Deep learning for natural language processing: advantages and challenges National Science Review
Today, natural language processing or NLP has become critical to business applications. This can partly be attributed to the growth of big data, consisting heavily of unstructured text data. The need for intelligent techniques to make sense of all this text-heavy data has helped put NLP on the map. These advancements have led to an avalanche of language models that have the ability to predict words in sequences. Models that can predict the next word in a sequence can then be fine-tuned by machine learning practitioners to perform an array of other tasks.
It is a known issue that while there are tons of data for popular languages, such as English or Chinese, there are thousands of languages that are spoken but few people and consequently receive far less attention. There are 1,250–2,100 languages in Africa alone, but the data for these languages are scarce. 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.
PROGRESS IN NATURAL LANGUAGE PROCESSING
But this can cause issues, putting barrage of problems for CX agents to deal with, adding additional tasks to their plate. Join us as we walk through a use case on improving healthcare services by training AI models on synthetic data. In this short session we will draw on our experience of working in highly restricted enterprise… For example monitoring and analysing social media to inform marketing and product departments about what customers are thinking about their products.
The sets of viable states and unique symbols may be large, but finite and known. Few of the problems could be solved by Inference A certain sequence of output symbols, compute the probabilities of one or more candidate states with sequences. 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. There is a system called MITA (Metlife’s Intelligent Text Analyzer) (Glasgow et al. (1998) ) that extracts information from life insurance applications. Ahonen et al. (1998)  suggested a mainstream framework for text mining that uses pragmatic and discourse level analyses of text.
Learning from Machine Learning Maarten Grootendorst: BERTopic, Data Science, Psychology
In natural language processing (NLP), A vector space is a mathematical vector where words or documents are represented by numerical vectors form. The word or document’s specific features or attributes are represented by one of the dimensions of the vector. Vector space models are used to convert text into numerical representations that machine learning algorithms can understand.
It takes the information of which words are used in a document irrespective of number of words and order. In second model, a document is generated by choosing a set of word occurrences and arranging them in any order. This model is called multi-nomial model, in addition to the Multi-variate Bernoulli model, it also captures information on how many times a word is used in a document.
Since all the users may not be well-versed in machine specific language, Natural Language Processing (NLP) caters those users who do not have enough time to learn new languages or get perfection in it. 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.
People often discuss machine learning as a cost-cutting measure – it can [newline]“replace human labour”. But it’s pretty difficult to make this actually work [newline]well, because reliability is often much more important than price. If you’ve got
a plan that consists of lots of separate steps, the total cost is going to be
additive, while the risk multiplies.
Machine learning-based NLP — the basic way of doing NLP
Recurrent Neural Networks are the type of artificial neural network that is specifically built to work with sequential or time series data. It is utilised in natural language processing activities such as language translation, speech recognition, sentiment analysis, natural language production, summary writing, and so on. It differs from feedforward neural networks in that the input data in RNN does not only flow in a single direction but also has a loop or cycle inside its design that has “memory” that preserves information over time. As a result, the RNN can handle data where context is critical, such as natural languages. As most of the world is online, the task of making data accessible and available to all is a challenge.
Read more about https://www.metadialog.com/ here.