What are the Natural Language Processing Challenges, and How to Fix?

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. Even though evolved grammar correction tools are good enough to weed out sentence-specific mistakes, the training data needs to be error-free to facilitate accurate development in the first place.

With the development of cross-lingual datasets, such as XNLI, the development of stronger cross-lingual models should become easier. Rationalist approach or symbolic approach assumes that a crucial part of the knowledge in the human mind is not derived by the senses but is firm in advance, probably by genetic inheritance. It was believed that machines can be made to function like the human brain by giving some fundamental knowledge and reasoning mechanism linguistics knowledge is directly encoded in rule or other forms of representation. Statistical and machine learning entail evolution of algorithms that allow a program to infer patterns.

Programming Languages, Libraries, And Frameworks For Natural Language Processing (NLP)

This mechanism allows tracking of the relations between attributes across long sequences in both forward and reverse directions. Using these approaches is better as classifier is learned from training data rather than making by hand. The naïve bayes is preferred because of its performance despite its simplicity (Lewis, 1998) [67] In Text Categorization 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 irrespective of order.

Linguistics is concerned not only with language per se, but must also deal with how humans model the world.1 The study of semantics, for example, must relate language expressions to their meanings, which reside in the mental models possessed by humans. The consequences of letting biased models enter real-world settings are steep, and the good news is that research on ways to address NLP bias is increasing rapidly. Hopefully, with enough effort, we can ensure that deep learning models can avoid the trap of implicit biases and make sure that machines are able to make fair decisions. Luong et al. [70] used neural machine translation on the WMT14 dataset and performed translation of English text to French text. The model demonstrated a significant improvement of up to 2.8 bi-lingual evaluation understudy (BLEU) scores compared to various neural machine translation systems. 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.

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While these proposals did not present significant changes in the Nx module, the remaining works conducted diverse modifications (Fig. 4). The work in Dong et al. (2021) includes a layer called Prediction Bag Label, which is a specific component to the problem of predicting classes for groups of graphs. The work in Chen et al. (2021b) uses two transformers since the first transformer generates an embedding of diseases, which is used as input of the second transformer to augment its classification accuracy. Secondly, the multi-head attention block is modified to generate attention weights between different input streams. As these streams were mixed, the architecture also includes reconstruction layers to redefine the original streams. The work in Li et al. (2021) uses the encoder-decoder transformer architecture.

The University of Oxford released the BEHRT model in 2020 (Li et al. 2020) and advanced this model in two works published in 2022 (Rao et al. 2022a, b) and another in 2023 (Li et al. 2023a, b). However, our analysis did not consider this latter application since it does not use longitudinal data. DemRQ3 was also useful to attest to the strong interdisciplinarity of this type of research.

This approach was found in almost all the reviewed papers, as discussed later in this paper (Sect. 4.5). Indeed, longitudinal data analysis in health does not have a “gold standard” machine learning method that could be used as a comparisons baseline. The choice of this depends on the specific research question, the nature of the health data, and the goals of the analysis.

In fact, NLP is a tract of Artificial Intelligence and Linguistics, devoted to make computers understand the statements or words written in human languages. It came into existence to ease the user’s work and to satisfy the wish to communicate with the computer in natural language, and can be classified into two parts i.e. Natural Language Understanding or Linguistics and Natural Language Generation which evolves the task to understand and generate the text. Linguistics is the science of language which includes Phonology that refers to sound, Morphology word formation, Syntax sentence structure, Semantics syntax and Pragmatics which refers to understanding.

Multilingual Learning is a technique where a single model is trained on multiple languages. The assumption is that the model will learn representations that are very similar for similar words and sentences of different languages. Thus, this can also assist cross-lingual transfer learning as knowledge from data for high-resource languages like English can transfer to the model’s representations for low-resource languages like Swahili. This way, base models can perform better on low-resource languages despite the lack of enough text corpora. This was partly because we do not have effective ways of expressing semantic and pragmatic constraints. Computational linguists were interested in formal declarative ways for relating syntactic and semantic levels of representation, but not so much in how semantic constraints are to be expressed.

A different approach is proposed in Fouladvand et al. (2021), which considers distinct feature streams and their combination is conducted during the calculation of the attention weights. This approach seems to better explore the relationship between the feature types. However, its complexity is exponential regarding the number of streams since the model needs to conduct more operations and maintain the results of these operations problems in nlp in memory for the next steps. While the explainability/interpretability of results is desirable for inductive systems, such a feature is compulsory for health support systems. In this context, we could expect discussions about the implementation and evaluation (EvaRQ6) of explanainability in the reviewed works. The following schema (Table 5) summarizes our findings regarding the use of explainability in the reviewed papers.

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 recent NarrativeQA dataset is a good example of a benchmark for this setting. Reasoning with large contexts is closely related to NLU and requires scaling up our current systems dramatically, until they can read entire books and movie scripts.

• In An et al. (2022), the authors use end/dec architectures to derive an aware contextual feature representation of inputs.
• Machines relying on semantic feed cannot be trained if the speech and text bits are erroneous.
• Hierarchies of transformers (Pang et al. 2021) are also used to create clusters of sequential data according to a sliding window.
• Thus, adaptations should minimally affect its structure or be very well justified and validated.
• Due to the nature of the article, I ignore technical details and focus instead on the motivation of the research and the lessons which I have learned through research.

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. In the late 1940s the term NLP wasn’t in existence, but the work regarding machine translation (MT) had started. In fact, MT/NLP research almost died in 1966 according to the ALPAC report, which concluded that MT is going nowhere. But later, some MT production systems were providing output to their customers (Hutchins, 1986) [60].

Where do you see the most potential of NLP for the healthcare industry?

The context of a text may include the references of other sentences of the same document, which influence the understanding of the text and the background knowledge of the reader or speaker, which gives a meaning to the concepts expressed in that text. Semantic analysis focuses on literal meaning of the words, but pragmatic analysis focuses on the inferred meaning that the readers perceive based on their background knowledge. ” is interpreted to “Asking for the current time” in semantic analysis whereas in pragmatic analysis, the same sentence may refer to “expressing resentment to someone who missed the due time” in pragmatic analysis.

Our review shows that several transformer architectures provide and evaluate approaches for explainability, aiming to engender trust with the healthcare professionals and provide transparency to the decision-making process. However, such approaches still need to evolve to be used in clinical practice. Moreover, in the mid-term, AI regulations make explainability of the decision models a requirement for so-called high-risk AI applications/domains, such as health. An example of such regulations is the European Union Artificial Intelligence Act (EU AI Act), which will regulate the use of AI in the EU and enforce the use of explainable models (Panigutti et al. 2023). However, as identified in our review, the current approaches for explainability mostly rely on identifying the importance of input features to the models’ outcomes (predictions or classifications). Moreover, explainability strategies must also be extended to characterize better liability regarding potential errors, which is a critical aspect for legal and ethical reasons in clinical practice (Naik et al. 2022).

• An application of the Blank Slate Language Processor (BSLP) (Bondale et al., 1999) [16] approach for the analysis of a real-life natural language corpus that consists of responses to open-ended questionnaires in the field of advertising.
• For example, we found that the reasoning carried out by domain experts on pathways is based on similarities between entities.
• In fact, MT/NLP research almost died in 1966 according to the ALPAC report, which concluded that MT is going nowhere.
• Finally, the model was tested for language modeling on three different datasets (GigaWord, Project Gutenberg, and WikiText-103).
• There are a multitude of languages with different sentence structure and grammar.

The first objective gives insights of the various important terminologies of NLP and NLG, and can be useful for the readers interested to start their early career in NLP and work relevant to its applications. The second objective of this paper focuses on the history, applications, and recent developments in the field of NLP. 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.

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. This is where NLP (Natural Language Processing) comes into play — the process used to help computers understand text data. Learning a language is already hard for us humans, so you can imagine how difficult it is to teach a computer to understand text data. On the other hand, for reinforcement learning, David Silver argued that you would ultimately want the model to learn everything by itself, including the algorithm, features, and predictions. Many of our experts took the opposite view, arguing that you should actually build in some understanding in your model.