Regional Accents Present Challenges For Natural Language Processing.

Evolution Of Natural Language Processing

While natural language processing isnt a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms.

As a human, you may speak and write in English, Spanish or Chinese. But a computers native language known as machine code or machine language is largely incomprehensible to most people. At your devices lowest levels, communication occurs not with words but through millions of zeros and ones that produce logical actions.

Indeed, programmers used punch cards to communicate with the first computers 70 years ago. This manual and arduous process was understood by a relatively small number of people. Now you can say, Alexa, I like this song, and a device playing music in your home will lower the volume and reply, OK. Rating saved, in a humanlike voice. Then it adapts its algorithm to play that song and others like it the next time you listen to that music station.

Lets take a closer look at that interaction. Your device activated when it heard you speak, understood the unspoken intent in the comment, executed an action and provided feedback in a well-formed English sentence, all in the space of about five seconds. The complete interaction was made possible by NLP, along with other AI elements such as machine learning and deep learning.

How Computers Make Sense Of Textual Data

NLP and text analytics

Natural language processing goes hand in hand with text analytics, which counts, groups and categorizes words to extract structure and meaning from large volumes of content. Text analytics is used to explore textual content and derive new variables from raw text that may be visualized, filtered, or used as inputs to predictive models or other statistical methods.

NLP and text analytics are used together for many applications, including:

• Investigative discovery. Identify patterns and clues in emails or written reports to help detect and solve crimes.
• Subject-matter expertise. Classify content into meaningful topics so you can take action and discover trends.
• Social media analytics. Track awareness and sentiment about specific topics and identify key influencers.

Everyday NLP examples

There are many common and practical applications of NLP in our everyday lives. Beyond conversing with virtual assistants like Alexa or Siri, here are a few more examples:

The evolution of NLP toward NLU has a lot of important implications for businesses and consumers alike. Imagine the power of an algorithm that can understand the meaning and nuance of human language in many contexts, from medicine to law to the classroom. As the volumes of unstructured information continue to grow exponentially, we will benefit from computers tireless ability to help us make sense of it all.

Data Labeling Workforce Options And Challenges

Text data continues to proliferate at a staggering rate. Due to the sheer size of todays datasets, you may need advanced programming languages, such as Python and R, to derive insights from those datasets at scale.

Natural language processing with Python and R, or any other programming language, requires an enormous amount of pre-processed and annotated data. Although scale is a difficult challenge, supervised learning remains an essential part of the model development process.

The most common options for scaling data labeling for NLP follow.

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First, to provide a broad overview of how NLP technology works, well cover the basics of NLP: What is it? How does it work? Why use natural language processing in the first place?

Next, well shine a light on the techniques and use cases companies are using to apply NLP in the real world today.

Finally, well tell you what it takes to achieve high-quality outcomes, especially when youre working with a data labeling workforce. Youll find pointers for finding the right workforce for your initiatives, as well as frequently asked questionsand answers.

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Modern Approach Gets Us Closer

According to the modern approach, NLP is based on AI algorithms, conducted by an increased need to manage unstructured enterprise with structured data. We must keep in mind that any technologies treating a language as only a sequence of symbols in pattern matching or based on the distribution and frequency of keywords, to mention a few, are bound to be a long way from achieving a goal which is to process everyday language and turn spoken/written words into structured data. Any NLP algorithms that do not have an authentic comprehension of a language will always be limited in their understanding capacity.

Therefore, here is the highly sophisticated issue of cognitive computing which embraces the attempts to overcome these limits by applying semantic algorithms that are able to mimic the human ability to read and understand.

Errors In Text And Speech

Misspelled or misused words can create problems for text analysis. Autocorrect and grammar correction applications can handle common mistakes, but dont always understand the writers intention.

With spoken language, mispronunciations, different accents, stutters, etc., can be difficult for a machine to understand. However, as language databases grow and smart assistants are trained by their individual users, these issues can be minimized.

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Natural Language Processing In Ai Applications Challenges

Building machines that comprehend and react to text or voice data and answer with text or speech of their own much like humans do is the goal of Natural Language Processing. It has a wide range of practical applications, including corporate intelligence, search engines, and medical research.

Contents

• 1.6 Natural Language Processing Challenges

Natural Language Processing Techniques

Many text mining, text extraction, and NLP techniques exist to help you extract information from text written in a natural language. The most common techniques our clients use follow.

Aspect mining is identifying aspects of language present in text, such as parts-of-speech tagging.

Categorization is placing text into organized groups and labeling based on features of interest. Categorization is also known as text classification and text tagging.

Data enrichment is deriving and determining structure from text to enhance and augment data. In an information retrieval case, a form of augmentation might be expanding user queries to enhance the probability of keyword matching.

Data cleansing is establishing clarity on features of interest in the text by eliminating noise from the data. It involves multiple steps, such as tokenization, stemming, and manipulating punctuation.

Entity recognition is identifying text that represents specific entities, such as people, places, or organizations. This is also known as named entity recognition, entity chunking, and entity extraction.

Intent recognition is identifying words that signal user intent, often to determine actions to take based on users responses. This is also known as intent detection and intent classification.

Semantic analysis is analyzing context and text structure to accurately distinguish the meaning of words that have more than one definition. This is also called context analysis.

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Accent Perception In Childhood

Accent perception during childhood is less well-documented than accent perception in early infancy or in adulthood, possibly because the focus of many studies with children has been on production. Indeed, numerous questions within this topic have been studied, such as when children acquire local features of their native variety or even a new language and to what extent they manage to acquire an accent in the local variety of a region they move into . Accent production research in children suggests an outstanding ability to acquire a new accent , which very likely suggests an excellent perceptual flexibility for accent variations. Some work argues that foreign accent in caregivers is ignored . One line of research within perception has thus studied potential differences in the detection of native and foreign accents.

Nlp Challenges To Consider

Words can have different meanings. Slangs can be harder to put out contextual. And certain languages are just hard to feed in, owing to the lack of resources. Despite being one of the more sought-after technologies, NLP comes with the following rooted and implementational challenges.

• Lack of Context for Homographs, Homophones, and Homonyms

A Bat can be a sporting tool and even a tree-hanging, winged mammal. Despite the spelling being the same, they differ when meaning and context are concerned. Similarly, There and Their sound the same yet have different spellings and meanings to them.

Even humans at times find it hard to understand the subtle differences in usage. Therefore, despite NLP being considered one of the more reliable options to train machines in the language-specific domain, words with similar spellings, sounds, and pronunciations can throw the context off rather significantly.

If you think mere words can be confusing, here is an ambiguous sentence with unclear interpretations.

I snapped a kid in the mall with my camera- If the spoken to, it can be the case that the machine gets confused as to whether the kid was snapped using the camera or when the kid was snapped, he had your camera.

This form of confusion or ambiguity is quite common if you rely on non-credible NLP solutions. As far as categorization is concerned, ambiguities can be segregated as Syntactic , Lexical , and Semantic .

• Errors relevant to Speed and Text

• Lack of Usable Data

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Natural Language Processing Challanges

At the first sight the issue of language processing seems to be the one which an average user of a computer/smartphone and the like, simply takes for granted given the constantly developing technology we already have at our disposal and, at the same time, provides seemingly unlimited possibilities. However, even a casual look at the brief Wikipedia definition proves the fallacy of such thinking: Natural Language processing the interdisciplinary scientific field which joins the issues of natural intelligence and linguistics, dealing with the automatization of language analysis, understanding, translation and generating the natural language by computer.

To put it in a nutshell, the whole idea is as follows: the system generating the natural language transforms the information recorded/saved in the database of a computer into the one easily accessible and comprehensible by a man. On the other side of the stick, the natural language samples are transformed into the more formal symbols, which in turn, are easier to master for the needs of computer programs.

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The Origins Of Nlp Technology

To continue our NLP introduction we should say about the roots of NLP technology, which go back into the times of the Cold War. The first practical application of Natural Language Processing was the translation of the messages from Russian to English to understand what the commies were at. The results were lackluster, but it was a step in the right direction. It took decades before the computers became powerful enough to handle NLP operations. You may check out current business applications of NLP in our article.

For a while, the major issue with NLP applications was flexibility. Long story short: early NLP software was stiff and not very practical. There was always something sore sticking out and breaking the game because language is complex and there is much going behind the words that were beyond the algorithms reach. Because of that, the algorithms required a lot of oversight and close attention to the details.

However, with the emergence of big data and machine learning algorithms, the task of fine-tuning and training Natural Language Processing models became less of an undertaking and more of a routine job.

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How Do You Annotate A Document

The use of automated labeling tools is growing, but most companies use a blend of humans and auto-labeling tools to annotate documents for machine learning. Whether you incorporate manual or automated annotations or both, you still need a high level of accuracy.

To annotate text, annotators manually label by drawing bounding boxes around individual words and phrases and assigning labels, tags, and categories to them to let the models know what they mean. This labeled data becomes your training dataset.

To annotate audio, you might first convert it to text or directly apply labels to a spectrographic representation of the audio files in a tool like Audacity. For natural language processing with Python, code reads and displays spectrogram data along with the respective labels.

Contextual Words And Phrases And Homonyms

The same words and phrases can have different meanings according the context of a sentence and many words especially in English have the exact same pronunciation but totally different meanings.

For example:

I ran to the store because we ran out of milk.

Can I run something past you real quick?

The house is looking really run down.

These are easy for humans to understand because we read the context of the sentence and we understand all of the different definitions. And, while NLP language models may have learned all of the definitions, differentiating between them in context can present problems.

Homonyms two or more words that are pronounced the same but have different definitions can be problematic for question answering and speech-to-text applications because they arent written in text form. Usage of their and there, for example, is even a common problem for humans.

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Processing Foreign And Within

In the Introduction, we merely stated that we would use linguistic variety as an umbrella term. We viewed this umbrella as necessary for both conceptual and empirical reasons. At a conceptual level, it is impossible to draw stable, non-arbitrary boundaries between different languages different dialects of the same language and non-native, dialectal, sociolectal accents. For example, among linguists, it is often said that a language is a dialect with an army and a navy . This phrase captures the fact that variation occurs along a continuum, whereas hard boundaries are derived from political, social, and historical reasons, rather than true linguistic distance between two linguistic varieties. At an empirical level, it is not rare to find two languages that are closer to each other than two dialects of the same language. One often cited example is Dutch and German, intuitively conceived as two languages in spite of the fact that they are fairly mutually intelligible in contrast, Taishanese and Pinghua are not mutually intelligible.

How Does Nlp Work

Breaking down the elemental pieces of language

Natural language processing includes many different techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. We need a broad array of approaches because the text- and voice-based data varies widely, as do the practical applications.

Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagramed sentences in grade school, youve done these tasks manually before.

In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning.

These underlying tasks are often used in higher-level NLP capabilities, such as:

In all these cases, the overarching goal is to take raw language input and use linguistics and algorithms to transform or enrich the text in such a way that it delivers greater value.

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Major Challenges Of Natural Language Processing

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Text and email autocorrection and customer care chatbots are all examples of artificial intelligence. They all interpret, “understand,” and respond to human language, both written and spoken, using machine learning algorithms and Natural Language Processing .

NLP is a powerful tool with huge benefits, but there are still many Natural Language Processing limitations and problems:

Although Natural Language Processing and its sister discipline, Natural Language Understanding , are continuously improving their capacity to compute words and text, human language is extraordinarily complex, fluid, and inconsistent, posing severe problems that NLP has yet to overcome fully.

Contextual words and phrases, as well as homonyms

The exact words and phrases can have distinct meanings depending on the context of a statement, and any terms, particularly in English, have the same pronunciation but completely different meanings.

As an example,

We were out of milk, so I dashed to the grocery.

Can I run something by you quickly?

The house appears to be in disrepair.

Humans can grasp them because we read the statement’s context and understand the numerous definitions. While NLP language models may have learnt all the meanings, distinguishing between them in context might be difficult.

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Ambiguity