Natural Language Processing: Challenges and Future Directions SpringerLink
Next, we discuss some of the areas with the relevant work done in those directions. To generate a text, we need to have a speaker or an application and a generator or a program that renders the application’s intentions into a fluent phrase relevant to the situation. Linguistics is a broad subject that includes many challenging categories, some of which are Word Sense Ambiguity, Morphological challenges, Homophones challenges, and Language Specific Challenges (Ref.1).
Natural language processing is likely to be integrated into various tools and services, and the existing ones will only become better. Multilingual communication and code-switching (blending many languages in a discussion) are prevalent in today’s globalized environment. Recognizing language shifts, comprehending each language’s context, and giving thoughtful responses are all part of this.
3 Information Extraction and Mapping –
They’re often adapted to multiple types, depending on the problem to be solved and the data set. For instance, deep learning algorithms such as convolutional neural networks and recurrent neural networks are used in supervised, unsupervised and reinforcement learning tasks, based on the specific problem and availability of data. 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.
Another challenge is that a user expects more accurate and specific results from Relational Databases (RDB) for their natural language queries like English. To retrieve information from RDBs for user requests in natural language, the requests have to be converted into formal database queries like SQL. This approach leverages NLP to understand the user requests in natural language and prepare application service request URLs to retrieve data from the connected databases. The second problem is that with large-scale or multiple documents, supervision is scarce and expensive to obtain.
More from samuel chazy and Artificial Intelligence in Plain English
It is used in applications, such as mobile, home automation, video recovery, dictating to Microsoft Word, voice biometrics, voice user interface, and so on. Machine translation is used to translate text or speech from one natural language to another natural language. NLU mainly used in Business applications to understand the customer’s problem in both spoken and written language. A language may not have an exact match for a certain action or object that exists in another language.
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