Challenges in natural language processing and natural language understanding by considering both technical and natural domains IEEE Conference Publication

Autore : Redazione
Pubblicato il : 05-06-2024

Natural language processing: state of the art, current trends and challenges Multimedia Tools and Applications

It also helps to quickly find relevant information from databases containing millions of documents in seconds. NLP systems generate concise summaries of lengthy documents or articles, helping users quickly grasp the main points without having to read the entire text. This is valuable in scenarios where time is limited or when dealing with large volumes of textual data, such as news articles, research papers, or legal documents. While Natural Language Processing (NLP) faces several challenges, researchers and practitioners have devised innovative solutions to overcome these hurdles. This may involve fine-tuning model parameters, incorporating new data, or developing more sophisticated algorithms.

In this blog, we’ll explore how NLP works, its benefits, the challenges NLP faces, and its real-world applications. In the United States, most people speak English, but if you’re thinking of reaching an international and/or multicultural audience, you’ll need to provide support for multiple languages. However, new techniques, like multilingual transformers (using Google’s BERT “Bidirectional Encoder Representations from Transformers”) and multilingual sentence embeddings aim to identify and leverage universal similarities that exist between languages. This is where training and regularly updating custom models can be helpful, although it oftentimes requires quite a lot of data.

Others such as grassroots groups may be concerned about ensuring that local communities benefit as directly as possible from linguistic and other assets developed from data about these communities. Strict proprietary limitations can severely curb the progress of these stakeholder groups in terms of the Global South inclusion project. There is an urgent need to align priorities among creators seeking reasonable returns and communities enabling access, so that more people can equitably build technologies preserving the intangible cultural heritages tied to certain languages. With compromises valuing both open innovation and systems to recognize and reward the contributions of local communities, it is possible to formulate policies nurturing advancement rather than slowing progress through legal constraints. Ultimately, ethical frameworks should promote using language technology in ways that put the public good above profits.

Experiences such as DEEP, Masakhané, and HuggingFace’s BigScience project32 have provided evidence of the large potential of approaches that emphasize team diversity as a foundational factor, and welcome contributors from diverse cultural and professional backgrounds. Importantly, HUMSET also provides a unique example of how qualitative insights and input from domain experts can be leveraged to collaboratively develop quantitative technical tools that can meet core needs of the humanitarian sector. As we will further stress in Section 7, this cross-functional collaboration model is central to the development of impactful NLP technology and essential to ensure widespread adoption. 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). Furthermore, the DEEP has promoted standardization and the use of the Joint Intersectoral Analysis Framework30.

However, these models are not perfect and still struggle with issues like maintaining consistency, avoiding repetition, and generating factually accurate content. Furthermore, these models require large amounts of data and computational resources to train, which can be a barrier for many organizations and researchers. This involves creating models that can write like a human, producing text that is coherent, relevant, and indistinguishable from text written by a human. This is a significant challenge, as it requires the model to understand the nuances of human language, generate creative and original content, and maintain coherence over long pieces of text. Before unstructured data can be used for NLP tasks, it needs to be preprocessed and transformed into a format that can be understood by NLP models. This process, known as text preprocessing, involves several steps such as tokenization (breaking text into individual words or tokens), stemming (reducing words to their root form), and removing stop words (common words like “and”, “the”, and “in” that do not carry much meaning).

The framework requires additional refinement and evaluation to determine its relevance and applicability across a broad audience including underserved settings. Phonology is the part of Linguistics which refers to the systematic arrangement of sound. The term phonology comes from Ancient Greek in which the term phono means voice or sound and the suffix –logy refers to word or speech. In 1993 Nikolai Trubetzkoy stated that Phonology is “the study of sound pertaining to the system of language” whereas Lass1998 [66]wrote that phonology refers broadly with the sounds of language, concerned with sub-discipline of linguistics, behavior and organization of sounds. Future advancements are expected to push the boundaries of what’s possible, especially in understanding context and adapting to new languages and dialects with unprecedented speed.

Coherence refers to the logical and semantic connection between sentences and paragraphs, while consistency refers to maintaining the same style, tone, and facts throughout the text. For example, if a model is generating a story, it needs to ensure that the characters, setting, and events are consistent throughout the story. Unstructured data can contain valuable information, but it can also contain a lot of noise – irrelevant or unnecessary information. Filtering out this noise and extracting the relevant information requires sophisticated NLP techniques.

Transfer Learning on Stack Exchange Tags

Depending on the application, an NLP could exploit and/or reinforce certain societal biases, or may provide a better experience to certain types of users over others. It’s challenging to make a system that works equally well in all situations, with all people. Autocorrect and grammar correction applications can handle common mistakes, but don’t always understand the writer’s intention. Even for humans this sentence alone is difficult to interpret without the context of surrounding text. POS (part of speech) tagging is one NLP solution that can help solve the problem, somewhat.

Over the past few years, UN OCHA’s Centre for Humanitarian Data7 has had a central role in promoting progress in this domain. We produce language for a significant portion of our daily lives, in written, spoken or signed form, in natively digital or digitizable formats, and for goals that range from persuading others, to communicating and coordinating our behavior. The field of NLP is concerned with developing techniques that make it possible for machines to represent, understand, process, and produce language using computers. Being able to efficiently represent language in computational formats makes it possible to automate traditionally analog tasks like extracting insights from large volumes of text, thereby scaling and expanding human abilities.

For instance, the EfficientQA competition (Min et al., 2020) at NeurIPS 2020 demonstrated the benefits of retrieval augmentation and large collections of weakly supervised question–answer pairs (Lewis et al., 2021). NLP technology is increasingly used in many real-world application areas, from creative self-expression to fraud detection and recommendation. 6 Ghana NLP is an open-source initiative focused on NLP of Ghanaian languages and its applications to local problems.

However, several challenges still exist that researchers and practitioners are diligently working to overcome. In this blog, we will delve into the complexities of NLP and explore the key challenges it faces, along with the ongoing efforts to tackle them. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. Human language is filled with many ambiguities that make it difficult for programmers to write software that accurately determines the intended meaning of text or voice data. But then programmers must teach natural language-driven applications to recognize and understand irregularities so their applications can be accurate and useful. NLP research has enabled the era of generative AI, from the communication skills of large language models (LLMs) to the ability of image generation models to understand requests.

As NLP continues to evolve, these considerations will play a critical role in shaping the future of how machines understand and interact with human language. Limiting the negative impact of model biases and enhancing explainability is necessary to promote adoption of NLP technologies in the context of humanitarian action. Awareness of these issues is growing at a fast pace in the NLP community, and research in these domains is delivering important progress. These analytical frameworks31 are the way in which humanitarian organizations and analysts structure their thinking and make the qualitative analysis of data more systematic, and they consist of a hierarchical set of categories or labels with which to annotate and organize data.

As a result, the creation of resources such as synonym or abbreviation lexicons [27, 36, 48] receives a lot of effort, as it serves as the basis for more advanced NLP and text mining work. Language analysis has been for the most part a qualitative field that relies on human interpreters to find meaning in discourse. Powerful as it may be, it has quite a few limitations, the first of which is the fact that humans have unconscious biases that distort their understanding of the information.

Naive Bayes is a probabilistic algorithm which is based on probability theory and Bayes’ Theorem to predict the tag of a text such as news or customer review. It helps to calculate the probability of each tag for the given text and return the tag with the highest probability. Bayes’ Theorem is used to predict the probability of a feature based on prior knowledge of conditions that might be related to that feature.

Information Extraction

It is often possible to perform end-to-end training in deep learning for an application. This is because the model (deep neural network) offers rich representability and information in the data can be effectively ‘encoded’ in the model. For example, in neural machine translation, the model is completely automatically constructed from a parallel corpus and usually no human intervention is needed.

Contextual UnderstandingGrasping the context in which language is used is another steep hill for NLP. This includes understanding sarcasm, idioms, and cultural nuances, which are often second nature to humans but complex for machines. The all-new enterprise studio that brings together traditional machine learning along with new generative AI capabilities powered by foundation models. Expertly understanding language depends on the ability to distinguish the importance of different keywords in different sentences. NLP is the way forward for enterprises to better deliver products and services in the Information Age. With such prominence and benefits also arrives the demand for airtight training methodologies.

Many companies uses Natural Language Processing technique to solve their text related problems. Tools such as ChatGPT, Google Bard that trained on large corpus of test of data uses Natural Language Processing technique to solve the user queries. 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. dialect) there are over 3,000 languages in Africa, alone. Developing labeled datasets to train and benchmark models on domain-specific supervised tasks is also an essential next step.

It involves language understanding, language generation, dialogue management, knowledge base access and inference. Dialogue management can be formalized as a sequential decision process and reinforcement learning can play a critical role. Obviously, combination of deep learning and reinforcement learning could be potentially useful for the task, which is beyond deep learning itself. Today, information is being produced and published (e.g. scientific literature, technical reports, health records, social media, surveys, registries and other documents) at unprecedented rates.

False positives occur when the NLP detects a term that should be understandable but can’t be replied to properly. The goal is to create an NLP system that can identify its limitations and clear up confusion by using questions or hints. The recent proliferation of sensors and Internet-connected devices has led to an explosion in the volume and variety of data generated.

Thus, the cross-lingual framework allows for the interpretation of events, participants, locations, and time, as well as the relations between them. Output of these individual pipelines is intended to be used as input for a system that obtains event centric knowledge graphs. All modules take standard input, to do some annotation, and produce standard output which in turn becomes the input for the next module pipelines. Their pipelines are built as a data centric architecture so that modules can be adapted and replaced. Furthermore, modular architecture allows for different configurations and for dynamic distribution. Another important challenge that should be mentioned is the linguistic aspect of NLP, like Chat GPT and Google Bard.

“Integrating social media communications into the rapid assessment of sudden onset disasters,” in International Conference on Social Informatics (Barcelona), 444–461. How does one go about creating a cross-functional humanitarian NLP community, which can fruitfully engage in impact-driven collaboration and experimentation? Experiences such as Masakhané have shown that independent, community-driven, open-source projects can go a long way. For long-term sustainability, however, funding mechanisms suitable to supporting these cross-functional efforts will be needed.

Furthermore, unstructured data can come in many different formats and languages, adding another layer of complexity to the task. These are the most common challenges that are faced in NLP that can be easily resolved. The main problem with a lot of models and the output they produce is down to the data inputted. If you focus on how you can improve the quality of your data using a Data-Centric AI mindset, you will start to see the accuracy in your models output increase. Multi-task benchmarks such as GLUE have become key indicators of progress but such static benchmark collections quickly become outdated.

Words often have multiple meanings depending on context, making it challenging for NLP systems to accurately interpret and understand text. Natural Language Processing (NLP) is a powerful filed of data science with many applications from conversational agents and sentiment analysis to machine translation and extraction of information. The understanding of context enables systems to interpret user intent, conversation history tracking, and generating relevant responses based on the ongoing dialogue. Apply intent recognition algorithm to find the underlying goals and intentions expressed by users in their messages.

NLP enables computers and digital devices to recognize, understand and generate text and speech by combining computational linguistics—the rule-based modeling of human language—together with statistical modeling, machine learning (ML) and deep learning. Additionally, universities should involve students in the development and implementation of NLP models to address their unique needs and preferences. Finally, universities should invest in training their faculty to use and adapt to the technology, as well as provide resources and support for students to use the models effectively. In summary, universities should consider the opportunities and challenges of using NLP models in higher education while ensuring that they are used ethically and with a focus on enhancing student learning rather than replacing human interaction. There is rich semantic content in human language that allows speaker to convey a wide range of meaning through words and sentences. Natural Language is pragmatics which means that how language can be used in context to approach communication goals.

Natural language data is often noisy and ambiguous, containing errors, misspellings, and grammatical inconsistencies. NLP systems must be robust enough to handle such noise and uncertainty while maintaining accuracy and reliability in their outputs. In some cases, NLP tools can carry the biases of their programmers, as well as biases within the data sets used to train them.

These extracted text segments are used to allow searched over specific fields and to provide effective presentation of search results and to match references to papers. For example, noticing the pop-up ads on any websites showing the recent items you might have looked on an online store with discounts. In Information Retrieval two types of models have been used (McCallum and Nigam, 1998) [77]. But in first model a document is generated by first choosing a subset of vocabulary and then using the selected words any number of times, at least once without any order. This model is called multi-nominal model, in addition to the Multi-variate Bernoulli model, it also captures information on how many times a word is used in a document.

As the race to build larger transformer models continues, the focus will turn to cost-effective and efficient means to continuously pre-train gigantic generic language models with proprietary domain-specific data. Even though large language models and computational graphs can help address some of the data-related challenges of NLP, they will also require infrastructure on a whole new scale. Today, vendors like NVIDIA are offering fully packaged products that enable organisations with extensive NLP expertise but limited systems, HPC, or large-scale NLP workload expertise to scale-out faster. So, despite the challenges, NLP continues to expand and grow to include more and more new use cases. Dealing with large or multiple documents is another significant challenge facing NLP models.

Furthermore, there are many different languages in the world, each with their own unique grammar, vocabulary, and idioms, which adds another layer of complexity to NLP. It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging

as well as more recent ones such as reading comprehension and natural language inference. The main objective

is to provide the reader with a quick overview of benchmark datasets and the state-of-the-art for their

task of interest, which Chat GPT serves as a stepping stone for further research. To this end, if there is a

place where results for a task are already published and regularly maintained, such as a public leaderboard,

the reader will be pointed there. This is where contextual embedding comes into play and is used to learn sequence-level semantics by taking into consideration the sequence of all words in the documents. This technique can help overcome challenges within NLP and give the model a better understanding of polysemous words.

We suggest that efforts in analyzing the specificity of languages and tasks could contribute to methodological advances in adaptive methods for clinical NLP. This shows that adapting systems that work well for English to another language could be a promising path. In practice, it has been carried out with varying levels of success depending on the task, language and system design. The importance of system design was evidenced in a study attempting to adapt a rule-based de-identification method for clinical narratives in English to French [79].

2. Needs assessment and the humanitarian response cycle

Some of these tasks have direct real-world applications such as Machine translation, Named entity recognition, Optical character recognition etc. Though NLP tasks are obviously very closely interwoven but they are used frequently, for convenience. Some of the tasks such as automatic summarization, co-reference analysis etc. act as subtasks that are used in solving larger tasks. Nowadays NLP is in the talks because of various applications and recent developments although in the late 1940s the term wasn’t even in existence. So, it will be interesting to know about the history of NLP, the progress so far has been made and some of the ongoing projects by making use of NLP.

On the other hand, for more complex tasks that rely on a deeper linguistic analysis of text, adaptation is more difficult. This paper follows-up on a panel discussion at the 2014 American Medical Informatics Association (AMIA) Fall Symposium [24]. Following the definition of the International Medical Informatics Association (IMIA) Yearbook [25, 26], clinical NLP is a sub-field of NLP applied to clinical texts or aimed at a clinical outcome.

Gain a competitive edge with our on-demand solutions and accelerate your project delivery. A false positive occurs when an NLP notices a phrase that should be understandable and/or addressable, but cannot be sufficiently answered. The solution here is to develop an NLP system that can recognize its own limitations, and use questions or prompts to clear up the ambiguity. Along similar lines, you also need to think about the development time for an NLP system. While Natural Language Processing has its limitations, it still offers huge and wide-ranging benefits to any business.

Unlike structured data, which is organized in a predefined manner (like in databases or spreadsheets), unstructured data does not have a predefined format or organization. Examples of unstructured data include text documents, social media posts, and web pages. The vast majority of data available today is unstructured, and extracting meaningful information from this data is a significant challenge for NLP. Additionally, human language is constantly evolving, with new words, phrases, and uses being created all the time. This makes it difficult for NLP models to keep up with the changes and understand the latest slang or idioms.

Chat GPT by OpenAI and Bard (Google’s response to Chat GPT) are examples of NLP models that have the potential to transform higher education. These generative language models, i.e., Chat GPT and Google Bard, can generate human-like responses to open-ended prompts, such as questions, statements, or prompts related to academic material. The recent release and increasing popularity (in early 2023) of Chat GPT and Google Bard made its use particularly relevant for supporting student learning in a range of contexts, such as language learning, writing, research, and general academic inquiry. Therefore, the use of NLP models in higher education expands beyond the aforementioned examples, with new applications being developed to aid students in their academic pursuits. This is how they can come to understand and analyze text or speech inputs in real time- based on the past data they studied.

A systematic evaluation of machine translation tools showed that off-the-shelf tools were outperformed by customized systems [156]; however, this was not confirmed when using a smaller in-domain corpus [157]. Encouragingly, medical speech translation was shown to be feasible in a real clinical setting, if the system focused on narrowly-defined patient-clinician interactions [158]. When training machine learning models to interpret language from social media platforms it’s very important to understand these cultural differences. Twitter, for example, has a rather toxic reputation, and for good reason, it’s right there with Facebook as one of the most toxic places as perceived by its users. By embracing the principles of openness and transparency in sharing experiences, data, code, and resources, these communities are making remarkable strides.

However, in recent times, there have been increased efforts to amplify perspectives from underrepresented and/or unrepresented jurisdictions including ones in the Global South so they can help shape discussions about responsible AI use and development. Within this atmosphere of inclusion (referred to here as the Global South inclusion project), openness, privacy, and copyright have continued to feature as important and indispensable considerations. It is imperative that all annotators, or “Data Labelers”, consistently apply the same criteria and follow the same guidelines to ensure consistent annotations. To achieve this, the use of a detailed guide or specific glossary is strongly recommended. You can foun additiona information about ai customer service and artificial intelligence and NLP. These tools provide clear references on annotation terminology and methodology, reducing individual variations and ensuring greater data accuracy. Considering these metrics in mind, it helps to evaluate the performance of an NLP model for a particular task or a variety of tasks.

From a regulatory standpoint, copyright and privacy laws may need internal reforms, or there may be a need for a specific sui generis piece of legislation such as the European Union has undertaken with the recently passed Artificial Intelligence Act. However, of more immediate benefit, given the protracted nature of legislative reforms, is the use of contracts and private ordering regimes. The doctrine of freedom of contract means that changes and tweaks can be made in existing open licensing regimes to address relevant challenges and harness relevant opportunities. Similarly, tensions arise from the absence of a real choice of licenses to address the concerns stated above. Focusing on the commercial pipeline of these datasets, licenses that restrict commercial uses are not feasible.

Benchmarks are among the most impactful artefacts of our field and often lead to entirely new research directions, so it is crucial for them to reflect real-world and potentially ambitious use cases of our technology. For language technology and information retrieval (IR), NIST ran the DARPA-funded TREC series of workshops covering a wide array of tracks and topics, which can be seen below. TREC organised competitions built on an evaluation paradigm pioneered by Cranfield in the 1960s where models are evaluated based on a set of test collections, consisting of documents, questions, and human relevance judgements. As the variance in performance across topics is large, scores are averaged over many topics. TREC’s “standard, widely available, and carefully constructed set of data laid the groundwork for further innovation” (Varian, 2008) in IR.

And even without an API, web scraping is as old a practice as the internet itself, right?. Natural Language Processing is a field of computer science, more specifically a field of Artificial Intelligence, that is concerned with developing computers with the ability to perceive, understand and produce human language. It has been observed recently that deep learning can enhance the performances in the first four tasks and becomes the state-of-the-art technology for the tasks (e.g. [1–8]). Syntactic ambiguity is another form of ambiguity where a sentence can be interpreted in more than one way due to its structure. For example, the sentence “I saw the man with the telescope” can mean that I used a telescope to see the man, or it can mean that I saw a man who had a telescope. Resolving such ambiguities requires a deep understanding of the structure and semantics of the language, which is a major challenge for NLP.

In essence, the license allows commercial uses, which may lead to products derived from the datasets being sold for a fee to the communities who contributed voice data for free. As indicated earlier, there are several aspects of agency and community ownership that are implicated or threatened when the norm of openness is embraced in relation to the Global South inclusion project. In the context of AI, data openness speaks to mechanisms that allow or require access to data and underlying metadata to be free (no cost) and free from restrictions that could make such data inaccessible. Enforcing, imposing, or according copyright protections to such data could impose restrictions on accessibility, given the exclusive nature of such protections. And from the perspective of privacy rights, there is a need to secure private information from public exposure. Framed in this way, these two regimes—one prioritizing copyright protections, and one prioritizing privacy rights—could be justifiably opposed to practices that make data accessible, sometimes at no cost.

Building scalable NLP solutions that can handle large datasets and complex computations while maintaining high performance remains a daunting task. NLP systems must be able to handle multiple languages and dialects to cater to diverse user populations. However, language variations, slang, and dialectical differences pose challenges in developing universal NLP solutions that work effectively across different linguistic contexts. Understanding context is crucial for NLP tasks such as sentiment analysis, summarization, and language translation. However, capturing and representing context accurately remains a challenging task, especially in complex linguistic environments.

Generative models such as models of the GPT family could be used to automatically produce fluent reports from concise information and structured data. An example of this is Data Friendly Space’s experimentation with automated generation of Humanitarian Needs Overviews25. Note, however, that applications of natural language generation (NLG) models in the humanitarian sector are not intended to fully replace human input, but rather to simplify and scale existing processes. While the quality of text generated by NLG models is increasing at a fast pace, models are still prone to generating text displaying inconsistencies and factual errors, and NLG outputs should always be submitted to thorough expert review. Distributional semantics (Harris, 1954; Schütze, 1992; Landauer and Dumais, 1997) is one of the paradigms that has had the most impact on modern NLP, driving its transition toward statistical and machine learning-based approaches. 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 choice of area in NLP using Naïve Bayes Classifiers could be in usual tasks such as segmentation and translation but it is also explored in unusual areas like segmentation for infant learning and identifying documents for opinions and facts. Anggraeni et al. (2019) [61] used ML and AI to create a question-and-answer system for retrieving information about hearing loss. They developed I-Chat Bot which understands the user input and provides an appropriate response and produces a model which can be used in the search for information about required hearing impairments. The problem with naïve bayes is that we may end up with zero probabilities when we meet words in the test data for a certain class that are not present in the training data. There are particular words in the document that refer to specific entities or real-world objects like location, people, organizations etc. To find the words which have a unique context and are more informative, noun phrases are considered in the text documents.

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.

• Users also can identify personal data from documents, view feeds on the latest personal data that requires attention and provide reports on the data suggested to be deleted or secured.
• Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, but it still faces several challenges that hinder its full potential.
• We have also highlighted how long-term synergies between humanitarian actors and NLP experts are core to ensuring impactful and ethically sound applications of NLP technologies in humanitarian contexts.
• RAVN’s GDPR Robot is also able to hasten requests for information (Data Subject Access Requests – “DSAR”) in a simple and efficient way, removing the need for a physical approach to these requests which tends to be very labor thorough.
• But in NLP, though output format is predetermined in the case of NLP, dimensions cannot be specified.
• This is because words and phrases between languages are not literal translations of each other.

Mendonça et al. (2021) frame this as an online learning problem in the context of MT. As models have become more powerful and general-purpose, benchmarks have become more application-oriented and increasingly moved from single-task to multi-task and single-domain to multi-domain benchmarks. For practitioners and outsiders, benchmarks provide an objective lens into a field that enables them to identify useful models and keep track of a field’s progress. For instance, the AI Index Report 2021 uses SuperGLUE and SQuAD as a proxy for overall progress in natural language processing. In recent times, multilingual language models (MLLMs) have emerged as a viable option to handle multiple languages in a single model.

Imagine NLP systems that grasp the subtleties of human language and anticipate the needs and intentions behind our words, offering responses and solutions even before we ask. Natural languages exhibit substantial variations across regions, dialects, cultures, and even individual users. Accounting for different linguistic patterns, slangs, grammatical structures, and cultural nuances is a challenge that requires extensive research and data representation techniques. Language is inherently ambiguous, with words and phrases having multiple meanings depending on the context. Developing techniques that accurately capture context and disambiguate word senses remains an active area of research.

Yes, words make up text data, however, words and phrases have different meanings depending on the context of a sentence. Although NLP models are inputted with many words and definitions, one thing they struggle to differentiate is the context. If possible, benchmarks should aim to collect multiple annotations to identify ambiguous examples. Such information may provide a useful learning signal (Plank et al., 2014) and can be helpful for error analysis. nlp challenges In light of such ambiguity, it is even more important to report standard metrics such as inter-annotator agreement as this provides a ceiling for a model’s performance on a benchmark. While evaluation of natural language generation (NLG) models is notoriously difficult, standard n-gram overlap-based metrics such as ROUGE or BLEU are furthermore less suited to languages with rich morphology, which will be assigned relatively lower scores.

However, the real-world application of a task may confront the model with data different from its training distribution. It is thus key to assess the robustness of the model and how well it generalises to such out-of-distribution data, including data with a temporal shift and data from other language varieties. As a first rule of social responsibility for NLP, Chris Potts proposes “Do exactly what you said you would do”. As researchers in the field, we should communicate clearly what performance on a benchmark reflects and how this corresponds to real-world settings. In a similar vein, Bowman and Dahl (2021) argue that good performance on a benchmark should imply robust in-domain performance on the task. A benchmark’s data composition and evaluation protocol should reflect the real-world use case, as highlighted by Ido Dagan.

There are many metrics and methods to evaluate NLP models and applications, such as accuracy, precision, recall, F1-score, BLEU, ROUGE, perplexity, and more. However, these metrics may not always reflect the real-world quality and usefulness of your NLP outputs. Therefore, you should also consider using human evaluation, user feedback, error analysis, and ablation studies to assess your results and identify the areas of improvement. Wiese et al. [150] introduced a deep learning approach based on domain adaptation techniques for handling biomedical question answering tasks. Their model revealed the state-of-the-art performance on biomedical question answers, and the model outperformed the state-of-the-art methods in domains.

HUMSET is an original and comprehensive multilingual collection of humanitarian response documents annotated by humanitarian response professionals through the DEEP platform. The dataset contains approximately 17,000 annotated documents in three languages (English, French, and Spanish) and covers a variety of humanitarian emergencies from 2018 to 2021 related to 46 global humanitarian response operations. Through this functionality, DEEP aims to meet the need for common means to compile, store, structure, and share information using technology and implementing sound ethical standards28. 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. In cases where affected individuals still retain access to digital technologies, NLP tools for information extraction or topic modeling could be used to process unstructured reports sent through SMS either spontaneously or through semi-structured prompts. The data and modeling landscape in the humanitarian world is still, however, highly fragmented.

Many Natural Language Processing applications require domain-specific knowledge and terminology, but obtaining labeled data for specialized domains can be difficult. This lack of domain-specific data limits the performance of NLP systems in specialized domains such as healthcare, legal, and finance. NLP models extract features from the preprocessed text, such as word frequencies, syntactic patterns, or semantic representations, which are then used as input for further analysis.

To address these challenges, institutions must provide clear guidance to students on how to use NLP models as a tool to support their learning rather than as a replacement for critical thinking and independent learning. Institutions must also ensure that students are provided with opportunities to engage in active learning experiences that encourage critical thinking, problem-solving, and independent inquiry. This can be particularly helpful for students working independently https://chat.openai.com/ or in online learning environments where they might not have immediate access to a teacher or tutor. Furthermore, chatbots can offer support to students at any time and from any location. Students can access the system from their mobile devices, laptops, or desktop computers, enabling them to receive assistance whenever they need it. This flexibility can help accommodate students’ busy schedules and provide them with the support they need to succeed.

Recently, deep learning has been successfully applied to natural language processing and significant progress has been made. This paper summarizes the recent advancement of deep learning for natural language processing and discusses its advantages and challenges. Bi-directional Encoder Representations from Transformers (BERT) is a pre-trained model with unlabeled text available on BookCorpus and English Wikipedia. This can be fine-tuned to capture context for various NLP tasks such as question answering, sentiment analysis, text classification, sentence embedding, interpreting ambiguity in the text etc. [25, 33, 90, 148]. Earlier language-based models examine the text in either of one direction which is used for sentence generation by predicting the next word whereas the BERT model examines the text in both directions simultaneously for better language understanding.