Local Interpretations for Explainable Natural Language Processing: A Survey ACM Computing Surveys

Libraries in these languages provide tools for a myriad of NLP tasks, such as text analysis, tokenisation, and semantic analysis. We witness this synthesis in cutting-edge AI research, where systems can now comprehend context, sarcasm, and even the subtleties of different dialects. These AI-driven NLP capabilities are not just academic pursuits; they’re being integrated into everyday applications, enhancing user experiences and making technology more accessible. Detecting stress, regional accents present challenges for natural language processing. frustration and other emotions from the tone of voice as well as the context is one of the tasks that machines can already do. Understanding of and the ability to simulate prosody and tonality is a big part of speech processing and synthesis right now. Good examples of current applications of emotion analysis are visual content search by emotion identifiers (“happiness,” “love,” “joy,” “anger”) in digital image repositories, and automated image and video tags predictions.

Additionally, text-to-speech technology benefits individuals with learning disabilities or language barriers, providing an alternative mode of accessing and comprehending information. Text-to-speech technology provides a range of benefits that greatly enhance the user experience. It allows individuals with visual impairments or reading difficulties to access content quickly, ensuring inclusivity and accessibility.

Even though we think of the Internet as open to everyone, there is a digital language divide between dominant languages (mostly from the Western world) and others. Only a few hundred languages are represented on the web and speakers of minority languages are severely limited in the information available to them. Techniques like Latent Dirichlet Allocation (LDA) help identify underlying topics within a collection of documents. Imagine analyzing news articles to discover latent themes like “politics,” “technology,” or “sports.”

As we continue to innovate, the potential to revolutionize communication and information processing is limitless. These areas highlight the breadth and depth of NLP as it continues to evolve, integrating more deeply with various aspects of technology and society. Each advancement not only expands the capabilities of what machines can understand and process but also opens up new avenues for innovation across all sectors of industry and research. Stanford’s socially equitable NLP tool represents a notable breakthrough, addressing limitations observed in conventional off-the-shelf AI solutions.

Reconsider if you really need a natural language IVR system

An essential distinction in interpretable machine learning is between local and global interpretability. Following Guidotti et al. [58] and Doshi-Velez and Kim [44], we take local interpretability to be “the situation in which it is possible to understand only the reasons for a specific decision” [58]. That is, a locally interpretable model is a model that can give explanations for specific predictions and inputs. We take global interpretability to be the situation in which it is possible to understand “the whole logic of a model and follow the entire reasoning leading to all the different possible outcomes” [58]. A classic example of a globally interpretable model is a decision tree, in which the general behaviour of the model may be easily understood by examining the decision nodes that make up the tree. NLP is integral to AI as it enables machines to read and comprehend human languages, allowing for more sophisticated interactions with technology.

Despite these challenges, advancements in machine learning and the availability of vast amounts of voice data for training models have led to significant improvements in speech recognition technology. This progress is continually expanding the usability and reliability of voice-controlled applications across many sectors, from mobile phones and automotive systems to healthcare and home automation. Within the field of Natural Language Processing (NLP) and computer science, an important sector that intersects with computational linguistics is Speech Recognition Optimization. This specialized area focuses on training AI bots to improve their understanding and performance in speech recognition tasks. By leveraging computational linguistic techniques, researchers and engineers work towards enhancing the accuracy, robustness, and efficiency of AI models in transcribing and interpreting spoken language. NLP is the capability of a computer to interpret and understand human language, whether it is in a verbal or written format.

• Typology of local interpretable methods by identifying the important features from inputs.
• CloudFactory provides a scalable, expertly trained human-in-the-loop managed workforce to accelerate AI-driven NLP initiatives and optimize operations.
• Keywords— Sentiment Analysis, Classification Algorithms, Naïve Bayes, Max Entropy, Boosted Trees, Random Forest.
• But with NLP tools, you can find out the key trends, common suggestions, and customer emotions from this data.
• Founder Kul Singh says the average employee spends 30 percent of the day searching for information, costing companies up to $14,209 per person per year.

Syntax and semantic analysis are two main techniques used in natural language processing. As technology evolves, chatbots are becoming more sophisticated, capable of handling increasingly complex tasks and providing more meaningful interactions. They are an integral part of the ongoing shift towards more interactive and responsive digital customer service environments. While faithfulness can be evaluated more easily via automatic evaluation metrics, the comprehensibility and trustworthiness of interpretations usually are evaluated through human evaluations in the current research works. Though using large numbers of participants helps remove the subjective bias, this requires the cost of setting up larger-scale experiments, and it is also hard to ensure that every participant understands the task and the evaluation criteria. For example, regression weights have classically been considered “interpretable” but require a user to have some understanding of regression beforehand.

Data connectors collect raw data from various sources and process them to identify key elements and their relationships. Natural Language Processing enables users to type their queries as they feel comfortable and get relevant search suggestions and results. Sentiment analysis has been a popular research topic in the field of Arabic NLP, with numerous datasets and approaches proposed in the literature [39][40].

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Text-to-Speech (TTS) technology converts written text into spoken words using advanced algorithms and NLP. The input text undergoes analysis and editing, breaking it down into phonetic sounds, which are then synthesized to convert text and create natural-sounding synthetic voices. Therefore, you may need to hire an NLP developer or software engineering team to create tailored solutions for your unique needs—especially if you’re in fields such as finance, manufacturing, healthcare, automotive, and logistics. While transformer models translate text and speech in real time, developers can make them focus on the most relevant segments of language to produce better results. One of the most visible examples is in voice-activated assistants like Siri and Alexa, which employ NLP to understand and respond to user requests.

With the global natural language processing (NLP) market expected to reach a value of $61B by 2027, NLP is one of the fastest-growing areas of artificial intelligence (AI) and machine learning (ML). Natural language processing (NLP) is the ability of a computer program to understand human language as it’s spoken and written — referred to as natural language. Linguistic probes, also referred to as “diagnostic classifiers” [73] or “auxiliary tasks” [2], are a post hoc method for examining the information stored within a model. However, recent research [70, 130, 141] has shown that probing experiments require careful design and consideration of truly faithful measurements of linguistic knowledge.

It enables individuals with visual impairments to access text-based content easily, making it highly valuable for accessibility purposes. Moreover, language learning platforms leverage text-to-speech tools to enhance pronunciation and reinforce learning. Achieving proper pronunciation, natural intonation, and rhythm contributes to producing human-like speech.

By marrying the computational power of machines with the intricacies of human language, we’re creating AI that can engage with us more effectively. Complex visual sentiment analysis requires higher levels of abstraction, cultural knowledge, understanding of subjectivity, concepts, and cues. It is harder to acquire labelled or curated datasets and create models for learning to extract and predict meaning for this purpose.

Kia Motors America regularly collects feedback from vehicle owner questionnaires to uncover quality issues and improve products. An NLP model automatically categorizes and extracts the complaint type in each response, so quality issues can be addressed in the design and manufacturing process for existing and future vehicles. By leveraging NLP algorithms, language learning apps can generate high-quality content that is tailored to learners’ needs and preferences. The use of AI-generated content enhances the language learning experience by providing accurate feedback, personalized learning materials, and interactive activities. However, like any technology, AI-generated content also has its challenges and limitations. By analyzing the emotional tone of content, brands can create content that elicits specific emotional responses from the audience.

Since the Transformer architecture processes all tokens in parallel and can not distinguish the order of these tokens by itself. The positional encodings are calculated using the Equations 4 and 5, and then added to the input embeddings before they are processed by the Transformer model. The positional encodings have the same dimension as the input embeddings, allowing them to be summed. Similarly, Khalifa et al. introduced the Gumar corpus [6], another large-scale multidialectal Arabic corpus for Arabian Gulf countries. The corpus consists of 112 million words (9.33 million sentences) extracted from 1200 novels that are publicly available and written in Arabian Gulf dialects, with 60.52% of the corpus text being written in Saudi dialect.

They can also leverage text-to-speech technology to receive audio support for written texts, helping them understand and comprehend the content more effectively. Meanwhile, despite their advancements, natural language processing systems can also struggle with the diverse range of dialects, regional accents, and mispronunciations that customers may use, potentially leading to further inaccuracies. Similarly, other potential flashpoints of allowing free-flowing conversations to occur include the challenges of word choice like industry jargon and slang. Although AI-powered speech recognition has come a long way in its ability to convert speech into text that it can comprehend, there is not a one-size-fits-all solution. Our world is an intricate tapestry of cultures and languages, and the imperative for NLP to be multilingual and sensitive to this diversity is clear.

NLP plays a crucial role in enhancing chatbot interactions by enabling them to understand user intent, extract relevant information, and generate appropriate responses. For example, a customer asking a chatbot, “What are the opening hours of your store?” can receive a personalized response based on their location and the current day. All supervised deep learning tasks require labeled datasets in which humans apply their knowledge to train machine learning models. Labeled datasets may also be referred to as ground-truth datasets because you’ll use them throughout the training process to teach models to draw the right conclusions from the unstructured data they encounter during real-world use cases. Current approaches to natural language processing are based on deep learning, a type of AI that examines and uses patterns in data to improve a program’s understanding.

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NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate well-formed human language on its own. NLU algorithms must tackle the extremely complex problem of semantic interpretation – that is, understanding the intended meaning of spoken or written language, with all the subtleties, context and inferences that we humans are able to comprehend. NLP plays a critical role in AI content generation by enabling machines to understand and generate human language. By leveraging NLP algorithms, businesses can create relevant, coherent, and engaging content for their social media platforms.

In French, ______ semantics deals with word meanings, while ______ ensures the right interpretation of sentence structure. This innovative technology allows for a personalized touch by tailoring the reading speed or selecting from a vast selection of speech voices, crafting a genuinely immersive literary experience. Whether on the move, engaged in daily routines, or simply unwinding, audiobooks rendered through text-to-speech integration promise limitless literary enjoyment. The LLMs in the public domain come preloaded with massive amounts of information and training. However, they tend to lack a targeted understanding of a given business’s needs and the intentions of its callers. Many customers may also lack the relevant vocabulary or precise product knowledge to produce adequate, on-the-spot responses without any suggestions or nudges from someone else.

As a subset of AI, NLP is emerging as a component that enables various applications in fields where customers can interact with a platform. These include search engines and data acquisition in medical research and the business intelligence realm. As computers can better understand humans, they will have the ability to gather the information to make better decision-making possible. However, apart from the discussed limitations of the current interpretable methods, one existing problem is that evaluating whether an interpretation is faithful mainly considers the interpretations for the model’s correct predictions. In other words, most existing interpretable works only explain why an instance is correctly predicted but do not give any explanations about why an instance is wrongly predicted. If the explanations of a model’s correct predictions precisely reflect the model’s decision-making process, then this interpretable method will usually be regarded as a faithful interpretable method.

Most of these earlier approaches use learned LSTM decoders to generate the explanations, learning a language generation module from scratch. Most of these methods generate their explanations post hoc, making a prediction before generating an explanation. This means that while the explanations may serve as valid reasons for the prediction, they may also not truthfully reflect the reasoning process of the model itself. They explicitly evaluate their model’s faithfulness using LIME and human evaluation and find that this improves performance and does indeed result in explanations faithful to the gradient-based explanations. Natural language processing involves the use of algorithms to analyze and understand human language. This can include the analysis of written text, as well as speech recognition and language translation.

As technology continues to advance, the demand for skilled NLP professionals will only grow, making it an exciting and rewarding field to pursue. You can foun additiona information about ai customer service and artificial intelligence and NLP. An NLP startup is a company that utilizes NLP applications as part of its business model to satisfy its target market. As an organization in the initial stages of operations, the NLP startup will usually be financed by its founders and subsequently be able to have access to additional external funding from a variety of sources, including venture capitalists. While there has been much study of the interpretability of DNNs, there are no unified definitions for the term interpretabilty, with different researchers defining it from different perspectives. Enhancements anticipated in processing spoken French, integrating with translation and NLP applications.

In the process, as a community we have overfit to the characteristics and conditions of English-language data. In particular, by focusing on high-resource languages, we have prioritised methods that work well only when large amounts of labelled and unlabelled data are available. Another area that is likely to see growth is the development of algorithms that are capable of processing data in real-time. This will be particularly useful for businesses that want to monitor social media and other digital platforms for mentions of their brand. CSB is likely to play a significant role in the development of these real-time text mining and NLP algorithms. We convert text into numerical features using techniques like bag-of-words, TF-IDF (Term Frequency-Inverse Document Frequency), or word embeddings (e.g., Word2Vec, GloVe).

Company XYZ, a leading telecommunications provider, implemented NLP to enhance their customer engagement strategies. By integrating NLP into their chatbot, they were able to accurately understand customer queries and provide relevant information in real-time. This resulted in reduced response times, improved customer satisfaction, and increased efficiency in handling customer inquiries. Additionally, by personalizing responses based on customer preferences and past interactions, Company XYZ witnessed a significant increase in customer loyalty and repeat business. By using sentiment analysis using NLP, the business can gain valuable insights into its prospects and improve its products and services accordingly.

These algorithms can also identify keywords and sentiment to gauge the speaker’s emotional state, thereby fine-tuning the model’s understanding of what’s being communicated. However, these models were pretrained on relatively small corpora with sizes ranging from 67M to 691MB. Moreover, compared to other prominent Arabic language models they exhibit modest performance improvements on specific benchmarks.

In this section, we’ll explore how artificial intelligence grasps the intricate nuances of human language through various linguistic methods and models. We’ll examine the roles of syntax, semantics, pragmatics, and ontology in AI’s language understanding capabilities. Incorporating Natural Language Processing into AI has seen tangible benefits in fields such as translation services, sentiment analysis, and virtual assistants.

The ultimate objective of NLP is to read, decipher, understand, and make sense of human languages in a valuable way. NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. One of the key ways that CSB has influenced text mining is through the development of machine learning algorithms. These algorithms are capable of learning from large amounts of data and can be used to identify patterns and trends in unstructured text data.

An NLP-centric workforce builds workflows that leverage the best of humans combined with automation and AI to give you the “superpowers” you need to bring products and services to market fast. Managed workforces are more agile than BPOs, more accurate and consistent than crowds, and more scalable than internal teams. They provide dedicated, trained teams that learn and scale with you, becoming, in essence, extensions of your internal teams. Data labeling is easily the most time-consuming and labor-intensive part of any NLP project. Building in-house teams is an option, although it might be an expensive, burdensome drain on you and your resources. Employees might not appreciate you taking them away from their regular work, which can lead to reduced productivity and increased employee churn.

While larger enterprises might be able to get away with creating in-house data-labeling teams, they’re notoriously difficult to manage and expensive to scale. For instance, you might need to highlight all occurrences of proper nouns in documents, and then further categorize those nouns by labeling them with tags indicating whether they’re names of people, places, or organizations. If the chatbot can’t handle the call, real-life Jim, the bot’s human and alter-ego, steps in. Data cleansing is establishing clarity on features of interest in the text by eliminating noise (distracting text) from the data. It involves multiple steps, such as tokenization, stemming, and manipulating punctuation. Another major benefit of NLP is that you can use it to serve your customers in real-time through chatbots and sophisticated auto-attendants, such as those in contact centers.

This has led to an increased need for more sophisticated text mining and NLP algorithms that can extract valuable insights from this data. In this section, we will discuss how CSB’s influence on text mining and NLP has changed the way businesses extract knowledge from unstructured data. In conclusion, understanding AI and natural language processing is crucial for developing AI-generated content for video game dialogue. NLP allows AI systems to comprehend player input, generate appropriate responses, and provide contextually relevant dialogue options. While challenges persist, the collaboration between AI and human writers is proving to be a promising approach for creating immersive and engaging gaming experiences. One aspect of AI that has experienced remarkable advancements is natural language processing (NLP).

Lastly, remember that there may be some growing pains as your customers adjust to the new system—even when you provide great educational resources. Most customers are familiar with (and may still expect) old-school IVR systems, so it’s not a great idea to thrust a new system upon them without warning. Aside from NLTK, Python’s ecosystem includes other libraries such as spaCy, which is known for its speed and efficiency, and TextBlob, which is excellent for beginners due to its simplicity and ease of use. For those interested in deep learning approaches to NLP, libraries like TensorFlow and PyTorch offer advanced capabilities.

For example, He et al. [65] measured the change in BLEU scores to examine whether certain input words were essential to the predictions in natural machine translation. In general, using extracted rationales from original textual inputs as the models’ local interpretations focuses on the faithfulness and comprehensibility of interpretations. While trying to select rationales that can well represent the complete inputs in terms of accurate prediction results, extracting short and consecutive sub-phrases is also the key objective of the current rationale extraction works. Such fluent and consecutive sub-phrases (i.e., the well-extracted rationales) make this rationales extraction a friendly, interpretable method that provides readable and understandable explanations to non-expert users without NLP-related knowledge. The subsequent decades saw steady advancements as the field shifted from rule-based to statistical methods.

From sentiment analysis to language translation, English is the undisputed leader of the pack. The major reason for this is the abundance of digital data available in English for AI to master. Natural language processing plays a vital part in technology and the way humans interact with it. Though it has its challenges, NLP is expected to become more accurate with more sophisticated models, more accessible and more relevant in numerous industries. The main benefit of NLP is that it improves the way humans and computers communicate with each other. Enabling computers to understand human language makes interacting with computers much more intuitive for humans.

Such a model would be crucial in advancing the field of Arabic NLP by significantly improving performance on tasks involving the Saudi dialect, thus addressing a significant gap in the existing language models. The integration of NLP technology in AI-generated podcasts ensures a more immersive, interactive, and accessible listening experience. As NLP algorithms continue to advance, we can expect further improvements in speech synthesis, sentiment analysis, and language understanding, further enhancing the capabilities of AI-generated podcasts. NLP works by breaking down human language into smaller parts and analyzing them to understand their meaning. This process involves several steps, including tokenization, part-of-speech tagging, parsing, and semantic analysis. Parsing involves analyzing the sentence structure to understand how the words and phrases relate to each other.

As we continue to advance in this field, the synergy between data mining, text analytics, and NLP will shape the future of information extraction. Sentiment analysis determines the emotional tone of text (positive, negative, or neutral). For instance, analyzing customer reviews to understand product sentiment or monitoring social media for brand perception. The latest NLP solutions have near-human levels of accuracy in understanding speech, which is the reason we see a huge number of personal assistants in the consumer market.

Explore the future of NLP with Gcore’s AI IPU Cloud and AI GPU Cloud Platforms, two advanced architectures designed to support every stage of your AI journey. From building to training to deployment, the Gcore’s AI IPU and GPU cloud infrastructures are tailored to enhance human-machine communication, interpret unstructured text, accelerate machine learning, and impact businesses through analytics and chatbots. The AI IPU Cloud platform is optimized for deep learning, customizable to support most setups for inference, and is the industry standard for ML. On the other hand, the AI GPU Cloud platform is better suited for LLMs, with vast parallel processing capabilities specifically for graph computing to maximize potential of common ML frameworks like TensorFlow. To achieve this goal, NLP uses algorithms that analyze additional data such as previous dialogue turns or the setting in which a phrase is used.

Advancements in speech synthesis algorithms and techniques are necessary to tackle these challenges effectively. Achieving accuracy and precision in speech synthesis is a key challenge in text-to-speech (TTS) technology. TTS systems must faithfully reproduce the best text words and sounds, ensuring correct https://chat.openai.com/ pronunciation, natural intonation, and appropriate emphasis. For example, if your organization can get by with a traditional speech IVR that handles simple “yes or no” questions, then you can save a lot of time, money, and other resources by holding off on implementing a natural language IVR system.

Compatibility issues may arise when using TTS across various devices and platforms, potentially limiting its accessibility and usability. Text-to-speech (TTS) technology encounters several challenges, including accurate pronunciation, generating natural-sounding speech, multilingual support, and accessibility. Overall, text-to-speech technology has the potential to bridge communication gaps and enhance understanding between people from different linguistic backgrounds. Advancements in technology have greatly enhanced accessibility for individuals with visual impairments.

In Section 4, we summarise several primary methods to evaluate the interpretability of each method discussed in Section 3. We finally discussed the limitations of current interpretable methods in NLP in Section 5 and the possible future trend of interpretability development at the end. Natural Language processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and human language. NLP plays a crucial role in AI content generation, as it enables machines to understand, interpret, and generate human language. In today’s fast-paced digital world, businesses are constantly looking for ways to engage with their customers more effectively.

In reality, the boundaries between language varieties are much blurrier than we make them out to be and language identification of similar languages and dialects is still a challenging problem (Jauhiainen et al., 2018). For instance, even though Italian is the official language in Italy, there are around 34 regional languages and dialects spoken throughout the country. If speech recognition software is particularly error prone with particular accents, customers with that accent will stop using it over time and instead use the traditional way of interacting with the system. Imagine a world where your computer not only understands what you say but how you feel, where searching for information feels like a conversation, and where technology adapts to you, not the other way around.

NLP is a branch of AI that focuses on the interaction between computers and humans through natural language. It enables machines to understand, interpret, and generate human language, making it an essential component of AI generated content. The exploration of Natural Language Processing (NLP) in today’s technological landscape highlights its critical role at the intersection of artificial intelligence, computer science, and linguistics. NLP enables machines to interpret, understand, and manipulate human language, bringing about transformative changes across various industries.

The most common approach is to use NLP-based chatbots to begin interactions and address basic problem scenarios, bringing human operators into the picture only when necessary. Categorization is placing text into organized groups and labeling based on features of interest. If you’ve ever tried to learn a foreign language, you’ll know that language can be complex, diverse, and ambiguous, and sometimes even nonsensical. English, for instance, is filled with a bewildering sea of syntactic and semantic rules, plus countless irregularities and contradictions, making it a notoriously difficult language to learn. Finally, we’ll tell you what it takes to achieve high-quality outcomes, especially when you’re working with a data labeling workforce. You’ll find pointers for finding the right workforce for your initiatives, as well as frequently asked questions—and answers.

Together, these two factors improve a business’ overall ability to respond to customer needs and wants. SaudiBERT is a BERT-based language model that was pretrained exclusively on Saudi dialectal text from scratch. The model follows the same architecture as the original BERT model with 12 encoder layers, 12 attention heads per layer, and a hidden layer size of 768 units. Additionally, we set the vocabulary size of SaudiBERT model to 75,000 wordpieces, enabling it to capture a wide range of terms and expressions found in Saudi dialectal text, including emojis.

Topic analysis is extracting meaning from text by identifying recurrent themes or topics. Aspect mining is identifying aspects of language present in text, such as parts-of-speech Chat GPT tagging. NLP helps organizations process vast quantities of data to streamline and automate operations, empower smarter decision-making, and improve customer satisfaction.

These systems mimic the human brain and ‘learn’ to understand the human language from huge datasets. Through techniques such as categorization, entity extraction, sentiment analysis and others, text mining extracts the useful information and knowledge hidden in text content. In the business world, this translates in being able to reveal insights, patterns and trends in even large volumes of unstructured data.

Part-of-speech (POS) tagging is a process where each word in a sentence is labeled with its corresponding grammatical category, such as noun, verb, adjective, or adverb. POS tagging helps in understanding the syntactic structure of a sentence, which is essential for accurate summarization. By analyzing the POS tags, NLP algorithms can identify the most important words or phrases in a sentence and assign them more weight in the summarization process. Your initiative benefits when your NLP data analysts follow clear learning pathways designed to help them understand your industry, task, and tool.

After all, the beauty of language lies not in monotony but in the polyphony of diverse accents, and it’s time our AI started singing along. Imagine a world where NLP comprehends the subtle poetry of Farsi, the rhythmic beats of Swahili, or the melodic charm of Italian, as fluently as it understands English. AI should not merely parrot English but appreciate the nuances of every language – each with its unique accent, melody, and rhythm.

However, these automated metrics must be used carefully, as recent work has found they often correlate poorly with human judgements of explanation quality. Natural Language Explanation (NLE) refers to the method of generating free text explanations for a given pair of inputs and their prediction. In contrast to rational extraction, where the explanation text is limited to that found within the input, NLE is entirely freeform, making it an incredibly flexible explanation method. This has allowed it to be applied to tasks outside of NLP, including reinforcement learning [48], self-driving cars [85], and solving mathematical problems [99].

Natural Language Processing (NLP) is a branch of AI that focuses on the interaction between computers and human language. Virtual digital assistants like Siri, Alexa, and Google’s Home are familiar natural language processing applications. These platforms recognize voice commands to perform routine tasks, such as answering internet search queries and shopping online.

They have achieved state-of-the-art results on the majority of tasks when compared with AraBERT and other multilingual models. Natural language processing goes hand in hand with text analytics, which counts, groups and categorizes words to extract structure and meaning from large volumes of content. Text analytics is used to explore textual content and derive new variables from raw text that may be visualized, filtered, or used as inputs to predictive models or other statistical methods.