مرصد حوسبة العربيَّة لعام (٢٠٢٥م) مرجعك في مستجدَّات الحوسبة العربيَّة
Welcome to the First Edition of the Arabic NLP Observatory
Highlights of the Summaries
Chapters of the Report
The results showed that 56% of the participants classified the availability of data as insufficient. These results indicate that the majority of participants find the available data either inadequate or difficult to access, which creates an obstacle to the progress of their research or industrial projects and limits the ability to develop effective solutions in the field of Arabic natural language processing.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
The results showed that 56% of the participants classified the availability of data as insufficient. These results indicate that the majority of participants find the available data either inadequate or difficult to access, which creates an obstacle to the progress of their research or industrial projects and limits the ability to develop effective solutions in the field of Arabic natural language processing.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The results showed that 56% of the participants classified the availability of data as insufficient. These results indicate that the majority of participants find the available data either inadequate or difficult to access, which creates an obstacle to the progress of their research or industrial projects and limits the ability to develop effective solutions in the field of Arabic natural language processing.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The results showed that 56% of the participants classified the availability of data as insufficient. These results indicate that the majority of participants find the available data either inadequate or difficult to access, which creates an obstacle to the progress of their research or industrial projects and limits the ability to develop effective solutions in the field of Arabic natural language processing.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The results showed that 56% of the participants classified the availability of data as insufficient. These results indicate that the majority of participants find the available data either inadequate or difficult to access, which creates an obstacle to the progress of their research or industrial projects and limits the ability to develop effective solutions in the field of Arabic natural language processing.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The results showed that 56% of the participants classified the availability of data as insufficient. These results indicate that the majority of participants find the available data either inadequate or difficult to access, which creates an obstacle to the progress of their research or industrial projects and limits the ability to develop effective solutions in the field of Arabic natural language processing.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The results showed that 56% of the participants classified the availability of data as insufficient. These results indicate that the majority of participants find the available data either inadequate or difficult to access, which creates an obstacle to the progress of their research or industrial projects and limits the ability to develop effective solutions in the field of Arabic natural language processing.
The results also show that nearly 37% of participants believe that the tools do not meet the needs, while about 52% reported that the digital infrastructure does not support the requirements of their projects. Furthermore, 71% of participants acknowledged that research collaboration is ineffective. This assessment highlights a gap in collaborative efforts between the academic and industrial sectors, which leads to delays in translating research into practical applications.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The findings further reveal that around 25% of participants are satisfied with the quality of published research in this field, while nearly 78% noted that financial support and funding are insufficient. This was the highest-rated challenge, indicating that funding constitutes a major obstacle to progress in this area. The evaluation also shows that about 40% of participants are dissatisfied with the level of human capacity in Arabic NLP, pointing to a gap in the linguistic and technical skills required to support Arabic NLP projects.
Although 38% of participants relatively share their expertise and knowledge, this contribution is still insufficient to achieve the desired level of knowledge exchange, underscoring the need to strengthen efforts to increase both the scale and quality of knowledge sharing among specialists.
The 2024 Hype Cycle reflects the developmental trajectory of Arabic language processing technologies, from innovation to practical application. The field experienced high expectations, followed by technical challenges, and then stabilization of applicable solutions. Technologies such as dialect recognition and efficient resource processing emerged prominently, with expectations peaking following the launch of nine large language models, before a reassessment due to challenges such as cultural bias.
Nevertheless, some successful technologies have stabilized, such as spell checking and speech recognition, while others, such as specialized models and dialect-to-dialect translation, have reached a productivity stage. This reflects the maturity of the field and opens new opportunities. Despite this progress, challenges remain that require further research to ensure the accuracy and effectiveness of these technologies, including increasing labeled data, addressing linguistic and cultural bias in models, supporting dialects in tools, establishing ethical guidelines to protect data, and improving infrastructure.
