
A metaphor
The last thing to mention is the most “dangerous” literary device for NLP applications, a metaphor. Why is this so dangerous? Mainly because an application could easily confuse with such a quote and the context would not assist in distinguishing the message, because usually the words surrounding the quote have almost nothing in common with it. So, metaphor is a figure of speech aimed at creating an implicit comparison without using comparative prepositions. Let’s check an example: “You are the sun in my sky”. In this sentence words “sun” in the “sky” have nothing in common with words surrounding it. An application could simply freeze at this point trying to determine the quote using context and will find it pointless. That’s why while creating such NLP programs, you will need to take that into account!
Types of NLP applications
There are several types of Natural Language Processing applications and each one of them fulfill different functions (deep learning models, count-based models, transformer-based models etc.). We are going to be taking a look at three main models. But if you need professional advice for your particular application, check these website.
Deep Structured Models
The first one is Deep Learning Models, also known as Deep Structured Models. These models are usually utilized for recognition speech, machine translation, computer vision, natural language processing, but sometimes it is also used for drug design, medical image analysis or climate science. This type of model analyzes texts by evaluating every single word, based on its context. This requires a small neural network. Although this is such an advanced study, it still has some limitations, let’s talk about them. Firstly, the possibilities of these studies for the correct determination of the exact value of the homonym are limited: sometimes it makes mistakes in understanding which version of the word has just been used, even analyzing the body of the text and the context. Secondly, this type of model is not capable of accommodating and recognizing unknown words that have not been used to train the model. It is only capable of identifying those words that were used for training this model. Thirdly, to attain an ideal result/outcome, such an application would need to examine a fair amount of data. This basically means that you would need to provide a model with text of high quality and quantity, and this is only for training. But still it can lead to an error even when working with such a high quality text: for example, sometimes it deems not really similar synonyms even based on the body of the text.
Transformer-based models
The second type of models are transformer-based ones. So what is this and how does it differ from the previous mentioned kind of model? Generally speaking, such models can analyze and comprehend text with a lot more complexity. The main thing that differs transformer-based models from deep learning ones is that the first one is capable of identifying unknown words and of recognizing the meaning of the homonym, which is quite impressive. However, to train such an application you would need literally billions of words and tons of context. High price, isn’t it? This is basically the only downside of this type of model.
Keras
Now let’s talk about Keras. This is one of the very popular deep learning frameworks, working with the NLP. This is a neural-network with some new features. It is the application that assists in working with images and text data which is necessary for programming. Also, it supports a convolutional and a recurrent neural network. This means that the program could work and analyze the visual imagery and exhibit a temporal dynamic change and behavior. Also, Keras can be exported to JavaScript for running right on the browser. You can also try to use this application to achieve the goal and get the desired result as fast as possible, the goal of Keras is to help with this.
