2301 08826 A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

The Technology and International Affairs Program develops insights to address the governance challenges and large-scale risks of new technologies. Our experts identify actionable best practices and incentives for industry and government leaders on artificial intelligence, cyber threats, cloud security, countering influence operations, reducing the risk of biotechnologies, and ensuring global digital inclusion. The sixth and final step to overcome Chat GPT is to be ethical and responsible in your NLP projects and applications. NLP can have a huge impact on society and individuals, both positively and negatively.

Seed-funding schemes supporting humanitarian NLP projects could be a starting point to explore the space of possibilities and develop scalable prototypes. 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. Words with more similar meanings will be closer in semantic space than words with more different meanings.

Datasets used in NLP and various approaches are presented in Section 4, and Section 5 is written on evaluation metrics and challenges involved in NLP. Natural language processing (NLP) is a rapidly evolving field at the intersection of linguistics, computer science, and artificial intelligence, which is concerned with developing methods to process and generate language at scale. Modern NLP tools have the potential to support humanitarian action at multiple stages of the humanitarian response cycle. Yet, lack of awareness of the concrete opportunities offered by state-of-the-art techniques, as well as constraints posed by resource scarcity, limit adoption of NLP tools in the humanitarian sector.

The third objective is to discuss datasets, approaches and evaluation metrics used in NLP. The relevant work done in the existing literature with their findings and some of the important applications and projects in NLP are also discussed in the paper. The last two objectives may serve as a literature survey for the readers already working in the NLP and relevant fields, and further can provide motivation to explore the fields mentioned in this paper.

Apart from finding enough raw data for training, the key challenge is to ensure accurate and extensive data annotation to make training data more reliable. Today, there is even a no-code platform that allows users to build NLP models in low-resource languages. AI innovation as it has been defined to date has tended to sideline African languages. For instance, the low-resourced state of African languages consequently leads to fewer AI products, services, and tools made for the African context. The grassroots movement is responding and seeking to counter this trend by authorizing the reproduction, reuse, and dissemination of local language data.

Development Time and Resource Requirements

This document aims to track the progress in Natural Language Processing (NLP) and give an overview

of the state-of-the-art (SOTA) across the most common NLP tasks and their corresponding datasets. Word embedding creates a global glossary for itself — focusing on unique words without taking context into consideration. With this, the model can then learn about other words that also are found frequently or close to one another in a document.

Language is not a fixed or simple system, but a dynamic and rich phenomenon that varies across cultures, contexts, and domains. For example, language can have ambiguity, irony, sarcasm, slang, idioms, metaphors, and other nuances that can be difficult for computers to interpret or generate. Language can also have different grammatical structures, vocabularies, spellings, and pronunciations across regions, dialects, and accents. To overcome this challenge, businesses need to use advanced NLP techniques that can handle the subtleties and variations of language. They also need to customize their NLP models to suit the specific languages, audiences, and purposes of their applications. For example, when a student submits a response to a question, the model can analyze the response and provide feedback customized to the student's understanding of the material.

Essentially, NLP systems attempt to analyze, and in many cases, “understand” human language. These systems are often responsible for recognizing human speech (in other words, being able to “hear” what someone is saying), understanding it (figuring out the context of what's been said), and generating a natural language response (i.e. talking back). Natural Language Processing technique is used in machine translation, healthcare, finance, customer service, sentiment analysis and extracting valuable information from the text data.

This process is crucial for any application of NLP that features voice command options. Speech recognition addresses the diversity in pronunciation, dialects, haste, slurring, loudness, tone and other factors to decipher intended message. An NLP-generated document accurately summarizes any original text that humans can’t automatically generate. Also, it can carry out repetitive tasks such as analyzing large chunks of data to improve human efficiency.

At the same time, we know that the capabilities that these benchmarks aim to test, such as general question answering are far from being solved. Data annotation broadly refers to the process of organising and annotating training data for specific NLP use cases. In-text annotation, a subset of data annotation, text data is transcribed and annotated so that ML algorithms are able to make associations between actual and intended meanings. Having made giant strides with their grassroots movement and open sharing culture, African NLP researchers are still left with the mismatch between their adopted ideology and strategy of openness on the one hand and the diverse breadth of the region’s AI community on the other. This diverse range includes, for example, African NLP researchers, data contributors who participate in and contribute to crowdfunded data projects, commercial entities, local communities who may provide context for data, and funders who facilitate the creation of datasets. It involves demands and/or pressure from many quarters to adopt a licensing regime that does not interfere with or make the commercial pipeline untenable.

Overcoming Common Challenges in Natural Language Processing

NLP is already part of everyday life for many, powering search engines, prompting chatbots for customer service with spoken commands, voice-operated GPS systems and digital assistants on smartphones. NLP also plays a growing role in enterprise solutions that help streamline and automate business operations, increase employee productivity and simplify mission-critical business processes. Social media monitoring tools can use NLP techniques to extract mentions of a brand, product, or service from social media posts. Once detected, these mentions can be analyzed for sentiment, engagement, and other metrics. This information can then inform marketing strategies or evaluate their effectiveness. One of the biggest challenges with natural processing language is inaccurate training data.

It takes the information of which words are used in a document irrespective of number of words and order. In second model, a document is generated by choosing a set of word occurrences and arranging them in any order. This model is called multi-nomial model, in addition to the Multi-variate Bernoulli model, it also captures information on how many times a word is used in a document. Most text categorization approaches to anti-spam Email filtering have used multi variate Bernoulli model (Androutsopoulos et al., 2000) [5] [15]. Emotion detection investigates and identifies the types of emotion from speech, facial expressions, gestures, and text. Sharma (2016) [124] analyzed the conversations in Hinglish means mix of English and Hindi languages and identified the usage patterns of PoS.

The authors implemented a probabilistic model of word segmentation using dictionaries. Abbreviations are common in clinical text in many languages and require term identification and normalization strategies. These have been studied for Spanish [34], Swedish [35], German [27, 36] and Japanese [37]. More complex semantic parsing tasks have been addressed in Finnish [38] through the addition of a PropBank layer [39] to clinical Finnish text parsed by a dependency parser [40]. Conducting a comprehensive survey of clinical NLP work for languages other than English is not a straightforward task because relevant studies are scattered across the literature of multiple fields, including medical informatics, NLP and computer science. In addition, the language addressed in these studies is not always listed in the title or abstract of articles, making it difficult to build search queries with high sensitivity and specificity.

By tackling these challenges with innovative solutions and continuous research, NLP will become even more integral to how we interact with technology, making our interactions more natural and understanding. Human-AI CollaborationThe blend of human intuition and AI’s analytical power is potent. Human oversight is essential in training models, correcting errors, and providing nuanced understanding that current AI models may overlook.

2. Typical NLP tasks

LUNAR (Woods,1978) [152] and Winograd SHRDLU were natural successors of these systems, but they were seen as stepped-up sophistication, in terms of their linguistic and their task processing capabilities. There was a widespread belief that progress could only be made on the two sides, one is ARPA Speech Understanding Research (SUR) project (Lea, 1980) and other in some major system developments projects building database front ends. The front-end projects (Hendrix et al., 1978) [55] were intended to go beyond LUNAR in interfacing the large databases. In early 1980s computational grammar theory became a very active area of research linked with logics for meaning and knowledge’s ability to deal with the user’s beliefs and intentions and with functions like emphasis and themes. One of the main challenges of NLP for AI is the quality and quantity of the data used to train and test the models. NLP models rely on large amounts of data to learn the patterns and rules of language, but not all data is equally useful or reliable.

Natural language processing (NLP) is a subfield of artificial intelligence devoted to understanding and generation of language. The recent advances in NLP technologies are enabling rapid analysis of vast amounts of text, thereby creating opportunities for health research and evidence-informed decision making. NLP is emerging as an important tool that can assist public health authorities in decreasing the burden of health inequality/inequity in the population. The purpose of this paper is to provide some notable examples of both the potential https://chat.openai.com/ applications and challenges of NLP use in public health. Natural language processing (NLP) is a field at the intersection of linguistics, computer science, and artificial intelligence concerned with developing computational techniques to process and analyze text and speech. Owing to the increased availability of large-scale text data (e.g., social media data, news archives, web content) and to advances in computing infrastructure, NLP has witnessed rapid and dramatic developments over the past few years (Ruder, 2018b; Min et al., 2021).

Corpus and terminology development are a key area of research for languages other than English as these resources are crucial to make headway in clinical NLP. The UMLS (Unified Medical Language System [137]) aggregates more than 100 biomedical terminologies and ontologies. In its 2016AA release, the UMLS Metathesaurus comprises 9.1 million terms in English followed by 1.3 million terms in Spanish. For all other languages, such as Japanese, Dutch or French, the number of terms amounts to less than 5% of what is available for English.

Symbol representations are easy to interpret and manipulate and, on the other hand, vector representations are robust to ambiguity and noise. How to combine symbol data and vector data and how to leverage the strengths of both data types remain an open question for natural language processing. Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN).

Integrating NLP with Existing IT Infrastructure

The Centre d’Informatique Hospitaliere of the Hopital Cantonal de Geneve is working on an electronic archiving environment with NLP features [81, 119]. At later stage the LSP-MLP has been adapted for French [10, 72, 94, 113], and finally, a proper NLP system called RECIT [9, 11, 17, 106] has been developed using a method called Proximity Processing [88]. It’s task was to implement a robust and multilingual system able to analyze/comprehend medical sentences, and to preserve a knowledge of free text into a language independent knowledge representation [107, 108]. The Columbia university of New York has developed an NLP system called MEDLEE (MEDical Language Extraction and Encoding System) that identifies clinical information in narrative reports and transforms the textual information into structured representation [45]. Overload of information is the real thing in this digital age, and already our reach and access to knowledge and information exceeds our capacity to understand it.

NLP has many applications for businesses, such as chatbots, sentiment analysis, text summarization, and voice assistants. However, NLP also faces many challenges and limitations that can affect its performance and accuracy. In this article, we will explore some of the common challenges of NLP for AI and how businesses can overcome them. While these models can offer valuable support and personalized learning experiences, students must be careful to not rely too heavily on the system at the expense of developing their own analytical and critical thinking skills.

Datasets on humanitarian crises are often hard to find, incomplete, and loosely standardized. Even when high-quality data are available, they cover relatively short time spans, which makes it extremely challenging to develop robust forecasting tools. First, we provide a short primer to NLP (Section 2), and introduce foundational principles and defining features of the humanitarian world (Section 3).

In other cases, full resource suites including terminologies, NLP modules, and corpora have been developed, such as for Greek [52] and German [53]. This section reviews the topics covered by recently published research on clinical NLP which addresses languages other than English. Table 2 presents a classification of the studies cross-referenced by NLP method and language. As described below, our selection of studies reviewed herein extends to articles not retrieved by the query.

Access and availability of appropriately annotated datasets (to make effective use of supervised or semi-supervised learning) are fundamental for training and implementing robust NLP models. While the number of freely accessible biomedical datasets and pre-trained models has been increasing in recent years, the availability of those dealing with public health concepts remains limited (73). The first step to overcome NLP challenges is to understand your data and its characteristics. Answering these questions will help you choose the appropriate data preprocessing, cleaning, and analysis techniques, as well as the suitable NLP models and tools for your project.

Given these objectives, the seemingly available choices of open licensing regimes for the community of AI researchers become quite narrow. This community tends to focus on licensing regimes that allow free distribution, the making of derivative works (meaning reuse in the same or different environments), and attribution. There is a growing interest in deploying artificial intelligence (AI) strategies to achieve public health outcomes, particularly in response to the global coronavirus disease 2019 (COVID-19) pandemic where novel datasets, surveillance tools and models are emerging very quickly. When labeling data for NLP, it is essential to establish clear guidelines for Data Labelersincluding the application of Named Entity Recognition (NER) in various projects. These guidelines should cover the various aspects to be annotated, such as named entities, relationships, sentiments, etc., and explain how to effectively integrate NER into the user's application.

Access to data on African languages has proven difficult over the years, leading to the birth of these African NLP communities highlighted above. However, using data scarcity as the major reason to adopt existing forms of openness could have unintended consequences by solving one problem and leaving other problems unsolved. In this case, the inventor’s paradox means that it would be better to solve the whole problem rather than to just solve the smaller issue of data access. Essentially, addressing the full language development problem might prove easier in the long run than just the narrow one of data access.

This is clearly an advantage compared to the traditional approach of statistical machine translation, in which feature engineering is crucial. Many words and phrases in English (and other languages) have multiple meanings, and the intended meaning can only be determined based on the context. For example, the word “bank” can refer to a financial institution, the side of a river, or a turn in aviation. Determining the correct meaning in a given context is a significant challenge for NLP models. Despite the significant advancements in this field, there are still numerous challenges that researchers and practitioners face when working with NLP.

Pretrained MLLMs have been successfully used to transfer NLP capabilities to low-resource languages. As a result, there is increasing focus on zero-shot transfer learning approaches to building bigger MLLMs that cover more languages, and on creating benchmarks to understand and evaluate the performance of these models on a wider variety of tasks. NLP has many applications in everyday life, including voice assistants, machine translation systems, chatbots, information retrieval, social network analysis and automatic document classification. A concrete example of a project carried out with the help ofInnovatiana involved the labeling of thousands of real estate ads to train an NLP model.

Text Analysis with Machine Learning

Whether it’s the text-to-speech option that blew our minds in the early 2000s or the GPT models that could seamlessly pass Turing Tests, NLP has been the underlying technology that has been enabling the evolution of computers. By now, Natural Language Processing is a huge part of our life and reality and there’s no way this will change. For now there are still challenges to overcome, but the benefits of NLP in spite of these cannot be denied. In order for a computer to fully understand the different meanings in different contexts, sophisticated algorithms need to be enabled. Whether you’re looking to extract insights from unstructured data, automate repetitive tasks, or enhance communication with your audience, Jellyfish Technologies is here to help. Partner with us to leverage the full capabilities of NLP and embark on a journey of innovation and growth in the digital age.

All rights are reserved, including those for text and data mining, AI training, and similar technologies. Hands-on training and regular review sessions can help improve the consistency and quality of annotations. Fan et al. [41] introduced a gradient-based neural architecture search algorithm that automatically finds architecture with better performance than a transformer, conventional NMT models.

Generating Human-like Text

[47] In order to observe the word arrangement in forward and backward direction, bi-directional LSTM is explored by researchers [59]. In case of machine translation, encoder-decoder architecture is used where dimensionality of input and output vector is not known. Neural networks can be used to anticipate a state that has not yet been seen, such as future states for which predictors exist whereas HMM predicts hidden states. Over the past few years, NLP has witnessed tremendous progress, with the advent of deep learning models for text and audio (LeCun et al., 2015; Ruder, 2018b; Young et al., 2018) inducing a veritable paradigm shift in the field4. Central to these recent advancements is the transformer architecture (Vaswani et al., 2017), which makes it possible to learn highly contextualized and semantically rich representations of language elements at the level of both individual words and text sequences.

In the second example, ‘How’ has little to no value and it understands that the user’s need to make changes to their account is the essence of the question. When a customer asks for several things at the same time, such as different products, boost.ai’s conversational AI can easily distinguish between the multiple variables. The dreaded response that usually kills any joy when talking to any form of digital customer interaction. This is what Explainable NLP will be all about, further ensuring accountability and fostering trust around AI solutions and developing a transparent ecosystem of AI fraternity.

All these forms the situation, while selecting subset of propositions that speaker has. Cross-lingual Transfer LearningThis approach leverages knowledge from one language to help understand another, which is particularly beneficial for languages with limited data. It’s a bridge allowing NLP systems to effectively support a broader array of languages. Identifying and categorizing named entities in text, such as names, locations, or organizations, is a challenging task.

With deep learning, the representations of data in different forms, such as text and image, can all be learned as real-valued vectors. For example, in image retrieval, it becomes feasible to match the query (text) against nlp challenges images and find the most relevant images, because all of them are represented as vectors. Deep learning certainly has advantages and challenges when applied to natural language processing, as summarized in Table 3.

• Early benchmarks for automatic speech recognition (ASR) such as TIMIT and Switchboard were funded by DARPA and coordinated by NIST starting in 1986.
• Our hope is that this effort will be the first in a series of clinical NLP shared tasks involving languages other than English.
• All modules take standard input, to do some annotation, and produce standard output which in turn becomes the input for the next module pipelines.
• Language can also have different grammatical structures, vocabularies, spellings, and pronunciations across regions, dialects, and accents.
• 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.

The first objective of this paper is to give insights of the various important terminologies of NLP and NLG. At Jellyfish Technologies, we recognize the immense potential of NLP and are committed to providing cutting-edge solutions that harness the power of language processing. Our Natural Language Processing services encompass a wide range of applications, including sentiment analysis, machine translation, chatbots, information extraction, and more. With our expertise in machine learning, NLP technologies, and AI development services, we empower businesses to unlock new possibilities, streamline operations, and deliver exceptional experiences to their customers.

That’s because human language is inherently complex as it makes "infinite use of finite means" by enabling the generation of an infinite number of possibilities from a finite set of building blocks. You can foun additiona information about ai customer service and artificial intelligence and NLP. The prevalent shape of syntax of every language is the result of communicative needs and evolutionary processes that have developed over thousands of years. As a result, NLP development is a complex and time-consuming process that requires evaluating billions of data points in order to adequately train AI from scratch. Currently, there are several annotation and classification tools for managing NLP training data at scale.

Communities of AI researchers with limited access to financial resources face inherent challenges in generating the data necessary for AI development. This data scarcity particularly impacts linguistic diversity, as the effects of colonialism and global power structures often sideline under-resourced languages even when they have millions of speakers. For example, most chatbots are built on high-resourced languages such as English because of the availability of data in those languages, sidelining access for people who can only speak, read, or write in an African language. To overcome this divide, grassroots NLP collectives leverage collaborative social and human capital rather than financial means.

According to Spring wise, Waverly Labs’ Pilot can already transliterate five spoken languages, English, French, Italian, Portuguese, and Spanish, and seven written affixed languages, German, Hindi, Russian, Japanese, Arabic, Korean and Mandarin Chinese. The Pilot earpiece is connected via Bluetooth to the Pilot speech translation app, which uses speech recognition, machine translation and machine learning and speech synthesis technology. Simultaneously, the user will hear the translated version of the speech on the second earpiece. Moreover, it is not necessary that conversation would be taking place between two people; only the users can join in and discuss as a group. As if now the user may experience a few second lag interpolated the speech and translation, which Waverly Labs pursue to reduce.

Nevertheless, there is increasing pressure toward developing robust and strongly evidence-based needs assessment procedures. Anticipatory action is also becoming central to the debate on needs assessment methodologies, and the use of predictive modeling to support planning and anticipatory response is gaining traction. Clinical NLP in any language relies on methods and resources available for general NLP in that language, as well as resources that are specific to the biomedical or clinical domain. Heideltime is a rule-based system developed for multiple languages to extract time expressions [111].

Santoro et al. [118] introduced a rational recurrent neural network with the capacity to learn on classifying the information and perform complex reasoning based on the interactions between compartmentalized information. Finally, the model was tested for language modeling on three different datasets (GigaWord, Project Gutenberg, and WikiText-103). Further, they mapped the performance of their model to traditional approaches for dealing with relational reasoning on compartmentalized information. Since simple tokens may not represent the actual meaning of the text, it is advisable to use phrases such as “North Africa” as a single word instead of ‘North’ and ‘Africa’ separate words. Chunking known as “Shadow Parsing” labels parts of sentences with syntactic correlated keywords like Noun Phrase (NP) and Verb Phrase (VP).

This process, known as feature extraction, can involve techniques like bag of words (representing text as a vector of word frequencies) or word embeddings (representing words as high-dimensional vectors that capture their semantic meaning). Both text preprocessing and feature extraction are challenging tasks that require careful consideration of the specific requirements of the NLP task at hand. Current NLP models are mostly based on recurrent neural networks (RNNs) that cannot represent longer contexts. However, there is a lot of focus on graph-inspired RNNs as it emerges that a graph structure may serve as the best representation of NLP data. Research at the intersection of DL, graphs and NLP is driving the development of graph neural networks (GNNs). Today, GNNs have been applied successfully to a variety of NLP tasks, from classification tasks such as sentence classification, semantic role labelling and relation extraction, to generation tasks like machine translation, question generation, and summarisation.

These approaches recognize that words exist in context (e.g. the meanings of “patient,” “shot” and “virus” vary depending on context) and treat them as points in a conceptual space rather than isolated entities. The performance of the models has also been improved by the advent of transfer learning, that is, taking a model trained to perform one task and using it as the starting model for training on a related task. Hardware advancements and increases in freely available annotated datasets have also boosted the performance of NLP models. New evaluation tools and benchmarks, such as GLUE, superglue and BioASQ, are helping to broaden our understanding of the type and scope of information these new models can capture (19–21). Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc.

The rapid increase in model performance in recent years has catapulted us from the decade-long to the near-term regime for many applications. However, even in this more application-oriented setting we are still relying on the same metrics that we have used to measure long-term research progress thus far. As models become stronger, metrics like BLEU are no longer able to accurately identify and compare the best-performing models. There is a large difference between metrics designed for decades-long research and metrics designed for near-term development of practical applications, as highlighted by Mark Liberman. For developing decade-scale technology, we need efficient metrics that can be crude as long as they point in the general direction of our distant goal. Examples of such metrics are the word error rate in ASR (which assumes that all words are equally important) and BLEU in machine translation (which assumes that word order is not important).

First, high-performing NLP methods for unstructured text analysis are relatively new and rapidly evolving (Min et al., 2021), and their potential may not be entirely known to humanitarians. Secondly, the humanitarian sector still lacks the kind of large-scale text datasets and data standards required to develop robust domain-specific NLP tools. Data scarcity becomes an especially salient issue when considering that humanitarian crises often affect populations speaking low-resource languages (Joshi et al., 2020), for which little if any data is digitally available. Thirdly, it is widely known that publicly available NLP models can absorb and reproduce multiple forms of biases (e.g., racial or gender biases Bolukbasi et al., 2016; Davidson et al., 2019; Bender et al., 2021).