Deep learning for natural language processing: advantages and challenges National Science Review

Developments in Natural Language Processing: Applications and Challenges IEEE Conference Publication

natural language processing challenges

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. This trend is not slowing down, so an ability to summarize the data while keeping the meaning intact is highly required. Here the speaker just initiates the process doesn’t take part in the language generation. It stores the history, structures the content that is potentially relevant and deploys a representation of what it knows.

There are also challenges that are more unique to natural language processing, namely difficulty in dealing with long tail, incapability of directly handling symbols, and ineffectiveness at inference and decision making. By capturing the unique complexity of unstructured language data, AI and natural language understanding technologies empower NLP systems to understand the context, meaning and relationships present in any text. This helps search systems understand the intent of users searching for information and ensures that the information being searched for is delivered in response. 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.

Bi-directional Encoder Representations from Transformers (BERT) is a pre-trained model with unlabeled text available on BookCorpus and English Wikipedia. You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. BERT provides contextual embedding for each word present in the text unlike context-free models (word2vec and GloVe).

LLM Challenges

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. They tested their model on WMT14 (English-German Translation), IWSLT14 (German-English translation), and WMT18 (Finnish-to-English translation) and achieved 30.1, 36.1, and 26.4 BLEU points, which shows better performance than Transformer baselines. CapitalOne claims that Eno is First natural language SMS chatbot from a U.S. bank that allows customers to ask questions using natural language.

A chatbot system uses AI technology to engage with a user in natural language—the way a person would communicate if speaking or writing—via messaging applications, websites or mobile apps. The goal of a chatbot is to provide users with the information they need, when they need it, while reducing the need for live, human intervention. This form of confusion or ambiguity is quite common if you rely on non-credible NLP solutions.

Finding the best and safest cryptocurrency exchange can be complex and confusing for many users. Crypto and Coinbase are two trading platforms where buyers and sellers conduct monthly or annual transactions. The detailed discussion on vs Coinbase help you choose what is suitable for you.

In our research, we rely on primary data from applicable legislation and secondary public domain data sources providing related information from case studies. Artificial Intelligence (AI) has been used for processing data to make decisions, Interact with humans, and understand their feelings and emotions. With the advent of the Internet, people share and express their thoughts on day-to-day activities and global and local events through text messaging applications. Hence, it is essential for machines to understand emotions in opinions, feedback, and textual dialogues to provide emotionally aware responses to users in today’s online world. The field of text-based emotion detection (TBED) is advancing to provide automated solutions to various applications, such as business and finance, to name a few.

natural language processing challenges

Statistical and machine learning entail evolution of algorithms that allow a program to infer patterns. An iterative process is used to characterize a given algorithm’s underlying algorithm that is optimized by a numerical measure that characterizes numerical parameters and learning phase. Machine-learning models can be predominantly categorized as either generative or discriminative. Generative methods can generate synthetic data because of which they create rich models of probability distributions.

This necessitated that all storage and analysis of the data take place on a secure server behind the KPNC firewall. While the secure server represented a solution to the challenge of maintaining security on confiden tial information, the processes for receiving training and obtaining access to the secure server were understandably rigorous and time-consuming. Furthermore, occasional server connectivity problems and limitations on computational speed of analyses performed via the server portal, together created occasional delays in data processing for the non-KPNC investigators on the team. Another issue was that patient or physician names and phone numbers necessitated additional data security measures be taken.

Meta AI Research Introduces MobileLLM: Pioneering Machine Learning Innovations for Enhanced On-Device Intelligence

The LSP-MLP helps enabling physicians to extract and summarize information of any signs or symptoms, drug dosage and response data with the aim of identifying possible side effects of any medicine while highlighting or flagging data items [114]. The National Library of Medicine is developing The Specialist System [78–80, 82, 84]. It is expected to function as an Information Extraction tool for Biomedical Knowledge Bases, particularly Medline abstracts.

Discriminative methods are more functional and have right estimating posterior probabilities and are based on observations. Srihari [129] explains the different generative models as one with a resemblance that is used to spot an unknown speaker’s language and would bid the deep knowledge of numerous languages to perform the match. Discriminative methods rely on a less knowledge-intensive approach and using distinction between languages. Whereas generative models can become troublesome when many features are used and discriminative models allow use of more features [38]. Few of the examples of discriminative methods are Logistic regression and conditional random fields (CRFs), generative methods are Naive Bayes classifiers and hidden Markov models (HMMs). Today, we can’t hear the word “chatbot” and not think of the latest generation of chatbots powered by large language models, such as ChatGPT, Bard, Bing and Ernie, to name a few.

Natural Language Processing in Humanitarian Relief Actions – ICTworks

Natural Language Processing in Humanitarian Relief Actions.

Posted: Thu, 12 Oct 2023 07:00:00 GMT [source]

[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. In the existing literature, most of the work in NLP is conducted by computer scientists while various other professionals have also shown interest such as linguistics, psychologists, and philosophers etc. One of the most interesting aspects of NLP is that it adds up to the knowledge of human language.

For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website. This is where training and regularly updating custom models can be helpful, although it oftentimes requires quite a lot of data. Synonyms can lead to issues similar to contextual understanding because we use many different words to express the same idea. Furthermore, some of these words may convey exactly the same meaning, while some may be levels of complexity (small, little, tiny, minute) and different people use synonyms to denote slightly different meanings within their personal vocabulary. This work was part of a larger parent study, ECLIPPSE, funded by the NIH/NLM (R01LM012355).

For example, CONSTRUE, it was developed for Reuters, that is used in classifying news stories (Hayes, 1992) [54]. It has been suggested that many IE systems can successfully extract terms from documents, acquiring relations between the terms is still a difficulty. PROMETHEE is a system that extracts lexico-syntactic patterns relative to a specific conceptual relation (Morin,1999) [89].

First, the capability of interacting with an AI using human language—the way we would naturally speak or write—isn’t new. And while applications like ChatGPT are built for interaction and text generation, their very nature as an LLM-based app imposes some serious limitations in their ability to ensure accurate, sourced information. Where a search engine returns results that are sourced and verifiable, ChatGPT does not cite sources and may even return information that is made up—i.e., hallucinations. However, if we need machines to help us out across the day, they need to understand and respond to the human-type of parlance. Natural Language Processing makes it easy by breaking down the human language into machine-understandable bits, used to train models to perfection. Yet, in some cases, words (precisely deciphered) can determine the entire course of action relevant to highly intelligent machines and models.

In this article, we will learn about the evolution of NLP and how it became the way it is as today. After that, we will go into the advancement of neural networks and their applications in the field of NLP, especially the Recurrent Neural Network (RNN). In the end, we will go into the SOTA models such as Hierarchical Attention Network (HAN) and Bidirectional Encoder Representations from Transformers (BERT). In this paper, we provide a short overview of NLP, then we dive into the different challenges that are facing it, finally, we conclude by presenting recent trends and future research directions that are speculated by the research community. So, for building NLP systems, it’s important to include all of a word’s possible meanings and all possible synonyms.

natural language processing challenges

Descartes and Leibniz came up with a dictionary created by universal numerical codes used to translate text between different languages. An unambiguous universal language based on logic and iconography was then developed by Cave Beck, Athanasius Kircher, and Joann Joachim Becher. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.

Merity et al. [86] extended conventional word-level language models based on Quasi-Recurrent Neural Network and LSTM to handle the granularity at character and word level. They tuned the parameters for character-level modeling using Penn Treebank dataset and word-level modeling using WikiText-103. Natural language processing is the key that unlocks the potential of AI to comprehend and utilize unstructured language data, bridging the automation gap between humans and technology and leveraging existing assets for new insights that were previously unavailable. Machines relying on semantic feed cannot be trained if the speech and text bits are erroneous. This issue is analogous to the involvement of misused or even misspelled words, which can make the model act up over time.

It is because a single statement can be expressed in multiple ways without changing the intent and meaning of that statement. Evaluation metrics are important to evaluate the model’s performance if we were trying to solve two problems with one model. 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. 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. Phonology includes semantic use of sound to encode meaning of any Human language.

When choosing between highly correlated indices, we selected theoretically motivated features/indices with demonstrated validity in previous writing-based studies. Second, selected the topmost important indices obtained after training the models. With the exception of the first LP model developed, these methods resulted in models for the LPs and the CPs that included from 15 to 20 indices. The data mining and pre-processing were further complicated by the need to maintain security of the confidential information. It was impractical to de-identify the voluminous data (e.g., remove patient names and phone numbers that occasionally existed in the messages).

Among all the NLP problems, progress in machine translation is particularly remarkable. Neural machine translation, i.e. machine translation using deep learning, has significantly outperformed traditional statistical machine translation. The state-of-the art neural translation systems employ sequence-to-sequence learning models comprising RNNs [4–6]. End-to-end training and representation learning are the key features of deep learning that make it a powerful tool for natural language processing. It might not be sufficient for inference and decision making, which are essential for complex problems like multi-turn dialogue.

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. Ambiguity in NLP refers to sentences and phrases that potentially have two or more possible interpretations. To address these possible imprecisions during LP and CP model development, we ran testing and training sets and used cross validation to try and maintain generalizability across the entire sample population (see below). Next, to address the problem of the parser stoppages, periodic human oversight of data processing was necessary. When parser stoppages occurred, the location of the stoppage was excised, and the parser was run again.

The paper presents a systematic literature review of the existing literature published between 2005 and 2021 in TBED. This review has meticulously examined 63 research papers from the IEEE, Science Direct, Scopus, and Web of Science databases to address four primary research questions. It also reviews the different applications of TBED across various research domains and highlights its use. An overview of various emotion models, techniques, feature extraction methods, datasets, and research challenges with future directions has also been represented. In order to address the challenges inherent to interdisciplinary collaboration, we employed real-time and post-hoc clarification and documentation of term and tasks (Table 1).

Google has provided us many convenient and powerful tools with their advanced algorithms. With cutting edge research in NLP, Google search and Google translate are the top two services that are almost used every day and are almost becoming an extension to our minds. Even if the NLP services try and scale beyond ambiguities, errors, and homonyms, fitting in slags or culture-specific verbatim isn’t natural language processing challenges easy. There are words that lack standard dictionary references but might still be relevant to a specific audience set. If you plan to design a custom AI-powered voice assistant or model, it is important to fit in relevant references to make the resource perceptive enough. NLP machine learning can be put to work to analyze massive amounts of text in real time for previously unattainable insights.

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. 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.

NLP, paired with NLU (Natural Language Understanding) and NLG (Natural Language Generation), aims at developing highly intelligent and proactive search engines, grammar checkers, translates, voice assistants, and more. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI. 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 aren’t written in text form.

The innovative LLM-to-SLM method enhances the efficiency of SLMs by leveraging the detailed prompt representations encoded by LLMs. This process begins with the LLM encoding the prompt into a comprehensive representation. A projector then adapts this representation to the SLM’s embedding space, allowing the SLM to generate responses autoregressively. To ensure seamless integration, the method replaces or adds LLM representations into SLM embeddings, prioritizing early-stage conditioning to maintain simplicity. It aligns sequence lengths using the LLM’s tokenizer, ensuring the SLM can interpret the prompt accurately, thus marrying the depth of LLMs with the agility of SLMs for efficient decoding. Vendors offering most or even some of these features can be considered for designing your NLP models.

We examined several methods of accounting for the imbalance that resulted from our initial analyses. Because the data we initially generated were imbalanced, the ML approach had to be adapted to different types of imbalances and the thresholds had to be set accordingly. As such, we explored whether alternative ML approaches would be more appropriate. In the end, we both refined our expert rating scoring systems and adjusted the ML algorithm scoring thresholds to balance the rating proportions.

natural language processing challenges

Xie et al. [154] proposed a neural architecture where candidate answers and their representation learning are constituent centric, guided by a parse tree. Under this architecture, the search space of candidate answers is reduced while preserving the hierarchical, syntactic, and compositional structure among constituents. Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research describes a theory derivation process that is used to develop a conceptual framework for medication therapy management (MTM) research. Review article abstracts target medication therapy management in chronic disease care that were retrieved from Ovid Medline (2000–2016).

How to deal with the long tail problem poses a significant challenge to deep learning. With deep learning, the representations of data in different forms, such as text and image, can all be learned as real-valued vectors. This makes it possible to perform information processing across multiple modality.

  • HMM is not restricted to this application; it has several others such as bioinformatics problems, for example, multiple sequence alignment [128].
  • In fact, a large amount of knowledge for natural language processing is in the form of symbols, including linguistic knowledge (e.g. grammar), lexical knowledge (e.g. WordNet) and world knowledge (e.g. Wikipedia).
  • There are also challenges that are more unique to natural language processing, namely difficulty in dealing with long tail, incapability of directly handling symbols, and ineffectiveness at inference and decision making.
  • Machine learning requires A LOT of data to function to its outer limits – billions of pieces of training data.

Based on the information generated in the focus group, three investigators plus one additional team member who had not participated in the focus group (a senior biostatistician) were then asked for follow-up communications after the virtual focus group. Three of these investigators were interviewed by WB over email and one by phone to delve deeper and to elicit more specifics about the challenges and solutions within and across study domains. Field notes were taken for all interviews; the focus group was recorded and transcribed. 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.

natural language processing challenges

This approach to making the words more meaningful to the machines is NLP or Natural Language Processing. Informal phrases, expressions, idioms, and culture-specific lingo present a number of problems for NLP – especially for models intended for broad use. Because as formal language, colloquialisms may have no “dictionary definition” at all, and these expressions may even have different meanings in different geographic areas. Furthermore, cultural slang is constantly morphing and expanding, so new words pop up every day. 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.

Earlier machine learning techniques such as Naïve Bayes, HMM etc. were majorly used for NLP but by the end of 2010, neural networks transformed and enhanced NLP tasks by learning multilevel features. Major use of neural networks in NLP is observed for word embedding where words are represented in the form of vectors. Initially focus was on feedforward [49] and CNN (convolutional neural network) architecture [69] but later researchers adopted recurrent neural networks to capture the context of a word with respect to surrounding words of a sentence. LSTM (Long Short-Term Memory), a variant of RNN, is used in various tasks such as word prediction, and sentence topic prediction.