Why nlp is difficult

Most NLP techniques rely on machine learning to derive meaning from human languages. In fact, a typical interaction between humans and machines using Natural Language Processing could go as follows:.

A human talks to the machine. The machine captures the audio. Audio to text conversion takes place. Data to audio conversion takes place. The machine responds to the human by playing the audio file. Natural Language Processing is the driving force behind the following common applications:.

Natural Language processing is considered a difficult problem in computer science. The rules that dictate the passing of information using natural languages are not easy for computers to understand. Some of these rules can be high-leveled and abstract; for example, when someone uses a sarcastic remark to pass information.

Comprehensively understanding the human language requires understanding both the words and how the concepts are connected to deliver the intended message. While humans can easily master a language, the ambiguity and imprecise characteristics of the natural languages are what make NLP difficult for machines to implement. NLP entails applying algorithms to identify and extract the natural language rules such that the unstructured language data is converted into a form that computers can understand.

When the text has been provided, the computer will utilize algorithms to extract meaning associated with every sentence and collect the essential data from them. Sometimes, the computer may fail to understand the meaning of a sentence well, leading to obscure results.

For example, a humorous incident occurred in the s during the translation of some words between the English and the Russian languages. Here is the biblical sentence that required translation:. Different businesses and industries often use very different language. An NLP processing model needed for healthcare, for example, would be very different than one used to process legal documents.

These days, however, there are a number of analysis tools trained for specific fields, but extremely niche industries may need to build or train their own models. AI machine learning NLP applications have been largely built for the most common, widely used languages.

However, many languages, especially those spoken by people with less access to technology often go overlooked and under processed. For example, by some estimations, depending on language vs. Machine learning requires A LOT of data to function to its outer limits — billions of pieces of training data. The more data NLP models are trained on, the smarter they become. That said, data and human language! All of the problems above will require more research and new techniques in order to improve on them.

Advanced practices like artificial neural networks and deep learning allow a multitude of NLP techniques, algorithms, and models to work progressively, much like the human mind does.

As they grow and strengthen, we may have solutions to some of these challenges in the near future. SaaS text analysis platforms, like MonkeyLearn , allow users to train their own machine learning NLP models, often in just a few steps, which can greatly ease many of the NLP processing limitations above.

While Natural Language Processing has its limitations, it still offers huge and wide-ranging benefits to any business. And with new techniques and new technology cropping up every day, many of these barriers will be broken through in the coming years. NLP machine learning can be put to work to analyze massive amounts of text in real time for previously unattainable insights.

Want to give NLP text analysis a try? NLP has heavily benefited from recent advances in machine learning, especially from deep learning techniques. The field is divided into the three parts:. Human language is special for several reasons.

It is a complex system, although little children can learn it pretty quickly. Another remarkable thing about human language is that it is all about symbols. According to Chris Manning, a machine learning professor at Stanford, it is a discrete, symbolic, categorical signaling system. This means we can convey the same meaning in different ways i. The encoding by the human brain is a continuous pattern of activation by which the symbols are transmitted via continuous signals of sound and vision.

Understanding human language is considered a difficult task due to its complexity. For example, there is an infinite number of different ways to arrange words in a sentence.

Also, words can have several meanings and contextual information is necessary to correctly interpret sentences. Every language is more or less unique and ambiguous. Note that a perfect understanding of language by a computer would result in an AI that can process the whole information that is available on the internet, which in turn would probably result in artificial general intelligence.

Syntactic analysis syntax and semantic analysis semantic are the two primary techniques that lead to the understanding of natural language. Language is a set of valid sentences, but what makes a sentence valid? Syntax and semantics. Syntax is the grammatical structure of the text, whereas semantics is the meaning being conveyed. A sentence that is syntactically correct, however, is not always semantically correct.

Syntactic analysis, also referred to as syntax analysis or parsing, is the process of analyzing natural language with the rules of a formal grammar.

Grammatical rules are applied to categories and groups of words, not individual words. Syntactic analysis basically assigns a semantic structure to text.

For example, a sentence includes a subject and a predicate where the subject is a noun phrase and the predicate is a verb phrase.

Again, it's important to reiterate that a sentence can be syntactically correct but not make sense. The way we understand what someone has said is an unconscious process relying on our intuition and knowledge about language itself. In other words, the way we understand language is heavily based on meaning and context. Computers need a different approach, however. Semantic analysis is the process of understanding the meaning and interpretation of words, signs and sentence structure.

This lets computers partly understand natural language the way humans do. I say partly because semantic analysis is one of the toughest parts of NLP and it's not fully solved yet. Speech recognition, for example, has gotten very good and works almost flawlessly, but we still lack this kind of proficiency in natural language understanding.

Also, some of the technologies out there only make you think they understand the meaning of a text. Let's look at some of the most popular techniques used in natural language processing. Note how some of them are closely intertwined and only serve as subtasks for solving larger problems. What is parsing? That actually nailed it but it could be a little more comprehensive. Parsing refers to the formal analysis of a sentence by a computer into its constituents, which results in a parse tree showing their syntactic relation to one another in visual form, which can be used for further processing and understanding.

Below is a parse tree for the sentence "The thief robbed the apartment. The letters directly above the single words show the parts of speech for each word noun, verb and determiner. One level higher is some hierarchical grouping of words into phrases.

For example, "the thief" is a noun phrase, "robbed the apartment" is a verb phrase and when put together the two phrases form a sentence, which is marked one level higher. But what is actually meant by a noun or verb phrase? Noun phrases are one or more words that contain a noun and maybe some descriptors, verbs or adverbs.