Challenges And Opportunities Of Applying Natural Language Processing In Business Process Management

The Business Process Management field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. There has been significant progress in basic NLP tasks over the past few years. This progress is mostly due to deep learning and transformer technology, which creates very large deep learning models with billions of parameters that can “mimic” language based on very large data sets. This technology, however, cannot deal with more advanced tasks that require reasoning and deep understanding of language. And, the compute resources required to train and leverage such models are prohibitive in many cases. Generally, machine learning models, particularly deep learning models, do better with more data.Halevy et. Al. explain that simple models trained on large datasets did better on translation tasks than more complex probabilistic models that were fit to smaller datasets.Sun et.

That popularity was due partly to a flurry of results showing that such techniques can achieve state-of-the-art results in many natural language tasks, e.g., in language modeling and parsing. This is increasingly important in medicine and healthcare, where NLP helps analyze notes and text in electronic health records that would otherwise be inaccessible for study when seeking to improve care. 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.

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It can also be useful for intent detection, which helps predict what the speaker or writer may do based on the text they are producing. This is a really powerful suggestion, but it means that if an initiative is not likely to promote progress on key values, it may not be worth pursuing.Paullada et. Al. makes the point that “imply because a mapping can be learned does not mean it Problems in NLP is meaningful”. In one of the examples above, an algorithm was used to determine whether a criminal offender was likely to re-offend. The reported performance of the algorithm was high in terms of AUC score, but what did it learn? As discussed above, models are the product of their training data, so it is likely to reproduce any bias that already exists in the justice system.

There are vast applications of NLP in the digital world and this list will grow as businesses and industries embrace and see its value. While a human touch is important for more intricate communications issues, NLP will improve our lives by managing and automating smaller tasks first and then complex ones with technology innovation. They learn to perform tasks based on training data they are fed, and adjust their methods as more data is processed. Using a combination of machine learning, deep learning and neural networks, natural language processing algorithms hone their own rules through repeated processing and learning. It’s an intuitive behavior used to convey information and meaning with semantic cues such as words, signs, or images. It’s been said that language is easier to learn and comes more naturally in adolescence because it’s a repeatable, trained behavior—much like walking. That’s why machine learning and artificial intelligence are gaining attention and momentum, with greater human dependency on computing systems to communicate and perform tasks. And as AI and augmented analytics get more sophisticated, so will Natural Language Processing .

Text Extraction

This involves having users query data sets in the form of a question that they might pose to another person. The machine interprets the important elements of the human language sentence, which correspond to specific features in a data set, and returns an answer. Three tools used commonly for natural language processing include Natural Language Toolkit , Gensim and Intel natural language processing Architect. Intel NLP Architect is another Python library for deep learning topologies and techniques. Information in documents is usually a combination of natural language and semi-structured data in forms of tables, diagrams, symbols, and on. A human inherently reads and understands text regardless of its structure and the way it is represented. Today, computers interact with written forms of human language overcoming challenges in natural language processing easily. Topdanmark, the second largest insurance company in Denmark, has built natural language processing models that inform whether they should accept the risk of insuring a property in real time.
Problems in NLP

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