5 Challenges in Natural Language Processing to watch out for TechGig

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.

• NLP models are not neutral or objective, but rather reflect the data and the assumptions that they are built on.
• While linguistics is an initial approach toward
  extracting the data elements from a document, it doesn’t stop there.
• In case of syntactic level ambiguity, one sentence can be parsed into multiple syntactical forms.
• Cosine similarity is calculated using the distance between two words by
  taking a cosine between the common letters of the dictionary word and the misspelled word.

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. The last two objectives may serve as a literature survey for the readers already working in the NLP and relevant fields, and further can provide motivation to explore the fields mentioned in this paper.

Classical Approaches

For example, by some
estimations, (depending on language vs. dialect) there are over 3,000 languages in Africa,
alone. The world’s first smart earpiece Pilot will soon be transcribed over 15 languages. The Pilot earpiece is connected via Bluetooth to the Pilot speech translation app, which uses speech recognition, machine translation and machine learning and speech synthesis technology.

NLP applications employ a set of POS tagging tools that assign a POS tag to each word or
symbol in a given text. Subsequently, the position of each word in a sentence is determined by
a dependency graph, generated in the same procedure. Those POS tags can be further
processed to create meaningful single or compound vocabulary terms. Natural Language Understanding (NLU) helps the machine to understand and analyse human language by extracting the metadata from content such as concepts, entities, keywords, emotion, relations, and semantic roles.

User feedback and adoption

It is used in customer care applications to understand the problems reported by customers either verbally or in writing. Linguistics is the science which involves the meaning of language, language context and various forms of the language. So, it is important to understand various important terminologies of NLP and different levels of NLP. We next discuss some of the commonly used terminologies in different levels of NLP. Machine learning requires A LOT of data to function to its outer limits – billions of pieces of
training data. That said,
data (and human language!) is only growing by the day, as are new machine learning
techniques and custom algorithms.

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. Most text categorization approaches to anti-spam Email filtering have used multi variate Bernoulli model (Androutsopoulos et al., 2000) [5] [15]. Pragmatic level focuses on the knowledge or content that comes from the outside the content of the document. Real-world knowledge is used to understand what is being talked about in the text.

Challenges and Solutions in Natural Language Processing (NLP)

For example, by some estimations, (depending on language vs. dialect) there are over 3,000 languages in Africa, alone. Natural Language Processing APIs allow developers to integrate human-to-machine communications and complete several useful tasks such as speech recognition, chatbots, spelling correction, sentiment analysis, etc. The main challenge is information overload, which poses a big problem to access a specific, important piece of information from vast datasets.

• 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.
• Sonnhammer mentioned that Pfam holds multiple alignments and hidden Markov model-based profiles (HMM-profiles) of entire protein domains.
• Though ML and NLP have emerged as the most potent and most used technology applied to the analysis of the text and text classification remains the most popular and the most used technique.
• Machine Translation is generally translating phrases from one language to another with the help of a statistical engine like Google Translate.
• To attain high-quality models, NLP performs an in-depth analysis of user inputs like lexical analysis, syntactic analysis, semantic analysis, discourse integration, and pragmatic analysis, etc.

Some of the tasks such as automatic summarization, co-reference analysis etc. act as subtasks that are used in solving larger tasks. Nowadays NLP is in the talks because of various applications and recent developments although in the late 1940s the term wasn’t even in existence. So, it will be interesting to know about the history of NLP, the progress so far has been made and some of the ongoing projects by making use of NLP. The third objective of this paper is on datasets, approaches, evaluation metrics and involved challenges in NLP. Section 2 deals with the first objective mentioning the various important terminologies of NLP and NLG.

Practical Guides to Machine Learning

This article will describe the benefits of natural language processing. We will provide a couple of examples of NLP use cases and tell you about its most remarkable achievements, future trends, and the challenges it faces. In the late 1940s the term NLP wasn’t in existence, but the work regarding machine translation (MT) had started. Russian and English were the dominant languages for MT (Andreev,1967) [4].

Moreover, data may be subject to privacy and security regulations, such as GDPR or HIPAA, that limit your access and usage. Therefore, you need to ensure that you have a clear data strategy, that you source data from reliable and diverse sources, that you clean and preprocess data properly, and that you comply with the relevant laws and ethical standards. The objective of this section is to discuss evaluation metrics used to evaluate the model’s performance and involved challenges. Seunghak et al. [158] designed a Memory-Augmented-Machine-Comprehension-Network (MAMCN) to handle dependencies faced in reading comprehension.

NLP scientists will try to create models with even better performance and more capabilities. Wiese et al. [150] introduced a deep learning approach based on domain adaptation techniques for handling biomedical question answering tasks. Their model revealed the state-of-the-art performance on biomedical question answers, and the model outperformed the state-of-the-art methods in domains. NLU enables machines to understand natural language and analyze it by extracting concepts, entities, emotion, keywords etc.

However, this is a major challenge for computers as they don’t have the same ability to the word was actually meant to spell. They literally take it for what it is — so NLP is very sensitive to spelling mistakes. 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).

There are other issues, such as ambiguity and slang, that create similar challenges. The main point is that the human language is a very complex and diversified mechanism. It varies greatly across geographical regions, industries, ages, types of people, etc.

Text summarization is extremely useful when there is no time or possibility to work with the entire text. Natural language processing algorithms will determine the most relevant phrases and sentences and present them as a summary of the text. We have all seen automatic text summarization in action, even if we did not realize it. One exciting application of text summarization is a Wikipedia article’s description. Any time we enter our query, if there is a Wikipedia article about it, Google will show one or two sentences describing the entity we are looking for.

NLP stands for Natural Language Processing, which is a part of Computer Science, Human language, and Artificial Intelligence. It is the technology that is used by machines to understand, analyse, manipulate, and interpret human’s languages. It helps developers to organize knowledge for performing tasks such as translation, automatic summarization, Named Entity Recognition (NER), speech recognition, relationship extraction, and topic segmentation. However, in practice, translating NLP queries to formal DB queries or service request URL is quite complicated due to several factors. These could be the complex DB layouts with table names, columns, and constraints, etc., or the semantic gap between user vocabulary and DB nomenclature.

Ritter (2011) [111] proposed the classification of named entities in tweets because standard NLP tools did not perform well on tweets. They re-built NLP pipeline starting from PoS tagging, then chunking for NER. A language can be defined as a set of rules or set of symbols where symbols are combined and used for conveying information or broadcasting the information. 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.

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