A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis IEEE Journals & Magazine

challenges in natural language processing

Luong et al. [70] used neural machine translation on the WMT14 dataset and performed translation of English text to French text. The model demonstrated a significant improvement of up to 2.8 bi-lingual evaluation understudy (BLEU) scores compared to various neural machine translation systems. Event discovery in social media feeds (Benson et al.,2011) [13], using a graphical model to analyze any social media feeds to determine whether it contains the name of a person or name of a venue, place, time etc. Data mining challenges abound in the actual visualization of the natural language processing (NLP) output itself.

  • Now, with improvements in deep learning and machine learning methods, algorithms can effectively interpret them.
  • OCR and NLP are the technologies that can help businesses win a host of perks ranging from the elimination of manual data entry to compliance with niche-specific requirements.
  • Semantics – The branch of linguistics that looks at the meaning, logic, and relationship of and between words.
  • Even if one were to overcome all the aforementioned issues in data mining, there is still the difficulty of expressing the complex outcome in a simplified manner.
  • This AI-based chatbot holds a conversation to determine the user’s current feelings and recommends coping mechanisms.
  • In case of syntactic level ambiguity, one sentence can be parsed into multiple syntactical forms.

Amygdala has a friendly, conversational interface that allows people to track their daily emotions and habits and learn and implement concrete coping skills to manage troubling symptoms and emotions better. This AI-based chatbot holds a conversation to determine the user’s current feelings and recommends coping mechanisms. Here you can read more on

the design process for Amygdala with the use of AI Design Sprints. Part of Speech tagging (or PoS tagging) is a process that assigns parts of speech (or words) to each word in a sentence. For example, the tag “Noun” would be assigned to nouns and adjectives (e.g., “red”); “Adverb” would be applied to

adverbs or other modifiers.

More explanations about Linguistic Terms

NLP is a perfect tool to approach the volumes of precious data stored in tweets, blogs, images, videos and social media profiles. So, basically, any business that can see value in data analysis – from a short text to multiple documents that must be summarized – will find NLP useful. But the biggest limitation facing developers of natural language processing models lies in dealing with ambiguities, exceptions, and edge cases due to language complexity. Without sufficient training data on those elements, your model can quickly become ineffective. Virtual digital assistants like Siri, Alexa, and Google’s Home are familiar natural language processing applications. These platforms recognize voice commands to perform routine tasks, such as answering internet search queries and shopping online.


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. Sometimes sentences can follow all the syntactical rules but don’t make semantical sense. These help the algorithms understand the tone, purpose, and intended meaning of language.

Machine Translation

Breaking up sentences helps software parse content more easily and understand its

meaning better than if all of the information were kept. The next step in natural language processing is to split the given text into discrete tokens. These are words or other

symbols that have been separated by spaces and punctuation and form a sentence. NLP gives people a way to interface with

computer systems by allowing them to talk or write naturally without learning how programmers prefer those interactions

to be structured. For example, DEEP partners have directly supported secondary data analysis and production of Humanitarian Needs Overviews (HNO) in four countries (Afghanistan, Somalia, South Sudan, and Sudan).

challenges in natural language processing

With a shared deep network and several GPUs working together, training times can reduce by half. You’ll need to factor in time to create the product from the bottom up unless you’re leveraging pre-existing NLP technology. There have been tremendous advances in enabling computers to interpret human language using NLP in recent years.

Human Geography

Healthcare AI companies now offer custom AI solutions that can analyze clinical text, improve clinical decision support, and even provide patient care through healthcare chatbot applications. This technology is also the driving force behind building an AI assistant, which can help automate many healthcare tasks, from clinical documentation to automated medical diagnosis. However, it is important to note that NLP can also pose accessibility challenges, particularly for people with disabilities. For example, people with hearing impairments may have difficulty using speech recognition technology, while people with cognitive disabilities may find it challenging to interact with chatbots and other NLP applications.

  • NLP is used to analyze, understand, and generate natural language text and speech.
  • This blog post discussed various NLP techniques and tasks that explain how

    technology approaches language understanding and generation.

  • Besides chatbots, question and answer systems have a large array of stored knowledge and practical language understanding algorithms – rather than simply delivering ‘pre-canned’ generic solutions.
  • We have also submitted one paper in the top 20 and three in the top 30 papers cited by ACL.
  • Distributional semantics is grounded in the idea that the meaning of a word can be defined as the set of contexts in which the word tends to occur.
  • The Robot uses AI techniques to automatically analyze documents and other types of data in any business system which is subject to GDPR rules.

This trend is not slowing down, so an ability to summarize the data while keeping the meaning intact is highly required. Ambiguity is one of the major problems of natural language which occurs when one sentence can lead to different interpretations. In case of syntactic level ambiguity, one sentence can be parsed into multiple syntactical forms. Lexical level ambiguity refers to ambiguity of a single word that can have multiple assertions.

1. Domain-specific constraints for humanitarian NLP

DEEP has successfully contributed to strategic planning through the Humanitarian Programme Cycle in many contexts and in a variety of humanitarian projects and initiatives. Chatbots have previously been used to provide individuals with health-related assistance in multiple contexts20, and the Covid-19 pandemic has further accelerated the development of digital tools that can be deployed in the context of health emergencies. The use of language technology to deliver personalized support is, however, still rather sparse and unsystematic, and it is hard to assess the impact and scalability of existing applications. Humanitarian assistance can be provided in many forms and at different spatial (global and local) and temporal (before, during, and after crises) scales.

challenges in natural language processing

But soon enough, we will be able to ask our personal data chatbot about customer sentiment today, and how we feel about their brand next week; all while walking down the street. But with time the technology matures – especially the AI component –the computer will get better at “understanding” the query and start to deliver answers rather than search results. Initially, the data chatbot will probably ask the question ‘how have revenues changed over the last three-quarters? But once it learns the semantic relations and inferences of the question, it will be able to automatically perform the filtering and formulation necessary to provide an intelligible answer, rather than simply showing you data.

Techniques and methods of natural language processing

They cover a wide range of ambiguities and there is a statistical element implicit in their approach. 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. Here we have a  small metadialog.com research group in NLP who has published work on the motivations, design and evaluation of conversational agents and is part of a globally established NLP, and knowledge representation community. We’ve already started to apply Noah’s Ark’s NLP in a wide range of Huawei products and services. For example, Huawei’s mobile phone voice assistant integrates Noah’s Ark’s voice recognition and dialogue technology.

Why NLP is harder than computer vision?

NLP is language-specific, but CV is not.

Different languages have different vocabulary and grammar. It is not possible to train one ML model to fit all languages. However, computer vision is much easier. Take pedestrian detection, for example.

With a tight-knit privacy mandate as this is set, it becomes easier to employ automated data protection and security compliance. In 2016, the researchers Hovy & Spruit released a paper discussing the social and ethical implications of NLP. In it, they highlight how up until recently, it hasn’t been deemed necessary to discuss the ethical considerations of NLP; this was mainly because conducting NLP doesn’t involve human participants. However, researchers are becoming increasingly aware of the social impact the products of NLP can have on people and society as a whole. Adjectives like disappointed, wrong, incorrect, and upset would be picked up in the pre-processing stage and would let the algorithm know that the piece of language (e.g., a review) was negative.

Domain-specific knowledge

An additional set of concerns arises with respect to ethical aspects of data collection, sharing, and analysis in humanitarian contexts. Text data may contain sensitive information that can be challenging to automatically identify and remove, thus putting potentially vulnerable individuals at risk. One of the consequences of this is that organizations are often hesitant around open sourcing. This is another major obstacle to technical progress in the field, as open sourcing would allow a broader community of humanitarians and NLP experts to work on developing tools for humanitarian NLP. The development of efficient solutions for text anonymization is an active area of research that humanitarian NLP can greatly benefit from, and contribute to.

challenges in natural language processing

The goal is to guess which particular object was mentioned to correctly identify it so that other tasks like

relation extraction can use this information. The potential of remote, text-based needs assessment is especially apparent for hard-to-reach contexts (e.g., areas where transportation infrastructure has been damaged), where it is impossible to conduct structured in-person interviews. There is a tremendous amount of information stored in free text files, such as patients’ medical records. Before deep learning-based NLP models, this information was inaccessible to computer-assisted analysis and could not be analyzed in any systematic way. With NLP analysts can sift through massive amounts of free text to find relevant information.

Challenges Of Natural Language Processing Natural Language Processing Applications IT

In this research, we present Arabic-Unitex, an Arabic Language Resource, with emphasis on vowel representation and encoding. Specifically, we present two dozens of rules formalizing a detailed description of vowel omission in written text. They are typographical rules integrated into large-coverage resources for morphological annotation.

What is the most challenging task in NLP?

Understanding different meanings of the same word

One of the most important and challenging tasks in the entire NLP process is to train a machine to derive the actual meaning of words, especially when the same word can have multiple meanings within a single document.

NLP hinges on the concepts of sentimental and linguistic analysis of the language, followed by data procurement, cleansing, labeling, and training. Yet, some languages do not have a lot of usable data or historical context for the NLP solutions to work around with. Natural Language Processing (NLP) has increased significance in machine interpretation and different type of applications like discourse combination and acknowledgment, limitation multilingual data frameworks, and so forth. Arabic Named Entity Recognition, Information Retrieval, Machine Translation and Sentiment Analysis are a percentage of the Arabic apparatuses, which have indicated impressive information in knowledge and security organizations. NLP assumes a key part in the preparing stage in Sentiment Analysis, Information Extraction and Retrieval, Automatic Summarization, Question Answering, to name a few.

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Toy example of distributional semantic representations, figure and caption from Boleda and Herbelot (2016), Figure 2, (with adaptations). On the left, a toy distributional semantic lexicon, with words being represented through 2-dimensional vectors. Semantic distance between words can be computed as geometric distance between their vector representations.

challenges in natural language processing

Because NLP works at machine speed, you can use it to analyze vast amounts of written or spoken content to derive valuable insights into matters like intent, topics, and sentiments. Information in documents is usually a combination of natural language and semi-structured data in forms of tables, diagrams, symbols, and on. A human inherently reads and understands text regardless of its structure and the way it is represented. Today, computers interact with written (as well as spoken) forms of human language overcoming challenges in natural language processing easily. Although natural language processing has come far, the technology has not achieved a major impact on society. Or because there has not been enough time to refine and apply theoretical work already done?

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What are the main challenges of natural language processing?

  • Training Data. NLP is mainly about studying the language and to be proficient, it is essential to spend a substantial amount of time listening, reading, and understanding it.
  • Development Time.
  • Homonyms.
  • Misspellings.
  • False Positives.

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