What are some of the challenges we face in NLP today? by Muhammad Ishaq DataDrivenInvestor

The first step of the NLP process is gathering the data (a sentence) and breaking it into understandable parts (words). Considering these metrics in mind, it helps to evaluate the performance of an NLP model for a particular task or a variety of tasks. The objective of this section is to discuss evaluation metrics used to evaluate the model’s performance and involved challenges. Named entity recognition is a core capability in Natural Language Processing (NLP). It’s a process of extracting named entities from unstructured text into predefined categories. The use of NLP has become more prevalent in recent years as technology has advanced.

For machines to perform these complex applications, they need to perform

several smaller, more bite-sized NLP tasks. In other words, to build

successful commercial NLP applications, we must master the NLP tasks

that serve as building blocks for those applications. By the 1990s, such successes led researchers to expand beyond text into

speech recognition. Speech recognition, like machine translation, had

been around since the early 1950s, spurred by early successes by the

likes of Bell Labs and IBM. In the 1960s, for example, such systems could take voice

commands for playing chess but not do much else. Like the broader field of artificial intelligence, NLP has had many

booms and busts, lurching from hype cycles to AI winters.

What language is best for natural language processing?

If you are interested in working on low-resource languages, consider attending the Deep Learning Indaba 2019, which takes place in Nairobi, Kenya from August 2019. 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). Instead of embedding having to represent the absolute position of a word, Transformer XL uses an embedding to encode the relative distance between the words. This embedding is used to compute the attention score between any 2 words that could be separated by n words before or after. All the ones mentioned are NLP libraries except BERT, which is a word embedding.

The issue with using formal linguistics to create NLP models is that the rules for any language are complex. The rules of language alone often pose problems when converted into formal mathematical rules. Although linguistic rules work well to define how an ideal person would speak in an ideal world, human language is also full of shortcuts, inconsistencies, and errors. The proposed test includes a task that involves the automated interpretation and generation of natural language. Merity et al. conventional word-level language models based on Quasi-Recurrent Neural Network and LSTM to handle the granularity at character and word level.

Natural Language Processing

In much the same way, until the machine performs dependency parsing, it has little to no knowledge of the structure of the text that it has converted into tokens. Once the structure is apparent, processing the text becomes a little bit easier. Statistical machine translation helped reduce the need for human

handcrafted rules, and it relied much more

heavily on learning from data. The more data (i.e., bilingual

text corpuses) the system had, the better the translation. Statistical machine translation would remain the most widely studied and

used machine translation method until the rise of neural machine

translation in the mid-2010s.

Natural language processing models tackle these nuances, transforming recorded voice and written text into data a machine can make sense of. Natural Language Processing excels at understanding syntax, but semiotics and pragmatism are still challenging to say the least. In other words, a computer might understand a sentence, and even create sentences that make sense. But they have a hard time understanding the meaning of words, or how language changes depending on context. Benefits and impact   Another question enquired—given that there is inherently only small amounts of text available for under-resourced languages—whether the benefits of NLP in such settings will also be limited.

Many text mining, text extraction, and NLP techniques exist to help you extract information from text written in a natural language. Customers calling into centers powered by CCAI can get help quickly through conversational self-service. If their issues are complex, the system seamlessly passes customers over to human agents. Human agents, in turn, use CCAI for support during calls to help identify intent and provide step-by-step assistance, for instance, by recommending articles to share with customers. And contact center leaders use CCAI for insights to coach their employees and improve their processes and call outcomes.

This trend is not slowing down, so an ability to summarize the data while keeping the meaning intact is highly required. Event discovery in social media feeds (Benson et al.,2011) [13], using a graphical model to analyze any social media feeds to determine whether it contains the name of a person or name of a venue, place, time etc. With its ability to understand human behavior and act accordingly, AI has already become an integral part of our daily lives. The use of AI has evolved, with the latest wave being natural language processing (NLP).

Classical Approaches

In Word2Vec, GloVe only word embeddings are considered and previous and next sentence context is not considered. The second section of the interview questions covers advanced NLP techniques such as Word2Vec, GloVe word embeddings, and advanced models such as GPT, Elmo, BERT, XLNET-based questions, and explanations. Bag of Words is a commonly used model that depends on word frequencies or occurrences to train a classifier.

Working with large contexts is closely related to NLU and requires scaling up current systems until they can read entire books and movie scripts. However, there are projects such as OpenAI Five that show that acquiring sufficient amounts of data might be the way out. Santoro et al. [118] introduced a rational recurrent neural network with the capacity to learn on classifying the information and perform complex reasoning based on the interactions between compartmentalized information. Finally, the model was tested for language modeling on three different datasets (GigaWord, Project Gutenberg, and WikiText-103). Further, they mapped the performance of their model to traditional approaches for dealing with relational reasoning on compartmentalized information.

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. Each of these levels can produce ambiguities that can be solved by the knowledge of the complete sentence. The ambiguity can be solved by various methods such as Minimizing Ambiguity, Preserving Ambiguity, Interactive Disambiguation and Weighting Ambiguity [125]. Some of the methods proposed by researchers to remove ambiguity is preserving ambiguity, e.g. (Shemtov 1997; Emele & Dorna 1998; Knight & Langkilde 2000; Tong Gao et al. 2015, Umber & Bajwa 2011) [39, 46, 65, 125, 139].

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