Major Challenges of Natural Language Processing NLP

They tuned the parameters for character-level modeling using Penn Treebank dataset and word-level modeling using WikiText-103. 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).

Besides, transferring tasks that require actual natural language understanding from high-resource to low-resource languages is still very challenging. The most promising approaches are cross-lingual Transformer language models and cross-lingual sentence embeddings that exploit universal commonalities between languages. However, such models are sample-efficient as they only require word translation pairs or even only monolingual data.

Reasoning about large or multiple documents

For example, mHealth applications daily assess different multifeature longitudinal data of their users, which generate multi-longitudinal sequences of health data (Wac 2016). In five years, the longitudinal data sequence will have about 1825 timesteps. According to experimental results (Culurciello 2018), RNNs are good for remembering sentences in the order of hundreds but not thousands of timesteps. Moreover, this sequential flow through RNN units also brings performance problems since RNNs must process data sequentially. Thus, they cannot employ parallel computing hardware and graphics processing units (GPU) in training and inference.

There is a huge amount of knowledge codified in the health datasets (e.g., EHRs), derived from the experience of a large number of experts for several years. As we show in this paper, current transformer models rely on such knowledge to make conclusions that are impossible or very hard to derive by humans due to the amount and complexity of relations involved. The approaches discussed in this review try to demonstrate a future where this ability can be leveraged accurately as a decision-support tool for healthcare experts. As the use of transformers to analyze multifeatured longitudinal health data is recent, we have not identified a convergence regarding aspects such as positional encoding, input embedding, or training strategies using categorical or numerical values.

Text Analysis with Machine Learning

Many responses in our survey mentioned that models should incorporate common sense. Though natural language processing tasks are closely intertwined, they can be subdivided into categories for convenience. The earliest decision trees, producing systems of hard if–then rules, were still very similar to the old rule-based approaches. Only the introduction of hidden Markov models, applied to part-of-speech tagging, announced the end of the old rule-based approach. The decoder and encoder architectures are very similar, and decoder units (Nx) can also be stacked on each other. However, the decoder outputs one token at a time since each output token becomes part of the next decoder input (auto-regressive process).

This shift continued further to the ongoing research, which uses a large language model (BioBERT). In this recent work, linguistic information is assumed to be implicitly embedded in the language model. The information is not represented explicitly in IE systems (Figure 11) (Ju, Miwa, and Ananiadou 2018; Trieu et al. 2020). These characteristics of IE as an NLP task made the mapping from language to information very different from the transfer phase in MT, which attempts to covey the same information in the source and target languages. Given a sentence, its representation of all the levels was constructed at the final stage by using the HPSG grammar. The first phase was a supertagger that would disambiguate supertags assigned to words in a sentence.

Natural Language Processing Journal

This paper uses a systematic review protocol to identify and analyze studies that proposed adaptations for transformers’ architectures so they can handle longitudinal health data. This protocol contains a set of research questions that guide the analysis of these approaches regarding their architectures, input vocabulary, aims, positional embedding implementations, explainability, and other technical aspects. Moreover, this analysis also allows the identification of trends in the area, main limitations, and opportunities for research directions.

The field of Natural Language Processing (NLP) has evolved with, and as well as influenced, recent advances in Artificial Intelligence (AI) and computing technologies, opening up new applications and novel interactions with humans. Intermediate tasks (e.g., part-of-speech tagging and dependency parsing) have not been needed anymore. There had been attempts to construct probabilistic models without supervision of annotated tree banks (for example, Fujisaki [1984]).

3 Continuous numerical data

The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. This review shows that clinical-based sparse electronic health records are the main multifeatured longitudinal data source for transformers in the health domain (Sect. 4.3).

However, such inputs are given in the form of graphs that relate concepts of the domain. Thus, this approach augments the explainability of the attention mechanism since it relies on the attention weights assigned for each graph instance rather than only weights between inputs and outcomes. The work in Peng et al. (2021) shows that its model learns interpretable representations according to the structure of an ontology given as input.

This step is essential since transformers process all the inputs in parallel, unlike RNN or LTSM approaches, where inputs are fed in sequence. While these techniques do not require any specific positional strategy because they already understand the sequence of the inputs, transformers need this additional information. The use of sequential indexes is the simplest strategy to generate positional encodings. However, it becomes problematic as the number of inputs increases since the high values of the latter positional encodings may dominate the initial values, distorting the final results. A simple solution is to convert the encoding values as a fraction of length, i.e., index/w, where w is the number of inputs. Strategies such as frequency-based positional encodings (Vaswani et al. 2017) avoid this issue.

Based on this analysis, the transformer literature does not present a concrete strategy to represent this temporal notion. This is one of the main issues of this type of architecture, as detailed later in this paper. One way the industry has addressed challenges in multilingual modeling is by translating from the target language into English and then performing the various NLP tasks. If you’ve laboriously crafted a sentiment corpus in English, it’s tempting to simply translate everything into English, rather than redo that task in each other language.

Challenges in Developing Multilingual Language Models in Natural Language Processing (NLP)

However, only a few classes of features (e.g., medication and diagnosis codes) have been considered at the same time thus far. An interesting exception is the work in Li et al. (2023a, b), which uses several health data types (diagnosis, medication, procedure, examinations, blood pressure, drinking status, smoking status, and body mass index). However, such data are part of the same vocabulary, and thus, the learning process cannot explore the particular semantics of each. In other words, the model does not know if a token represents, for example, a medication or a diagnosis. A different approach explicitly indicates this type, such as in Prakash et al. (2021). This additional semantics can buster the efficiency and effectiveness of the learning process, similar to natural language processing, when the model knows that a token represents a noun or an adjective.

Therefore, RNNs can model the temporal dependencies between parts of the longitudinal data and understand how they evolve over time. The main reason is that RNNs suffer from the vanishing gradient problem since long-term information must sequentially travel through all RNN units before generating results. Thus, such information will likely vanish by being multiplied many times by small values. RNN-like networks, such as LSTM and GRU, consider this problem, but their more complex architectures still present sequential paths from older past units until the final one.

Machine learning requires A LOT of data to function to its outer limits – billions of pieces of training data. That said, data (and human language!) is only growing by the day, as are new machine learning techniques and custom algorithms. All of the problems above will require more research and new techniques in order to improve on them. Approaches that address these and other ethical questions may employ neuro-symbolic strategies, which integrate symbolic knowledge (e.g., ontologies, knowledge graphs) into the inductive reasoning process. These strategies can be designed to incorporate explicit ethical rules and principles, as well as be programmed with ethical guidelines to guide their decision-making and prevent them from making unethical choices. The study of Dong et al. (2021), for example, represents the first step in this direction as it relies on the weights assigned for each graph.

In this research paper, a comprehensive literature review was undertaken in order to analyze Natural Language Processing (NLP) application based in different domains. Also, by conducting qualitative research, we will try to analyze the development of the current state and the challenge of NLP technology as a key for Artificial Intelligence (AI) technology, pointing out some of the limitations, risks and opportunities. In our research, we rely on primary data from applicable legislation and secondary public domain data sources providing related information from case studies. The next step is to understand how the Nx module of each approach differs (ArcRQ2) from the original design illustrated in Fig. This layer uses activation functions such as Sigmoid (Li et al. 2020; Rao et al. 2022a) and Softmax (Florez et al. 2021; Zeng et al. 2022). Other works follow (Florez et al. 2021; Boursalie et al. 2021; Prakash et al. 2021) the decoder stage of this architecture, while the proposal in An et al. (2022) relies on both stages.

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. Most higher-level NLP applications involve aspects that emulate intelligent behaviour and apparent comprehension of natural language. More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above).

• Named entity recognition (NER) is a technique to recognize and separate the named entities and group them under predefined classes.
• 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.
• The entire process of creating these valuable assets is fundamental and straightforward.
• Such a large portion of the world population is still underserved by NLP systems because of various challenges that developers face when building NLP systems for low-resource languages.

In production, where request rates increase and decrease, scaling expenses for one multilingual model will always be less than or equal to n monolingual models. The formal theory of language was not necessarily concerned problems in nlp with human language. They revealed the relationship between classes of language and the computational power of their recognizers. Parsing algorithms of formal languages were studied not necessarily for human languages.