Natural Language Processing - Science topic

Creating a high-quality dataset for fine-tuning machine learning models is a crucial step in building robust and accurate models. The process of creating a dataset involves data collection, preprocessing, labeling, and validation. Here's a step-by-step guide to help you create a dataset for fine-tuning ML models:

Creating a high-quality dataset is a foundational step in machine learning, and it often requires substantial effort. Properly curated datasets are essential for training and fine-tuning models effectively.

Najib Abusalbi I did yes, I searched in datasets websites like hugging face and kaggle, google datasets, searched on Google search engine and Google Scholars, and across journals and many websites, I didn't manage to find any public repository except the one made by the National council of Italy, other than that, did not find datasets, even searching in published papers and articles, no one mention from where they got their datasets or where it can be available, a few who do that sadly.

Considering that physiotherapy is a clinical work, it needs more research in this field because each patient is different from another patient even with the same symptoms and needs a different treatment plan.

I suggest to read ChatGPT vs. InstructGPT. ChatGPT is an AI-powered intelligent model developed by OpenAI, capable of generating human-like text based on context & past conversations. ChatGPT is basically a sibling model to InstructGPT. It is a language model that follows a given instruction so as to provide a detailed response for user query.

Dear Tong Guo ,

Language models like GPT-3 have revolutionized modern deep learning applications for NLP, leading to widespread publicity and recognition. Interestingly, however, most of the technical novelty of GPT-3 was inherited from its predecessors GPT and GPT-2 . As such, a working understanding of GPT and GPT-2 is useful for gaining a better grasp of current approaches for NLP.

Regards,

Shafagat

In the multifaceted landscape of artificial neural architectures, activation functions emerge as pivotal computational primitives that instantiate non-linearities within the model, thereby amplifying the model's capacity for function approximation in high-dimensional input spaces. As we pivot towards natural language processing (NLP) applications—particularly large language models like transformer architectures—the exigencies for nuanced, adaptable activation functions are exacerbated. Herein, we present a disquisition on the Swish-Gated Linear Unit (SwiGLU), elucidating its mathematical formulations, computational affordances, and empirical efficacy vis-à-vis traditional activation functions in the domain of expansive language models.

Theoretical Underpinnings

The SwiGLU activation function can be mathematically characterized as a convex combination of the input x and a non-linear function f(x), effectively amalgamating aspects of both linearity and non-linearity. The architecture incorporates gating mechanisms, frequently utilized in Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) cells, which facilitate the dynamic recalibration of the information flow within the neural nodes.

SwiGLU(x)=αx+(1−α)f(x)

Here, α is a gating parameter that can either be learned through back-propagation or set heuristically.

Comparative Advantages

Application in Large Language Models

In the deployment of SwiGLU within large language models, the activation function is commonly used in the hidden layers and attention mechanisms, modulating the weight matrices and facilitating a more nuanced understanding of linguistic constructs, semantics, and contextual embeddings. Preliminary empirical analyses evince that models employing SwiGLU outperform counterparts utilizing traditional activation functions in tasks ranging from text classification to machine translation and abstractive summarization.

In summation, the SwiGLU activation function manifests as a veritable computational artifact that augments the modeling acumen of large language models. It amalgamates the advantages of both linear and non-linear functional forms, offering a nuanced mechanism for capturing the multifarious complexities inherent in natural language processing tasks. Its adoption is likely to catalyze advancements in the accuracy, efficiency, and interpretability of contemporary NLP architectures.

The advent of ChatGPT has engendered a paradigmatic shift in the dynamics of task-oriented dialogue systems, eliciting a reevaluation of long-standing approaches to conversational agent architecture, design, and performance metrics. Prior to ChatGPT, task-oriented dialogue agents predominantly employed modular architectures, often rooted in classical intent-based and slot-filling techniques. These systems were commonly anchored by discrete components for natural language understanding (NLU), dialogue state tracking (DST), and natural language generation (NLG), each fine-tuned for specific and narrowly defined objectives. However, such architectures often demonstrated brittleness and exhibited a lack of generalization capabilities, making them susceptible to semantic noise and syntactic perturbations.

Conversely, with the proliferation of large-scale transformer-based models such as ChatGPT, there has been a migration towards end-to-end trainable architectures, which offer more fluid, context-aware, and semantically rich interactions. Utilizing mechanisms like attention and memory, these models implicitly encode a more nuanced understanding of dialogue semantics without requiring explicit intent or entity recognition modules. Furthermore, they can generate text conditioned on extended conversational history, thereby augmenting the coherency and contextuality of dialogic exchanges.

Moreover, the pre-ChatGPT era was characterized by a certain proclivity towards handcrafted or rule-based dialogue management strategies. In stark contrast, ChatGPT and its kin leverage data-driven methodologies, fueled by gargantuan corpora that encapsulate a plethora of linguistic nuances and colloquialisms. This has engendered a democratization of sophisticated conversational capabilities, circumventing the need for labor-intensive feature engineering or domain-specific expert knowledge.

In terms of user experience, ChatGPT and similar models have broadened the ontological horizons of task-oriented dialogue. Earlier systems were highly specialized, excelling in constrained, transactional tasks but faltering when faced with more exploratory, open-ended dialogues. ChatGPT's expansive knowledge base and conversational dexterity enable a synthesis of transactional efficiency and contextual profundity, allowing for a more multimodal interaction schema that blends task-oriented and chit-chat dialogue strategies.

However, this shift is not without its attendant challenges, including ethical considerations surrounding data provenance and the potential for model-generated disinformation. Additionally, there is the computational overhead and resource-intensive nature of training and deploying these large-scale models, a factor that may engender a form of algorithmic elitism, marginalizing those without the requisite computational wherewithal.

In summation, the introduction of ChatGPT has engendered a seismic shift in the landscape of task-oriented dialogue systems, influencing both architectural paradigms and experiential dynamics. This transition necessitates a comprehensive reevaluation of established norms, algorithms, and evaluation criteria in light of the capabilities and complexities introduced by these advanced dialogue systems.

Integrating NLP tasks into an LLM doesn't necessarily guarantee a drop in accuracy to 85% from 90%. The impact depends on interactions between tasks, fine-tuning, model complexity, data quality, and task-specific challenges. Accurate evaluation across tasks is crucial to assess the overall effect.

In a large language model handling multiple NLP tasks, negative interactions can occur, including task interference, bias amplification, context confusion, and challenges in resource allocation. Careful engineering and fine-tuning strategies are needed to balance task-specific performance without compromising overall language understanding. Regular monitoring and adaptation help address these challenges.

Ahmad Al Khraisat "Profound thanks for your insights; your synthesis of epistemological nuances and empirical methodologies has significantly enriched the discourse here."

The real gain of large language models is their big capability. They can perform a wide range of tasks such as text generation, summarization, translation, and question answering. Few-shot learning is one of the benefits of large language models but not the only one. Large language models can learn from a few examples and generalize to new tasks and domains.

You can join our team.

e-mail: hilal. yagin@inonu.edu.tr

I will be happy to contribute.

Sure

Dear Yaniv Hozez ,

Quantum computing and artificial intelligence are both transformational technologies and artificial intelligence are likely to require quantum computing to achieve significant progress. Although artificial intelligence produces functional applications with classical computers, it is limited by the computational capabilities of classical computers.

Regards,

Shafagat

ChatGPT, including the advanced GPT-4 version, is an AI model that synthesizes information rather than producing it through original research or direct observation. Thus, it functions more like a secondary source. It's important to understand that this AI doesn't have beliefs, opinions, or firsthand experiences. Instead, it generates outputs based on the patterns it learned during its training on a diverse range of internet text.

However, the distinction between primary and secondary sources in research can depend on the context:

As a Secondary Source: In most cases, if you're using information directly from GPT-4, it's a secondary source. This is because GPT-4 compiles, interprets, analyzes, and synthesizes information it has learned from its training data, rather than providing original, firsthand data or evidence.

As a Primary Source: In some specific contexts, GPT-4 could potentially be considered a primary source. For instance, if you're conducting research on AI-generated text, the outputs of GPT-4 would be primary data because they are original products of the AI system you're studying. If you use GPT-4 to generate responses to research questionnaires, those responses could be considered primary data for your study on AI responses to these questionnaires.

Remember, though, that even when GPT-4 is treated as a primary source in these specific contexts, it is still generating responses based on patterns it learned from secondary data, not from its own experiences or original research.

As with all sources, it's crucial to use critical thinking when evaluating the information you receive from GPT-4, and to cross-reference it with other sources for accuracy.

Publishing your own created labeled corpus can be done through various avenues depending on your goals and the field you're working in. If you wish to contribute to the academic community and share your research findings, publishing it in the form of an article or paper in relevant journals or conference proceedings would be appropriate. This allows you to provide a detailed description of your corpus creation process, its applications, and potential insights derived from it. Alternatively, you could explore open-access platforms or repositories specific to linguistic resources, such as the Linguistic Data Consortium (LDC), where researchers can deposit and share their corpora. Additionally, if your corpus is of significant value and relevance, you may consider reaching out to organizations or institutions involved in language processing or research, as they may be interested in hosting and making it accessible to others in the field.

Thank you for your contribution Dr. Alexandru Ioan , what worries me is that if addiction is created, it open the door for a new human need, from one point of view it is a development with the merits of (Economic Growth-Improved Quality of Life-Technological Advancement), from the other point of view it is (Consumer Manipulation-Overconsumption and Waste-Shifting Priorities-Dependency)!

ensuring that the benefits outweigh the potential drawbacks and that ethical considerations are taken into account is crucial when creating a human need!

There are several interesting research topics in the domain of language modeling, natural language processing, deep learning, and machine learning that you can consider beyond data editing or data loading for LLM.

One area that you can explore is the use of transfer learning techniques for language modeling. This involves leveraging pre-trained models and fine-tuning them on specific tasks to achieve state-of-the-art results with limited amounts of training data. Another area of interest is the development of multi-task learning approaches for language modeling that can simultaneously learn to perform multiple related tasks such as text classification, named entity recognition, and sentiment analysis.

You could also investigate the use of attention mechanisms in language modeling, which can help models focus on specific parts of the input sequence and improve performance. Additionally, exploring ways to incorporate external knowledge sources, such as ontologies or knowledge graphs, into language models could be a promising avenue for improving their accuracy and efficiency.

Other potential research topics include exploring the use of generative models for language generation tasks, investigating the impact of data augmentation techniques on language modeling performance, and exploring novel architectures for language models that can better handle long-term dependencies and capture context.

In summary, there are many exciting research directions in the field of language modeling and natural language processing that you can pursue beyond data editing or loading for LLM.

There is DeBERTa by Microsoft which has some minor improvements over the vanilla BERT architecture, and which is the most similar to BERT and RoBERTa, as it is also purely encoder-based, and will likely give you slight improvements for many tasks. FLAN-T5 is also an excellent option, because, as an encoder-decoder model, you can use it in a pretty flexible manner to classify, translate and generate text. And, of course, there is this whole family of decoder-based generative models like GPT-J or Llamas which are aimed at text generation, but can obviously also be used for translation or classification.

Here are some natural language processing (NLP) journals with their impact factors, based on the latest available data:

Computational Linguistics: 2.50

Natural Language Engineering: 1.126

Journal of Natural Language Processing: 0.579

Natural Language and Linguistic Theory: 0.902

Computer Speech and Language: 2.551

It's important to note that impact factors can fluctuate over time and may not be the only factor to consider when evaluating the quality of a journal.

can you provide the website link sir

Some top-tier journals in Natural Language Processing:

- TACL

- Computational Linguistics

Please read my book

The American University Laboratories For Electrical Engineering Part 1

You can find a computer section.

Use something you find interesting.

Thank you

Ziad

John Lawton Mcbrayer I'm glad to see that you have shared your concerns about the need to be cautious when using new technology. You emphasized the importance of ethical principles and protecting patients' privacy, which are issues that are worth paying attention to. Before adopting new technology, we need to conduct in-depth research and comprehensive evaluations to ensure that it does not infringe on human rights and privacy. At the same time, we need to actively communicate with the public and establish transparency and trust, which are key factors in ensuring the smooth and successful application of technology.

In addition, we should embrace the development of technology and engage in thorough assumptions and validation throughout the process. The development of ChatGPT can undoubtedly reduce some repetitive work, such as office work. However, if it is used to assist scientific research, this issue needs further explanation.

Natural language processing has been minimally practiced in electoral data in central and eastern Europe. Academic research on social democratic parties in the region relies mostly on human-based content analysis and expert reviews; ideology and programs are usually only analyzed as part of a broader context and often using case studies or qualitative comparisons. Curry, Urban 2003; White, Lewis, Batt 2013; Bozóki, Ishiyama 2002; Hlouek, Kopeek 2016; Koubek, Poláek 2017; Kraovec, Cabada 2018 2018 It needs to draw on a general set of assumptions from the broader literature. (PDF) Natural language processing was applied to the electoral data of social democratic parties in Central and Eastern European countries. Available from: https://www.researchgate.net/publication/341659776_Application_of_Natural_Language_Processing_to_the_Electoral-Manifestos_of_Social-Democracy_Parties_in_Central-Eastern_European_Countries [Accessed March 23, 2023].

Kerry Sluchinski You might try the following user-friendly POS taggers/segmenters for Korean language data:

1. KoNLPy: KoNLPy is a Python module for Korean natural language processing. It features a POS tagger as well as numerous tools for Korean language processing. KoNLPy is straightforward and well-documented.

2. KOMORAN: KOMORAN is a Korean morphological analyzer and POS tagger that is free source. It is available as a command-line utility and as a Java library. For testing reasons, KOMORAN offers a user-friendly online interface.

3. Hannanum is a Korean morphological analyzer and POS tagger. It is a Java library that is built on a dictionary-based approach. Hannanum is simple to use and provides a user-friendly online interface for testing.

4. Korean Parser: Korean Parser is a dependency parser and part-of-speech tagger for Korean. It is written in Python and may be used as either a command-line utility or a Python library. Korean Parser is straightforward and well-documented.

5. Lingua-STS: Lingua-STS is a web-based tool for processing Korean language. It features a POS tagger as well as numerous tools for Korean language processing. Lingua-STS is simple to use and features an intuitive online interface.

These tools are all simple to use and may be used to separate Korean text data and conduct POS tagging.

Yes, we can combine NLP and social media with document retrieval to extract relevant information and insights from social media data using natural language processing techniques.

Dear Tong Guo ,

Machine learning (ML) for natural language processing (NLP) and text analytics involves using machine learning algorithms and “narrow” artificial intelligence (AI) to understand the meaning of text documents. These documents can be just about anything that contains text: social media comments, online reviews, survey responses, even financial, medical, legal and regulatory documents. In essence, the role of machine learning and AI in natural language processing and text analytics is to improve, accelerate and automate the underlying text analytics functions and NLP features that turn this unstructured text into useable data and insights.

Regards,

Shafagat

Kalpa Kalhara Sampath

Can you provide the link to reference 3 I did not find it either in google nor the Elsevier home for the journal

I want to check were the authors state:

since, adversarial attacks vary in nature and their underlying assumptions. Further, ML algorithms such as neural networks are designed to infer the underlying distribution. But their tolerance to underlying distribution shifts depends on the type of shift that the attacker makes(some will be compensated while other not). This ges to a principle in computer security which states that security is a process and not a software, program or device.

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Abderrahmane Boudribila Language translation is one example of a work where LSTMs may be preferable over Transformers. Because they can retain information about the original phrase in their memory cells, LSTMs are frequently employed for machine translation. This can be important for maintaining context and providing reliable translations.

Transformers, on the other hand, has proven to be quite effective in language creation tasks such as summarization, when the aim is to provide a short summary of a lengthy text. Transformers' self-attention processes enable the model to properly grasp long-term relationships in the input and output coherent text.

It is important to note that these are only a few instances, and there is no one-size-fits-all answer to which architecture is preferable for a specific purpose. The architecture used is frequently determined by the task's unique needs, accessible data, and computational resources. It is common practice to test both architectures to evaluate whether one performs better for a particular job.

Do you know https://towardsdatascience.com? There are a lot of samples here.

Dear Zhengang Zhang ,

Look the link, maybe useful.

Regards,

Shafagat

Dear Nazmul Hasan,

For your dissertation, I would like to propose the following research topic: The application of artificial intelligence in conjunction with Biga Data Analytics, multi-criteria simulation models and selected other Industry 4.0 technologies in credit risk and/or cybercrime risk management processes.

Warm regards,

Dariusz Prokopowicz

Seconding Jennifer Walsh Marr - Corpustool is the go-to for SFL. The automated analysis is not 100% but it at least gives you a first pass that you can then tidy. If you go to https://www.nasfla.org/links.html, there's a webinar that Michael Maune did on how to use corpustool.

Would it not be possible to integrate projects of your more senior team members and make focussed, small contributions, in that way starting to get familiar with academic publishing?

Dear Muhammad Owais ,

Integration and interdisciplinarity are the cornerstones of modern science and industry. One example of recent attempts to combine everything is the integration of computer vision and natural language processing (NLP). Both these fields are one of the most actively developing machine learning research areas. Yet, until recently, they have been treated as separate areas without many ways to benefit from each other. It is now, with the expansion of multimedia, researchers have started exploring the possibilities of applying both approaches to achieve one result.

Regards,

Shafagat

Nlp is a green place to start with (this is my opinion) there are more fields and topics to choose: Sentiment analysis, topic modeling, system recommendations, text generation, text summarization and etc.

Shafagat Mahmudova , Fredrick Ishengoma what am looking for mail conversations among B2B sales, not as a email list . Thanks for this

Sheikh Sadi Bandan N. Syed Siraj Ahmed Thank you for the information..Is there any article that you could suggest that I could refer to? I couldn't find more articles on this aspect.

Thanks

The main challenge is information overload, which poses a big problem to access a specific, important piece of information from vast datasets. Semantic and context understanding is essential as well as challenging for summarisation systems due to quality and usability issues.

GOOD LUCK

A good (free) place to get started is https://developer.ibm.com/technologies/natural-language-processing/tutorials/

Thank you. Dear Shima Shafiee

librosa is a python package for music and audio analysis. It provides the building blocks necessary to create music information retrieval systems.

Sajal Das Take a look:

1.

2. https://ieeexplore.ieee.org/document/9225565

3. https://github.com/shaoncsecu/BanglaEmotion

Chen Yijia Perplexity is the multiplicative inverse of the language model's probability assigned to the test set, normalized by the number of words in the test set. A language model is more accurate if it can anticipate unknown words from the test set, i.e., the P(a sentence from a test set) is the highest.

Have a look: https://towardsdatascience.com/how-to-evaluate-text-generation-models-metrics-for-automatic-evaluation-of-nlp-models-e1c251b04ec1

This depends hugely on your budget. if you're talking in the range of $4-5000 for the whole system then you'd be looking at consumer level GPUs like the RTX3080 or 90. The prices of these cards will probably drop considerably in the next few months. You'd also want to have a reasonable amount of RAM, say 64GB min, and also a good CPU. If you're talking $15-20000 you could get a nice server setup with multiple enterprise level GPUs. What framework would you be using?

Most of the SE based on Design and cost functions. Concentrate on

Dear Chen Yijia,

The main problem of architecture is not to generate a verbal model of the future transformation of the premises, consisting of symbols, signs and meanings connected by unique spatial relationships, but to translate them into a geometric characteristic architectural image.

Hi Abdus,

I would suggest you should check the performance of your model with and without duplication of records. Generally, the duplication may increase the biasedness of the data, which may lead to a biased model. To solve this you can use the data augmentation approach.

No problem Muhammedcan Pirinççi I am glad it helped you.

In my humble opinion, first, we should consider the difference between transfer learning and fine-tuning and then decide which one better fits our problem. In this regard, I found this link very informative and useful: https://stats.stackexchange.com/questions/343763/fine-tuning-vs-transferlearning-vs-learning-from-scratch#:~:text=Transfer%20learning%20is%20when%20a,the%20model%20with%20a%20dataset.

Afterward, when you decide which approach to use, there are tons of built-in functions and frameworks to do such for you. I am not sure if I understood your question completely, however, I tried to talk about it a little bit. If there is still something vague to you please don't hesitate to ask me.

Regards

Have a look at https://www.youtube.com/watch?v=He_ob8ydc9E

Sketch Engine is quite robust

Hi,

here is a link to an old paper of mine. It discusses pros and cons of different approaches up to 2010 or so.

Br, Kimmo

Bahadorreza Ofoghi , thanks for sharing, it looks interesting.

hi,

kindly rtefer this link:

Best wishes..

kindly refer this link:

Examples of self-supervised learning include future word prediction, mask word prediction in-painting, colorization, and super-resolution. Self-supervised learning is widely used in the field of NLP, i.e., Word2Vec, BERT, RoBERTa, ALBERT, etc. The CV uses contrastive learning or MAE methods to learn general representation.

You can see this link

https://www.kaggle.com/datasets/imdevskp/corona-virus-report

and this

Hi Hicham

Please have a look at the following GitHub repo:

I hope this helps.

Good luck!

The following link is also very useful: https://www.sussex.ac.uk/research/centres/ai-research-group/research/nlp

Dear Seif Maged . See the useful link: https://www.microsoft.com/en-us/research/uploads/prod/2021/10/kb_vlp_ICML2021.pdf

DEAR Matiam Essa

This text mining technique focuses on identifying the extraction of entities, attributes, and their relationships from semi-structured or unstructured texts. Whatever information is extracted is then stored in a database for future access and retrieval.The famous technique are:

With the increasing amount of text data, effective techniques need to be employed to examine the data and to extract relevant information from it. We have understood that various text mining techniques are used to decipher the interesting information efficiently from multiple sources of textual data and continually used to improve text mining process.

You may check it:

Artificial Intelligence An International Journal - Elsevier

Thanks Aparna Sathya Murthy

I hope this can help

Dear Dr Fatemeh Daneshfar . See the following useful RG link:

thanks Santhakumar Munuswamy

Hi,

You can do it easily with Python Pandas dataframe isna instruction, and you can remove the duplicated values using DataFrame.drop_duplicates ..

best of luck

Have a look at https://aclanthology.org/N18-3009v2.pdf

https://arxiv.org/ftp/arxiv/papers/1908/1908.04899.pdf and