AI4Bhārat’s Post

Excited to share that our paper, "TransLSTM: A Hybrid LSTM-Transformer Model for Fine-grained Suggestion Mining" has been accepted for publication in the NLP journal! Our model outperforms existing methods by 6.76%, achieving an F1 score of 0.834 (SubTask A) and 0.881 (SubTask B) on the SemEval Task-9 dataset. Big thanks to Jaleed Khan and the team for their hard work and support! #AI #NLP #DeepLearning #Research https://lnkd.in/d7GaKci3

We're excited to announce the release of OpenDebateEvidence, a massive new dataset for argument mining and summarization. As pioneers in #intelligentagent technology, we understand the importance of high quality training data. There is a significant amount of research showing the value of argumentative techniques for improving performance of #AIagent systems. Pluralistic #AI systems which can deliberate in their pursuit of the truth can self-correct and reason in unique and novel ways. OpenDebateEvidence represents a major leap forward, providing over 3.5 million documents of real-world argumentative content. This rich dataset will be instrumental in developing agents that can reason, debate, and make persuasive arguments across a wide range of topics. Key highlights: - 25x larger than previous debate datasets - Comprehensive metadata for nuanced understanding - Proven performance gains for state-of-the-art language models At Wand AI, we're leveraging OpenDebateEvidence to enhance our AI agents' ability to: - Construct logical arguments - Summarize complex information - Engage in multi-step reasoning - Understand and respond to different perspectives This advancement brings us one step closer to intelligent AI agents that can truly think and act in the world. We're excited to see how researchers and developers will use this dataset to push the boundaries of AI. Read the full paper here: https://lnkd.in/g_ArzAVQ

How Text Mining is Revolutionizing Decision-Making🚀 Text data is everywhere - social media posts, emails, articles, and more! But how do you extract valuable insights from this unstructured mess? Text Mining is your secret weapon! 🔍⛏️ It's like a super-powered search engine that goes beyond keywords to uncover hidden patterns and trends. 💡Here's the question: What industry do you think could benefit most from Text Mining? Comment your answer below and let's discuss!💬 See full post below where we delve deeper into how Text Mining works, its applications across different fields, and free tools you can use to get started! 🔗 https://lnkd.in/d7saNY9A #TextMining #DataScience #AI #BusinessIntelligence #BigData #MachineLearning

𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝟐 𝐨𝐟 𝐀𝐫𝐚𝐛𝐞𝐫𝐭-𝐓𝐫𝐢𝐩𝐥𝐞𝐭-𝐌𝐚𝐭𝐫𝐲𝐨𝐬𝐡𝐤𝐚 𝐢𝐬 𝐧𝐨𝐰 𝐩𝐮𝐛𝐥𝐢𝐜! 🌟 I couldn't wait too long to train the #Matryoshka model on the fantastic dataset released by Mr. Abed Khooli. So, here it is – the 𝑨𝒓𝒂𝒃𝒆𝒓𝒕-𝑻𝒓𝒊𝒑𝒍𝒆𝒕-𝑴𝒂𝒕𝒓𝒚𝒐𝒔𝒉𝒌𝒂-𝑽2 model! [https://lnkd.in/dAgvTkBY] 🎉 This new version has once again achieved 𝐭𝐡𝐞 𝟏𝐬𝐭 𝐩𝐥𝐚𝐜𝐞 𝐨𝐧 𝐭𝐡𝐞 𝐌𝐓𝐄𝐁 𝐥𝐞𝐚𝐝𝐞𝐫𝐛𝐨𝐚𝐫𝐝! 🏆 This success builds upon Abed's 1M curated triplet dataset [https://lnkd.in/dWQb-wtt], which stems from the translated data of all NLI Triplet Dataset I initially translated with more data and editing. Together, we are making great strides in advancing Arabic NLP and setting new benchmarks for the field. Explore the model and join us in driving forward the future of Arabic NLP! https://lnkd.in/dAgvTkBY #ArabicNLP #MatryoshkaEmbeddings #SemanticSearch #AI #MachineLearning #MTEBLeaderboard #sentencetransformer

Lets start with why we need to learn text mining? Text is everywhere like books, facebook or twitter. Text data is growing each passing day. As I read on the internet, amount of text data will be approximately 40 zettabytes(10^21) in 2 years. We learn text mining to sentiment analysis, topic modelling, understanding, identfying, finding, classfying and extracting information. Content: Basic Text Mining Methods len() split() istitle() endswith() startswith() set() isupper() islower() isdigit() strip() find() rfind() replace() list() readline() read() splitlines() contains() Regular Expression Package re search() findall() Natural Language Process import nltk FreqDist Normalization and Stemming words Lemmatization Tokenization Text Classification Continue

Exciting developments in #MOF research using #AI! Prof. Dr. Omar Yaghi et al. show how #GPT4V can navigate and mine complex data from graphical sources with >93% accuracy, opening new possibilities in #reticular #chemistry. Read more: https://hubs.li/Q02H2z8d0

Great use case of using AI to discover rare minerals. It also highlights the importance of looking "beyond the hype" of Generative AI / Large Language Models and use most applicable AI models: in this case, predictive AI models designed to forecast areas with mineral enrichment. It is important to understand strengths and limitations of different AI models and use combination of AI techniques as applicable to task at hand. #artificialintelligence #airevolution

I totally agree with @Nitesh Singh, that organizations need a 'mindset shift' in order to start adopting AI into their systems. Change Management and coaching can add significant value in the field of AI, not only because it helps prepare people, mentally and emotionally for change but it also identifies gaps, provides training and facilitates adoption, through an entire process. When people do not understand the 'why, how, where, when and what' of change they go into freeze or flight mode. By meeting them at their specific point of uncertainty one is able to mitigate the resistance and help them face the change, courageously and even enthusiastically. Ignoring AI won't make it go away. AI needs to be elevated to a line on the business strategy of every company. It needs to be planned for, budgeted for and trained for. #AI #AICoach #AIChangeManagement #AIadoption #AIinbusiness

📊 Unearthing Insights: Text Mining in Social Media Picture a treasure trove not buried under earth, but hidden in plain sight within the vast conversations on social media. Text mining allows us to dig deep into this rich soil, extracting valuable insights that can shape businesses, influence public policy, and drive social change. 🔍 Decoding the Digital Chatter Every tweet, post, and comment is a strand of data that, when analyzed, reveals patterns and trends that are invisible to the naked eye. Text mining employs natural language processing to sift through these strands, identifying sentiments, trends, and public opinions. This process transforms random chatter into structured data ripe for analysis. 🌐 Impacting Real-world Decisions From understanding consumer preferences to gauging public reaction during crises, text mining is a powerful tool that offers real-time insights into the human psyche at a macro level. Businesses can tailor products, governments can respond more effectively to citizen needs, and NGOs can better align their initiatives with public sentiment. 💬 Let’s Discuss How do you think text mining social media can impact other sectors? What potential applications excite you the most? Share your thoughts or ask questions below—let's explore the possibilities together! #DataScience #MachineLearning #ArtificialIntelligence #BigDataAnalytics #StatisticalAnalysis #PredictiveModeling #PythonProgramming #DataVisualization #DeepLearning

Interested in cracking into literature mining, but unsure of where to start? My collaborators and I wrote this (hopefully) helpful paper where we tested out several classical, machine learning, and large language models for biological text mining (in this case, protein-protein interactions). We were most interested in understanding the behavior of each of these algorithms to provide suggestions to the community, especially to those unfamiliar with this field. We believe this work will be invaluable to the community and that it offers important insights. Special thanks to our PI Lisa Bramer, and to the two talented data scientists who helped me make this work a reality: SJ Kim and Clayton Strauch Read it here: