LEADER 04337oam 22005535 450 001 9910735382703321 005 20231208175501.0 010 $a1-4842-7386-9 024 7 $a10.1007/978-1-4842-7386-9 024 8 $a9781484273852 024 8 $a9781484273869 035 $a(CKB)5500000000157237 035 $a(MiAaPQ)EBC6845682 035 $a(Au-PeEL)EBL6845682 035 $a(OCoLC)1287894989 035 $a(OCoLC-P)1287894989 035 $a(DE-He213)978-1-4842-7386-9 035 $a(CaSebORM)9781484273869 035 $a(PPN)27225908X 035 $a(EXLCZ)995500000000157237 100 $a20220124d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNatural language processing projects $ebuild next -generation NLP applications using AI techniques /$fAkshay Kulkarni, Adarsha Shivananda, Anoosh Kulkarni 205 $a1st ed. 2022. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2022. 215 $a1 online resource (327 pages) 300 $aIncludes index. 311 0 $a1-4842-7385-0 327 $aChapter 1: Natural Language Processing and Artificial Intelligence Overview -- Chapter 2: Product360 - Sentiment and Emotion Detector -- Chapter 3: TED Talks Segmentation and Topics Extraction Using Machine Learning -- Chapter 4: Enhancing E-commerce Using an Advanced Search Engine and Recommendation System -- Chapter 5: Creating a Resume Parsing, Screening, and Shortlisting System -- Chapter 6: Creating an E-commerce Product Categorization Model Using Deep Learning -- Chapter 7: Predicting Duplicate Questions in Quora -- Chapter 8: Named Entity Recognition Using CRF and BERT -- Chapter 9: Building a Chatbot Using Transfer Learning -- Chapter 10: News Headline Summarization -- Chapter 11: Text Generation - Next Word Prediction -- Chapter 12: Conclusion and Future Trends. 330 $aLeverage machine learning and deep learning techniques to build fully-fledged natural language processing (NLP) projects. Projects throughout this book grow in complexity and showcase methodologies, optimizing tips, and tricks to solve various business problems. You will use modern Python libraries and algorithms to build end-to-end NLP projects. The book starts with an overview of natural language processing (NLP) and artificial intelligence to provide a quick refresher on algorithms. Next, it covers end-to-end NLP projects beginning with traditional algorithms and projects such as customer review sentiment and emotion detection, topic modeling, and document clustering. From there, it delves into e-commerce related projects such as product categorization using the description of the product, a search engine to retrieve the relevant content, and a content-based recommendation system to enhance user experience. Moving forward, it explains how to build systems to find similar sentences using contextual embedding, summarizing huge documents using recurrent neural networks (RNN), automatic word suggestion using long short-term memory networks (LSTM), and how to build a chatbot using transfer learning. It concludes with an exploration of next-generation AI and algorithms in the research space. By the end of this book, you will have the knowledge needed to solve various business problems using NLP techniques. You will: Implement full-fledged intelligent NLP applications with Python Translate real-world business problem on text data with NLP techniques Leverage machine learning and deep learning techniques to perform smart language processing Gain hands-on experience implementing end-to-end search engine information retrieval, text summarization, chatbots, text generation, document clustering and product classification, and more. 606 $aArtificial intelligence 606 $aNatural language processing (Computer science) 615 0$aArtificial intelligence. 615 0$aNatural language processing (Computer science) 676 $a006.35 700 $aKulkarni$b Akshay$01376507 702 $aShivananda$b Adarsha 702 $aKulkarni$b Anoosh 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910735382703321 996 $aNatural language processing projects$93413734 997 $aUNINA