A Systematic Literature Review of Natural Language Processing: Current State, Challenges and Risks SpringerLink

Thus, semantic analysis is the study of the relationship between various linguistic utterances and their meanings, but pragmatic analysis is the study of context which influences our understanding of linguistic expressions. Pragmatic analysis helps users to uncover the intended meaning of the text by applying contextual background knowledge. More complex models for higher-level tasks such as question answering on the other hand require thousands of training examples for learning. Transferring tasks that require actual natural language understanding from high-resource to low-resource languages is still very challenging. With the development of cross-lingual datasets for such tasks, such as XNLI, the development of strong cross-lingual models for more reasoning tasks should hopefully become easier.

Over this process, the vector of features from the encoder supports the decoder in focusing on the appropriate positions of the input sequence. We suggest the original paper “Attention is All You Need” (Vaswani et al. 2017) for details about these components. Multilingual learning also empowers NLP models to generalize and infer for languages it was not fine-tuned on. Exemplarily, if we have fine-tuned a model on English, Hindi, and Marathi data, we can also predict Tamil with reasonably high accuracy assuming the base language model was pre-trained on a very large set of languages including Tamil. A model must be able to detect hate speech in different languages including a few low and high-resource languages. Through multilingual training, we make one model jointly learn on data from various languages, often exceeding 50 or more languages.

Natural Language Processing

The Linguistic String Project-Medical Language Processor is one the large scale projects of NLP in the field of medicine [21, 53, 57, 71, 114]. 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 [78,79,80, 82, 84]. It is expected to function as an Information Extraction tool for Biomedical Knowledge Bases, particularly Medline abstracts. The lexicon was created using MeSH (Medical Subject Headings), Dorland’s Illustrated Medical Dictionary and general English Dictionaries.

According to Peng et al. (2021), ODEs are particularly interesting in handling arbitrary time gaps between observations. The input embedding layer is a type of lookup table that contains vectorial representations of input data (e.g., each term of the vocabulary). This layer is essential because transformers process vectors of continuous values like any other machine learning algorithm. There are several proposals for input embeddings, which can be classified into context-independent (traditional) and context-dependent (contextualized) embeddings (Wang et al. 2020). While the former produces unique and distinct representations for each token without considering its context; the latter learns different embeddings for the same token according to its context (Fig. 2). Transformers also allow each sequential input to contain multiple embeddings.

Natural Language Processing: Challenges and Future Directions

This section summarizes the main concepts of transformers intending to enable a better understanding of this review. These concepts are also used to formulate the research questions of the research protocol (Sect. 3). Current systems are prone to bias and incoherence, and occasionally behave erratically. Despite the challenges, machine learning engineers have many opportunities to apply NLP in ways that are ever more central to a functioning society. Note that transformational grammar considered a set of rules applied to generate surface structures from the deep structure.

The top discipline, linguistics, on the other hand, is concerned with rules that are followed by languages. This schematic view is certainly oversimplified, and there are subject fields in which these disciplines overlap. Psycholinguistics, for example, is a subfield of linguistics which is concerned with how the human mind processes language. One of the challenges with NLP is not just measuring accuracy via an F1 score, but also looking at things like biases, inclusiveness, and “black holes” that the models miss.

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Neural machine translation, based on then-newly-invented sequence-to-sequence transformations, made obsolete the intermediate steps, such as word alignment, previously necessary for statistical machine translation. According to this strategy, a further embedding called “Vocab_type” could be added to the other inputs, including the semantics of each vocabulary to the final embedding. However, this approach problems in nlp generates an overload of information in the final embedding. Thus, we must evaluate the real predictive value of the embeddings to eliminate or represent them differently. The fourth column characterizes the fine-tuning strategies (EvaRQ3) when they are employed. These strategies use traditional layers for prediction or classification, which use sigmoid or softmax as the activation function.

Creating and maintaining natural language features is a lot of work, and having to do that over and over again, with new sets of native speakers to help, is an intimidating task. It’s tempting to just focus on a few particularly important languages and let them speak for the world. A company can have specific issues and opportunities in individual countries, and people speaking less-common languages are less likely to have their voices heard through any channels, not just digital ones.

A useful way to verify the effectiveness of transformers is by comparing their results with outcomes generated by clinicians. However, only two papers (Li et al. 2020; Rasmy et al. 2021) employed clinicians during the validation process. Modern NLP involves machines’ interaction with human languages for the study of patterns and obtaining meaningful insights. Transformers (Vaswani et al. 2017) are a recent type of deep neural network focused on analyzing sequences. Consequently, it is possible to scale the speed and capacity of such processing compared to previous RNN-like approaches. Moreover, transformers introduced the attention mechanism, which considers the relationship between attributes, irrespective of where they are placed in a sequence.