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

This is no small endeavour and we must deploy the latest and our own proprietary research and methods to be successful on our mission to break down language barriers for everyone. In the following sections, we will describe the most relevant techniques and methods that we have been using and explain why they provide advantages when working with low-resource languages. Without analysis based on theories provided by other language-related disciplines, erratic and unexpected behaviors of NN-based NLP systems will remain and limit potential applications.

These tasks rely on identifying temporal patterns and modeling nonstationary dynamics of human contexts (e.g., physical, physiological, mental, social, and environmental), providing a way to understand complex time variations and dependencies. The literature brings several derivations of RNNs already employed in the health area. For example, long short-term memory (LSTM) networks are a type of RNN capable of learning order dependence in long sequence prediction problems in nlp problems. Guo et al. (2021) used LSTM models to predict future cardiovascular health levels based on previous measurements from longitudinal electronic health record (EHR) data. Gated Recurrent Units (GRU) are derivations of RNN that use gates to control the flow of information, deciding what information should be passed to the output. GRU was used, for example, for early detection of post-surgical complications using longitudinal EHR data (Chen et al. 2021a).

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I also note that advances in the fields of computer science/engineering significantly changed what was possible to achieve in NLP. The final phase not only built the final representation of all the levels, but it also checked extra constraints specified in the original grammar. Because the first two phases only use partial constraints specified in the HPSG grammar, the final phase would reject results produced by the first two phases if they failed to satisfy these extra constraints. In this case, the system would backtrack to the previous phases to obtain the next candidate.

This view was changed by the emergence of feature-based formalisms that used directed acyclic graphs (DAGs) to allow reentrancy. Instead of mappings from one level to another, it described mutual relationships among different levels of representation in a declarative manner. This view was in line with our idea of description-based transfer, which used a bundle of features of different levels for transfer.

Modular Deep Learning

It is worth noting that, while the resultant architecture was similar to the climbing-up hierarchy processing, each stage in the final architecture was clearly defined and related to each other through the single declarative grammar. Furthermore, although feature-based formalisms were neat in terms of describing constraints in a declarative manner, the unification operation, which was a basic operation for treating feature-based descriptions, was computationally very expensive. To deliver practical NLP systems, we had to develop efficient implementation technologies and processing architectures for feature-based formalisms. By climbing up such a hierarchy, the differences among languages would become increasingly small, so that the mapping (i.e., the transfer phase) from one language to another would become as simple as possible. Independently of the target language, the goal of the analysis phase was to climb up the hierarchy, while the aim of the generation phase was to climb down the hierarchy to generate surface expressions in the target language.

IE systems should work at many levels, from word recognition to discourse analysis at the level of the complete document. 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. The goal of NLP is to accommodate one or more specialties of an algorithm or system. The metric of NLP assess on an algorithmic system allows for the integration of language understanding and language generation.

Some studies also use different RNN derivations in the same problem (e.g., longitudinal prediction modeling of Alzheimer’s disease) to identify the best strategy in terms of accuracy (Tabarestani et al. 2019). 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.

Thus, our main contribution is to consolidate a body of knowledge that supports advances in longitudinal health data analysis using transformers. Cross-lingual representations   Stephan remarked that not enough people are working on low-resource languages. There are 1,250-2,100 languages in Africa alone, most of which have received scarce attention from the NLP community. The question of specialized tools also depends on the NLP task that is being tackled. Cross-lingual word embeddings are sample-efficient as they only require word translation pairs or even only monolingual data.

There is a system called MITA (Metlife’s Intelligent Text Analyzer) (Glasgow et al. (1998) [48]) that extracts information from life insurance applications. Ahonen et al. (1998) [1] suggested a mainstream framework for text mining that uses pragmatic and discourse level analyses of text. We first give insights on some of the mentioned tools and relevant work done before moving to the broad applications of NLP. NLP can be classified into two parts i.e., Natural Language Understanding and Natural Language Generation which evolves the task to understand and generate the text. The objective of this section is to discuss the Natural Language Understanding (Linguistic) (NLU) and the Natural Language Generation (NLG). Omoju recommended to take inspiration from theories of cognitive science, such as the cognitive development theories by Piaget and Vygotsky.

Patterns matching the state-switch sequence are most likely to have generated a particular output-symbol sequence. Training the output-symbol chain data, reckon the state-switch/output probabilities that fit this data best. Text standardization is the process of expanding contraction words into their complete words. Contractions are words or combinations of words that are shortened by dropping out a letter or letters and replacing them with an apostrophe. In relation to NLP, it calculates the distance between two words by taking a cosine between the common letters of the dictionary word and the misspelt word.

Challenges in using NLP for low-resource languages and how NeuralSpace solves them

However, despite best efforts, it is nearly impossible to collect perfectly clean data, especially at the scale demanded by deep learning. NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users. If we get one augmented version from each instance, it already doubles the amount of data. It makes the process of generating up to 40 instances for each intent very easy, without any manual data collection. One can extrapolate this strategy using a combination of different data augmentation techniques to create even much larger datasets. On the other hand, CL tends to treat language as a closed system or focus on study on specific aspects of regularities that language show.

• However, such approaches still need to evolve to be used in clinical practice.
• 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.
• The most promising approaches are cross-lingual Transformer language models and cross-lingual sentence embeddings that exploit universal commonalities between languages.
• Ambiguity is one of the major problems of natural language which occurs when one sentence can lead to different interpretations.
• The world’s first smart earpiece Pilot will soon be transcribed over 15 languages.

But in NLP, though output format is predetermined in the case of NLP, dimensions cannot be specified. 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. 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.

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.

The approach in Shome (2021) uses the results of a set of depthwise separable residual feature extractor networks. This set receives the concatenation of the assessed sensorial data and the timestep when such data were assessed. The work presented in Darabi et al. (2020) considers two different input modalities. Firstly, the encoding of the sum of categorial inputs and positional encoding.

Their work was based on identification of language and POS tagging of mixed script. They tried to detect emotions in mixed script by relating machine learning and human knowledge. They have categorized sentences into 6 groups based on emotions and used TLBO technique to help the users in prioritizing their messages based on the emotions attached with the message. Seal et al. (2020) [120] proposed an efficient emotion detection method by searching emotional words from a pre-defined emotional keyword database and analyzing the emotion words, phrasal verbs, and negation words.