Arabic NLP - Science topic

Yes, absolutely! Building an AI chatbot from scratch is an excellent way to dive deep into the world of Natural Language Processing (NLP). This hands-on experience will provide you with a comprehensive understanding of various NLP techniques and their practical applications.

By embarking on this journey, you'll gain valuable insights into the intricacies of NLP and position yourself for future advancements in the field.

To conduct a comparative evaluation, you may need to design experiments and benchmarks tailored to your specific use case and evaluation criteria. Additionally, consider consulting academic research papers, user reviews, and developer documentation to gather insights and perspectives on the performance of StanfordNLP CoreNLP and Elasticsearch default segmenter for Arabic text segmentation.

Please follow me if it's helpful. All the very best. Regards, Safiul

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.

That is interesting. I am fully occupied, I will share that with my colleagues and students, if you are still looking.

Dear Amin Honarmandi Shandiz , thank you for your contribution. It is very interesting paper. On the other hand, the Integration of Vision and Language Processing is only one part of the way to implementation into AI the understanding of meaning.

You can find it at: https://metatext.io/datasets

Antonio Moreno Sandoval and his team created an Arabic Medical Corpus. I'd suggest to contact them in case this resource is available for other researchers.

Many thanks for you reply, Edmond Furter

* “Mappings are arbitrary, anything can be said.” But limited by context, situation, convention, intelligence, ultimately archetype.

Yes, but there is no direct (algorithmic) relationship between environment and what can be said - that lens (your 'limitation') is provided by your interpretation - the 'world' you construct.

Daniel Chandler talks about whether language surrounds ideas (a cloak) or whether language supports ideas (a mould) . My software works on the principle that words are ideas, depending on that lens: the 'lens' is constructed with words (noting that - as Ogden and Richards point out - words don't 'mean' anything in themselves!).

There is one caveat, that we extend belief to the speaker until they say something negative - something we don't understand, or something that is contrary to our beliefs, at which point we need to express something ourselves - "now hold on/sorry, can you repeat that". This is known as 'felicity' by Austin, or 'adequacy' in the Ogden and Richards Triangle, and this directs the flow of conversation (which is how I model conversation in Enguage) I'm thinking of encoding it, "yes, ...", "ok, ...", "sorry, ..." etc, but it becomes stilted.

But I think this simple 'truth' is what Human Life Programming Laguage should really be looking for as mathematical pattern.

There are two methods which can help with overfitting. One is regularization. Is your output sigmoid? If so, activity regularization on the previous layer is helpful. Otherwise, kernel regularization might be a better bet.

The other common method is to add noise: Dropout to a binary layer, or GaussianDropout to a layer with continuous values. If you are using Keras, there is an option on the LSTM class to specify some dropout. The most commonly used value is 0.5, but I often use less.

You need to fix the seed number so that you can replicate the result each time you perform the task.

HTH.

Dr. Samer Sarsam

Thanks Yasser Abdelsatar ,

I will double-check.

Regards,

Refer to this paper, for various word embedding evaluation methods

For journals, except TALLIP, there is no dedicated one about Arabic NLP. There were some attempts years ago to propose one but it didn't succeed. You can check however these two journals where it is very common to see papers and even special issues about Arabic NLP

- Journal of King Saud University - Computer and Information Sciences

- Arabian Journal for Science and Engineering

For conferences, there is one dedicated regular conference about Arabic NLP since 2006 which is "International conference on Arabic Language Processing" ICALP. next edition will take place in Nancy in October 2019. There second more recent one is ACLing which takes place in Dubai. There is finally a regular workshop that takes place in conjunction with other known conference such EMNLP and ACL. The workshop is named WANLP. next edition will take place in Florence, July 2019.

Dear Mousa, there are around 6000 spoken languages in the world and most researches are doing nlp kind of research on their local language or national language , so what my point here is that iit is a responsibility of local researcher like you to take a lead and start serving your nation and whole humanity.

You can try this free Arabic NER tool here:

قتل is classified as positive with 100 confidence, this is the one I remember, I may give you some others when I get to the office

See These :

Good luck

You should look into the example of arff file in WEKA. WEKA includes examples in the directory where WEKA is installed.

An arff file contains two main parts:

1. Attributes

2. Feature values

To generate a arff file, which WEKA can read, you should:

1. Define attributes (feature name along its type)

2. Coding to generate the value of each feature and write it into a file.arff

Hope this helps.

A plausible semantic solution to your problem for the concerned example could be as follows:

Keep 3 bounded queues for each type of subjects: {male, female, objects}. Have an extensible mapping knowledge base to identify the which category a given subject fall under. E.g, names like Mary fall under female category, names like Mark fall under male, the rest as objects (places, animals, and other things).

You may keep the queue size for each category to be two. E.g, you could say "Mark was talking to Anthony. He told him that..." in here 'he' would refer to Mark and 'him' would refer to Anthony. For simplicity you may keep the queue size as one considering the simple case you have illustrated in the example.

Each time a pronoun is referenced, replace it accordingly with the names in the respective queues.

Each time a new subject is encountered, queue in the subject to the queue that matches the respective category (one of male, female or object).

The above solution would certainly work for the simple cases as the one described in the question. Accuracy for complex forms may vary. Complex forms would require more use of deductive systems. You may also use Turing Machines to process such tasks.