Natural language and machine translation Books

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

a huge range and FREE tracked UK delivery on ALL orders.

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisBuild smart applications by implementing real-world artificial intelligence projectsKey Features Explore a variety of AI projects with Python Get well-versed with different types of neural networks and popular deep learning algorithms Leverage popular Python deep learning libraries for your AI projects Book DescriptionArtificial Intelligence (AI) is the newest technology that’s being employed among varied businesses, industries, and sectors. Python Artificial Intelligence Projects for Beginners demonstrates AI projects in Python, covering modern techniques that make up the world of Artificial Intelligence.This book begins with helping you to build your first prediction model using the popular Python library, scikit-learn. You will understand how to build a classifier using an effective machine learning technique, random forest, and decision trees. With exciting projects on predicting bird species, analyzing student performance data, song genre identification, and spam detection, you will learn the fundamentals and various algorithms and techniques that foster the development of these smart applications. In the concluding chapters, you will also understand deep learning and neural network mechanisms through these projects with the help of the Keras library.By the end of this book, you will be confident in building your own AI projects with Python and be ready to take on more advanced projects as you progressWhat you will learn Build a prediction model using decision trees and random forest Use neural networks, decision trees, and random forests for classification Detect YouTube comment spam with a bag-of-words and random forests Identify handwritten mathematical symbols with convolutional neural networks Revise the bird species identifier to use images Learn to detect positive and negative sentiment in user reviews Who this book is forPython Artificial Intelligence Projects for Beginners is for Python developers who want to take their first step into the world of Artificial Intelligence using easy-to-follow projects. Basic working knowledge of Python programming is expected so that you’re able to play around with codeTable of ContentsTable of Contents Building Your Own Prediction Models Prediction with Random Forests Application for comment classification Neural Networks Deep Learning

Read more less

Book SynopsisBuild end-to-end industrial-strength NLP models using advanced morphological and syntactic features in spaCy to create real-world applications with easeKey Features Gain an overview of what spaCy offers for natural language processing Learn details of spaCy's features and how to use them effectively Work through practical recipes using spaCy Book DescriptionspaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy's features and real-world applications.You'll begin by installing spaCy and downloading models, before progressing to spaCy's features and prototyping real-world NLP apps. Next, you'll get familiar with visualizing with spaCy's popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you'll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow's Keras API together with spaCy. You'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results.By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.What you will learn Install spaCy, get started easily, and write your first Python script Understand core linguistic operations of spaCy Discover how to combine rule-based components with spaCy statistical models Become well-versed with named entity and keyword extraction Build your own ML pipelines using spaCy Apply all the knowledge you've gained to design a chatbot using spaCy Who this book is forThis book is for data scientists and machine learners who want to excel in NLP as well as NLP developers who want to master spaCy and build applications with it. Language and speech professionals who want to get hands-on with Python and spaCy and software developers who want to quickly prototype applications with spaCy will also find this book helpful. Beginner-level knowledge of the Python programming language is required to get the most out of this book. A beginner-level understanding of linguistics such as parsing, POS tags, and semantic similarity will also be useful.Table of ContentsTable of Contents Getting Started with spaCy Core Operations with spaCy Linguistic Features Rule-Based Matching Working with Word Vectors and Semantic Similarity Putting Everything Together: Semantic Parsing with spaCy Customizing spaCy Models Text Classification with spaCy spaCy and Transformers Putting Everything Together: Designing Your Chatbot with spaCy

Read more less

Book SynopsisBuild cutting edge machine and deep learning systems for the lab, production, and mobile devices.Purchase of the print or Kindle book includes a free eBook in PDF format.Key Features Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples Implement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learning Learn cutting-edge machine and deep learning techniques Book DescriptionDeep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.What you will learn Learn how to use the popular GNNs with TensorFlow to carry out graph mining tasks Discover the world of transformers, from pretraining to fine-tuning to evaluating them Apply self-supervised learning to natural language processing, computer vision, and audio signal processing Combine probabilistic and deep learning models using TensorFlow Probability Train your models on the cloud and put TF to work in real environments Build machine learning and deep learning systems with TensorFlow 2.x and the Keras API Who this book is forThis hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems.Some machine learning knowledge would be useful. We don't assume TF knowledge.Table of ContentsTable of Contents Neural Networks Foundations with TF Regression and Classification Convolutional Neural Networks Word Embeddings Recurrent Neural Network Transformers Unsupervised Learning Autoencoders Generative Models Self-Supervised Learning Reinforcement Learning Probabilistic TensorFlow An Introduction to AutoML The Math Behind Deep Learning Tensor Processing Unit Other Useful Deep Learning Libraries Graph Neural Networks Machine Learning Best Practices TensorFlow 2 Ecosystem Advanced Convolutional Neural Networks

Read more less

Book SynopsisOpenAI’s GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Improve your productivity with OpenAI’s ChatGPT and GPT-4 from prompt engineering to creating and analyzing machine learning models Pretrain a BERT-based model from scratch using Hugging Face Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data Book DescriptionTransformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using DALL-E 2, ChatGPT, and GPT-4. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective.What you will learn Discover new techniques to investigate complex language problems Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3 Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E Learn the mechanics of advanced prompt engineering for ChatGPT and GPT-4 Who this book is forIf you want to learn about and apply transformers to your natural language (and image) data, this book is for you. You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community to help guide you on your transformers journey!Table of ContentsTable of Contents What are Transformers? Getting Started with the Architecture of the Transformer Model Fine-Tuning BERT Models Pretraining a RoBERTa Model from Scratch Downstream NLP Tasks with Transformers Machine Translation with the Transformer The Rise of Suprahuman Transformers with GPT-3 Engines Applying Transformers to Legal and Financial Documents for AI Text Summarization Matching Tokenizers and Datasets Semantic Role Labeling with BERT-Based Transformers Let Your Data Do the Talking: Story, Questions, and Answers Detecting Customer Emotions to Make Predictions Analyzing Fake News with Transformers Interpreting Black Box Transformer Models From NLP to Task-Agnostic Transformer Models The Emergence of Transformer-Driven Copilots The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4' Appendix I — Terminology of Transformer Models Appendix II — Hardware Constraints for Transformer Models Appendix III — Generic Text Completion with GPT-2 Appendix IV — Custom Text Completion with GPT-2 Appendix V — Answers to the Questions

Read more less

Book SynopsisHarness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAI Purchase of the print or Kindle book includes a free PDF eBook Key Features Explore the theory behind generative AI models and the road to GPT3 and GPT4 Become familiar with ChatGPT’s applications to boost everyday productivity Learn to embed OpenAI models into applications using lightweight frameworks like LangChain Book DescriptionGenerative AI models and AI language models are becoming increasingly popular due to their unparalleled capabilities. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. You’ll start with an introduction to the field of generative AI, helping you understand how these models are trained to generate new data. Next, you’ll explore use cases where ChatGPT can boost productivity and enhance creativity. You’ll learn how to get the best from your ChatGPT interactions by improving your prompt design and leveraging zero, one, and few-shots learning capabilities. The use cases are divided into clusters of marketers, researchers, and developers, which will help you apply what you learn in this book to your own challenges faster. You’ll also discover enterprise-level scenarios that leverage OpenAI models’ APIs available on Azure infrastructure; both generative models like GPT-3 and embedding models like Ada. For each scenario, you’ll find an end-to-end implementation with Python, using Streamlit as the frontend and the LangChain SDK to facilitate models' integration into your applications. By the end of this book, you’ll be well equipped to use the generative AI field and start using ChatGPT and OpenAI models’ APIs in your own projects.What you will learn Understand generative AI concepts from basic to intermediate level Focus on the GPT architecture for generative AI models Maximize ChatGPT’s value with an effective prompt design Explore applications and use cases of ChatGPT Use OpenAI models and features via API calls Build and deploy generative AI systems with Python Leverage Azure infrastructure for enterprise-level use cases Ensure responsible AI and ethics in generative AI systems Who this book is forThis book is for individuals interested in boosting their daily productivity; businesspersons looking to dive deeper into real-world applications to empower their organizations; data scientists and developers trying to identify ways to boost ML models and code; marketers and researchers seeking to leverage use cases in their domain – all by using Chat GPT and OpenAI Models. A basic understanding of Python is required; however, the book provides theoretical descriptions alongside sections with code so that the reader can learn the concrete use case application without running the scripts.Table of ContentsTable of Contents Introduction to Generative AI OpenAI and ChatGPT – Beyond the Market Hype Getting Familiar with ChatGPT Understanding Prompt Design Boosting Day-to-Day Productivity with ChatGPT Developing the Future with ChatGPT Mastering Marketing with ChatGPT Research Reinvented with ChatGPT OpenAI and ChatGPT for Enterprises – Introducing Azure OpenAI Trending Use Cases for Enterprises Epilogue and Final Thoughts

Read more less

Book SynopsisGet to grips with the LangChain framework from theory to deployment and develop production-ready applications. Code examples regularly updated on GitHub to keep you abreast of the latest LangChain developments. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LLMs’ capabilities and work around their inherent weaknesses Delve into the realm of LLMs with LangChain and go on an in-depth exploration of their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Bard. It also demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn Understand LLMs, their strengths and limitations Grasp generative AI fundamentals and industry trends Create LLM apps with LangChain like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is forThe book is for developers, researchers, and anyone interested in learning more about LLMs. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs and are looking to stay ahead of the curve in the LLMs and LangChain arena. Basic knowledge of Python is a prerequisite, while some prior exposure to machine learning will help you follow along more easily.Table of ContentsTable of Contents What Is Generative AI? LangChain for LLM Apps Getting Started with LangChain Building Capable Assistants Building a Chatbot like ChatGPT Developing Software with Generative AI LLMs for Data Science Customizing LLMs and Their Output Generative AI in Production The Future of Generative Models

Read more less

Book SynopsisThis book constitutes the refereed proceedings of the 10th International Conference on Computational Data and Social Networks, CSoNet 2021, which was held online during November 15-17, 2021. The conference was initially planned to take place in Montreal, Quebec, Canada, but changed to an online event due to the COVID-19 pandemic. The 24 full and 8 short papers included in this book were carefully reviewed and selected from 57 submissions. They were organized in topical sections as follows: Combinatorial optimization and learning; deep learning and applications to complex and social systems; measurements of insight from data; complex networks analytics; special track on fact-checking, fake news and malware detection in online social networks; and special track on information spread in social and data networks. Table of ContentsCombinatorial Optimization and Learning.- Streaming algorithms for maximizing non-submodular functions on the integer lattice.- Causal Inference for Influence Propagation --- Identifiability of the In-dependent Cascade Model.- Streaming algorithms for Budgeted $k$-Submodular Maximization problem.- Approximation algorithms for the lower bounded correlation clustering problem.- Approximation Algorithm for Maximizing Nonnegative Weakly Mono-tonic Set Functions.- Differentially Private Submodular Maximization over Integer Lattice.- Maximizing the sum of a supermodular function and a monotone DR-submodular function subject to a knapsack constraint on the integer lattice.- Deep Learning and Applications to Complex and Social Systems.- A Framework for Accelerating Graph Convolution Networks on Massive Datasets.- AdvEdge: Optimizing Adversarial Perturbations against Interpretable Deep Learning.- Incorporating Transformer Models for Sentiment Analysis and News Classification in Khmer.- Deep Bangla Authorship Attribution using Transformer Models.- A Deep Learning Based Traffic Sign Detection for Intelligent Transportation Systems.- Detecting Hate Speech Contents Using Embedding Models.- MIC Model for Cervical Cancer Risk Factors Deep Association Analysis.- Power Grid Cascading Failure Prediction Based on Transforme.- Measurements of Insight from Data.- Security Breaches in the Healthcare Domain: A Spatiotemporal Analysis.- Social and Motivational Factors for the Spread of Physical Activities in a Health Social Network.- Understanding the Issues Surrounding COVID-19 Vaccine Roll Out Via User Tweets.- Complex Networks Analytics.- Minimize Travel Time with Traffic Flow Density Equilibrium on Road Network.- Network based Framework to Compare Vaccination Strategies.- Groups Influence with Minimum Cost in Social Network.- Recovering communities in temporal networks using persistent edges.- Community Detection using Semilocal Topological Features and Label Propagation Algorithm.- Twitter Analysis of Covid-19 Misinformation in Spain.- Comparing Community-aware Centrality Measures in Online Social Networks.- Two-Tier Cache-Aided Full-Duplex Content Delivery in Satellite-Terrestrial Networks.- Special Track: Fact-Checking, Fake News and Malware Detection in Online Social Networks.- Mean User-Text Agglomeration (MUTA): Practical User Representation and Visualization for Detection of Online Influence Operations.- The Role of Information Organization and Knowledge Structuring in Combatting Misinformation: A Literary Analysis.- Fake News Detection using LDA Topic Modelling and K-Nearest Neighbor Classifier.- Special Track: Information Spread in Social and Data Networks.- Summarization Algorithms for News: a Study of the Coronavirus Theme and its Impact on the News Extracting Algorithm.- Social cohesion during stay-at-home phase during the first wave of COVID-19 in Poland.- Influence and Activation Thresholds Target Set Selection within Community Structure.

Read more less

Book SynopsisThis book evaluates the impact of relevant factors affecting the results of speech quality assessment studies carried out in crowdsourcing. The author describes how these factors relate to the test structure, the effect of environmental background noise, and the influence of language differences. He details multiple user-centered studies that have been conducted to derive guidelines for reliable collection of speech quality scores in crowdsourcing. Specifically, different questions are addressed such as the optimal number of speech samples to include in a listening task, the influence of the environmental background noise in the speech quality ratings, as well as methods for classifying background noise from web audio recordings, or the impact of language proficiency in the user perception of speech quality. Ultimately, the results of these studies contributed to the definition of the ITU-T Recommendation P.808 that defines the guidelines to conduct speech quality studies in crowdsourcing.Table of Contents1. Introduction.2. Related Work.3. Method.4. Test Structure.5. Impact of Background Noise.6. Influence of Language.7. Conclusion.

Read more less

Book SynopsisPreface.- Acknowledgements.- Chapter 1. Introduction.- Chapter 2. BERT and Broad Learning for Textual Emotion Classification.- Chapter 3. Cascading Broad Learning for Textual EmotionClassification.- Chapter 4. Dual Broad Learning for Textual Emotion Classification.- Chapter 5. Single-source Domain Adaptation for Emotion Classification Using CNN-Based Broad Learning.- Chapter 6. Multi-source Domain Adaptation for Emotion Classification Using Bi-LSTM-Based Broad Learning. Chapter 7. Emotion Classification in Textual Conversations Using Deep Broad Learning.- Chapter 8. Rational Graph Attention Network and Broad Learning for Emotion Classification in TextualConversations.- Chapter 9. Summary and Outlook.

Read more less

Book Synopsis.-Soccer-GraphRAG: Applications of GraphRAG in Soccer..- Enhanced Semantic Understanding with Graph-based Information Retrieval..- Identifying Shopping Intent in Product QA for Proactive Recommendations..- KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering..- The Effectiveness of Graph Contrastive Learning on Mathematical Information Retrieval..- The Impact of Source-Target Node Distance on Vicious Adversarial Attacks in Social Network Recommendation Systems.

Read more less

Book SynopsisChapter 1 Introduction to Entity Discovery and Linking.- Chapter 2 Knowledge Bases, Datasets, and Evaluation.- Chapter 3 Overview of Entity Discovery and Linking Pipeline.

Read more less

Book Synopsis.- Learning Arabic and dialectal and Sentiment Analysis..- Sentiment Analysis on Moroccan Dialect of Arabic combining NLP and ML methods..- Enhancing Arabic Sentiment Analysis using Arabic LLMs..- Sentiment analysis of texts written in Arabic: Addressing the issue of negation..- Automatic Arabic Essays Scoring: A Scoping Review..- Bridging the Emotional Gap: Google NL-AI Sentiment Analysis in Comparing Moroccan Literary Translations..- HateTune: Tunisian Dialect Hate Speech Detection Dataset..- DarijaGenie: Learning Moroccan Arabic Through a Multimodal Chatbot..- Morpho-lexical based approach for Arabic WorldNet extension..- A Hybrid Annotation Model for Arabic Argumentative Debate Corpus..- Alkhalil Platform for Arabic Language Processing..- Advancements in Deep Learning for Arabic Language Processing : Generation, Translation, and QA..- Exploring semantic Hadith Overlap across Topics..- Stance Detection in Arabic Dialects: Preliminary Experiments..- MAGENTA: Generating and Detecting Arabic Machine-Generated Text in Multiple Domains..- Transformers and Spark for Automated CV Classification in Arabophone regions..- Fine-Tuning AraBART on AHS dataset for Arabic Abstractive Summarization..- Qur'an Passage Ranking Using Transformer Models..- Question Answering Over the Arabic Hadith Sharif Using Transformer Models..- Deep Reinforcement Learning for Arabic Machine Translation: A Study on Reward Signals..- ARAP-IRONY: a Multi-dialectal Arabic Irony Corpus for Irony Detection..- Context-aware Arabic Diacritization using Transformers..- Classifying Persuasion Modes in Arabic Debates: A Preliminary Language Model-based Analysis..- Strengthening deep learning through morphological analysis for an Arabic Lemmatizer development..- Enhancing Arabic Word Sense Disambiguation with Ensemble BERT-based Models.

Read more less

Book Synopsis.- Linguistic Resources for Arabic NLP..- CAIV: Corpus of Arabic Infinitive Verbs..- MAOffens: Moroccan Arabic Offensive Language Dataset..- Exploring the Impact of Stop Words and Particles on Arabic Word Sense Disambiguation..- The Contribution of Corpus Linguistics and Natural Language Processing Tools in the Development of School Lexicography..- Opportunities to Use Arabic YouTube Comments in Text Mining and Masked Language Modeling..- Arabic Dialect Audio Identification Using Wav2Vec2..- Corpus Analysis of Coronavirus Disease-19 (COVID-19)-Related Loneliness in Twitter..- Compiling a Bilingual Lexicon Using a Semi-Automatic Approach..- BOUTEF: Bolstering Our Understanding Through an Elaborated Fake News Corpus..- Various analysis of Arabic..- Image Generation from Arabic Text: Comparative Study of Proposed Architectures..- Enhancing Arabic handwritten recognition system based CNN-BLSTM using Generative Adversarial Networks..- An Intention-Driven Architecture for Context-Aware Systems..- A Review of Arabic Fake News Detection Approaches on Social Media..- Creating a Multilingual Dataset in Arabic and Croatian from Sports Videos through a Data Processing Pipeline Combining ASR and MT..- Exploring the Impact of Natural Language Processing Technologies on Academic Performance and Student Engagement in Higher Education..- Deep Facial Expression Recognition Using Xception Model..- A New Meta-heuristic OptimizationTechnique for Solving Feature Selection and Classification Problems for Arabic Text..- Detecting Fake News: Exploring Key Features in Multilingual Arabic Dialect Corpus.

Read more less

Book SynopsisVagueness and the Connectives.- A Probabilistic Logic for Causal Counterfactuals.- Disjunction, Juxtaposition, and Alternative Questions in Mandarin.- Disjunctions of Universal Modals and Conditionals.- Questions and Connectives.- Variation in Conditional Perfection: A Comparative Study of English/German Versus Mandarin.- Disjunction and Parentheses.- Negation, Disjunction, and Choice Questions: Reflections on Yuen Ren Chao on Chinese Logical Expressions.

Read more less

Book Synopsis.- Understanding and Designing for Human-AI Interactions..- Analyzing Patterns of Conversational Breakdown in Human-Chatbot Customer Service Conversations..- Selecting Empathic Response Headers in Customer Support Conversations with LLM-based Emotion Recognition..- Exploring the Effects of Consistency-based Hallucination Detection for LLM-based QA Chatbots: A Simulation Study..- LadderChat – An LLM-based Conversational Agent for Laddering Interviews..- Can Machine Learning Models Recognise Emotions, Particularly Neutral, Better Than Humans?..- Human-Centred AI in Education and Social Support..- An AI-Powered Learning Companion for Adaptive and Personalized STEM Educatio..- Development and Evaluation of a University Chatbot Using Deep Learning: A RAG-Based Approach..- A Voice-Enabled Intelligent Virtual Agent for People with Memory Impairments: Thematic Analysis of Focus Group Results..- The BookBot Project: Conceptual Design of a Social Robot Facilitating Reading Motivation..- Questions people ask ChatGPT regarding their romantic relationships and what they think about the provided answers: An exploratory study..- Conversational AI for Citizens and Customers..- AI-Driven Dialogue: Leveraging Generative AI in Conversational Agent Voting Advice Applications (CAVAA)..- First Aid for Europe – A Study on the Impact of Digital Voting Assistants on Young Adults During the Elections for the European Parliament in 2024..- An Analysis of Federal and Municipal Chatbots in Germany..- LLM-powered Conversational AI in Customer Service: Users’ Expectations and Anticipated Use..- Feeling Understood by AI: How Empathy Shapes Trust and Influences Patronage Intentions in Conversational AI.

Read more less

Book Synopsis.- Crossing the Structure Chasm – Querying Data Without Limits..- Understanding the Interplay between LLMs’ Utilisation of Parametric and Contextual Knowledge..- Knowledge Graphs Are Dead, Long Live Knowledge Graphs..- LIBRA: Measuring Bias of Large Language Model from a Local Context..- Embedding Cultural Diversity in Prototype-based Recommender Systems..- Is Relevance Propagated from Retriever to Generator in RAG?..- Measuring Actual Privacy of Obfuscated Queries in Information Retrieval..- One size doesn’t fit all: Predicting the Number of Examples for In-Context Learning..- MURR: Model Updating with Regularized Replay for Searching a Document Stream..- Token Pruning Optimization for Efficient Dense Retrieval with Multi-Vector Representations..- Advancing Math Formula Search Using Diverse Structural and Symbolic Representations..- Ragnar¨ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track..- Retrieve, Annotate, Evaluate, Repeat: Leveraging Multimodal LLMs for Large-Scale Product Retrieval Evaluation..- Graph Representation of Tables+Text and Compact Subgraph Retrieval for QA Tasks..- Higher Order Knowledge Graph Embeddings..- Improving the Re-Usability of Conversational Search Test Collections..- Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next Basket Recommendation..- Guiding Retrieval using LLM-based Listwise Rankers..- Lost but Not Only in the Middle: Positional Bias in Retrieval Augmented Generation..- Biased PromptORE: Enhancing Relation Extraction in Gendered Languages and Complex Texts – The Case of Spanish Documents from the XVI Century..- LSTM-based Selective Dense Text Retrieval Guided by Sparse Lexical Retrieval..- Context Example Selection For LLM Generated Relevance Assessments..- Enhancing FEVER-Style Claim Fact-Checking Against Wikipedia: A Diagnostic Taxonomy and Generative Framework..- Evaluating Auto-complete Ranking for Diversity and Relevance..- Semantically Proportioned nDCG for Explaining ColBERT’s Learning Process..- Opt-in Transparent Fairness for Recommender Systems..- Malevolence Attacks Against Pretrained Dialogue Models..- Zero-Shot and Efficient Clarification Need Prediction in Conversational Search..- Decoding the Hierarchy: A Hybrid Approach to Hierarchical Multi-Label Text Classification..- A Multi-modal Recipe for Improved Multi-domain Recommendation..- Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a Baseline..- Corpus Subsampling: Estimating the Effectiveness of Neural Retrieval Models on Large Corpora.

Read more less

Book Synopsis.- Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders..- Patent Figure Classification using Large Vision-language Models..- E!cient Session Retrieval Using Topical Index Shards..- Feature Attribution Explanations of Session-based Recommendations..- Evaluating Sequential Recommendations in the Wild: A Case Study on offine Accuracy, Click Rates, and Consumption..- Graph-Convolutional Networks: Named Entity Recognition and Large Language Model Embedding in Document Clustering..- Exploring the relationship between listener receptivity and source of music recommendations..- News Without Borders: Domain Adaptation of Multilingual Sentence Embeddings for Cross-lingual News Recommendation..- Maybe you are looking for CroQS: Cross-modal Query Suggestion for Text-to-Image Retrieval..- Evaluating LLM Abilities to Understand Tabular Electronic Health Records: A Comprehensive Study of Patient Data Extraction and Retrieval..- MVAM: Multi-View Attention Method for Fine-grained Image-Text Matching..- An Investigation of Prompt Variations for Zero-shot LLM-based Rankers..- Query Performance Prediction using Dimension Importance Estimators..- Uncertainty Estimation in the Real World: A study on Music Emotion Recognition..- Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models..- Semi-Supervised Image-Based Narrative Extraction: A Case Study with Historical Photographic Records..- LLM is Knowledge Graph Reasoner: LLM’s Intuition-aware Knowledge Graph Reasoning for Cold-start Sequential Recommendation..- PEIR: Modeling Performance in Neural Information Retrieval..- mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval..- Leveraging Retrieval-Augmented Generation for Keyphrase Synonym Suggestion..- Can Large Language Models E#ectively Rerank News Articles for Background Linking?..- OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System..- CUP: a Framework for Resource-E!cient Review-Based Recommenders..- Towards E!cient and Explainable Hate Speech Detection via Model Distillation..- Visual Latent Captioning - Towards Verbalizing Vision Transformer Encoders..- On the Robustness of Generative Information Retrieval Models: An Out-of-Distribution Perspective..- Towards Reliable Testing for Multiple Information Retrieval System Comparisons..- Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval.

Read more less

Book Synopsis.- exHarmony: Authorship and Citations for Benchmarking the Reviewer Assignment Problem..- Unraveling the Impact of Visual Complexity on Search as Learning..- Enhancing Utility in Differentially Private Recommendation Data Release via Exponential Mechanism..- CountNet: Utilising Repetition Counts in Sequential Recommendation..- The Impact of Mainstream-Driven Algorithms on Recommendations for Children..- Leveraging Query Terms for Efficient Legal Document Recommendation..- Inducing Diversity in Differentiable Search Indexing..- EGL-DST: Error-Guided Learning for Multidimensional Evaluation Method of Dialogue State Tracking via GPT-4..- Examining the Impact of Transcript Accuracy on Podcast Search and Re-Ranking..- Ranking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions..- Counterfactual Query Rewriting to Use Historical Relevance Feedback..- Improving Language Model Performance by Training on Prototypical Contradictions..- LiT and Lean: Distilling Listwise Rerankers into Encoder-Decoder Models..- The Impact of Incidental Multilingual Text on the Cross-Lingual Transferring in Monolingual Retrieval..- Approximate Bag-of-Words Top-k Corpus Graphs..- Gradual Negative Matching for LLM Unlearning..- Fact-Driven Health Information Retrieval: Integrating LLMs and Knowledge Graphs to Combat Misinformation..- Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG..- Investigating the Performance of Dense Retrievers for Queries with Numerical Conditions..- Hierarchical Skip Decoding for Efficient Autoregressive Language Model..- Iterative Self-Training for Code Generation via Reinforced Re-Ranking..- Efficient Constant-Space Multi-Vector Retrieval..- DiffGR: A Discrete Diffusion-Based Model for Personalised Recommendation by Reconstructing User-Item Bipartite Graphs..- BAAF - A Framework for Media Bias Detection..- A Simple but Effective Closed-form Solution for Extreme Multi-label Learning..- Efficient and Effective Conversational Search with Tail Entity Selection..- Large Language Model Can Be a Foundation for Hidden Rationale- Based Retrieval..- SAFERec: Self-Attention and Frequency Enriched Model for Next Basket Recommendation..- Benchmarking Prompt Sensitivity in Large Language Models..- Do LLMs Provide Consistent Answers to Health-Related Questions across Languages?..- Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-ranking..- Benchmark Creation for Narrative Knowledge Delta Extraction Tasks: Can LLMs Help?..- Passage Segmentation of Documents for Extractive Question Answering..- Can Generative AI Adequately Protect Queries? Analyzing the Trade-off Between Privacy Awareness and Retrieval Effectiveness..- Retrieval-Augmented Neural Team Formation..- A Test Collection for Dataset Retrieval..- A new dataset for keyword extraction from IT job descriptions..- Entity-Aware Cross-Modal Pretraining for Knowledge-based Visual Question Answering..- Patience in Proximity: A Simple Early Termination Strategy for HNSW Graph Traversal in Approximate k-Nearest Neighbor Search..- Improving RAG for Personalization with Author Features and Contrastive Examples..- E2Rank: Efficient and Effective Layer-wise Reranking..- Token-Level Graphs for Short Text Classification..- Investigating the Scalability of Approximate Sparse Retrieval Algorithms to Massive Datasets..- A Comparative Analysis of Retrieval-Augmented Generation and Crowdsourcing for Fact-Checking..- Exploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankers.

Read more less

Book Synopsis.- BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A..- PIE-Med: Predicting, Interpreting and Explaining Medical Recommendations..-FinPersona: An LLM-Driven Conversational Agent for Personalized Financial Advising..- Semantic Search and Filtering with AI Agents..- TheoremView: A Framework for Extracting Theorem-Like Environments from Raw PDFs..- Checky, the Paper-Submission Checklist-Generator for Authors, Reviewers and LLMs..- AirTOWN: A Privacy-Preserving Mobile App for Real-time Pollution-Aware POI Suggestion..- Spoken Question Answering on Municipal Council Meetings..- MindWell: A Conversational Agent for Professional Depression Screening on Social Media..- Leveraging Language Models to Improve Human Annotation Efficiency with INCEpTION..- DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense Retrieval..- ASPIRE: Assistive System for Performance Evaluation in IR..- MedLink: Retrieval and Ranking of Case Reports to assist Clinical Decision Making..- Forecasting Prescription Efficacy..- Rebuilding the Past: Reconstructing Portuguese News Outlets with Web Archives..- MechIR: A Mechanistic Interpretability Framework for Information Retrieval..- Web-scale Retrieval Experimentation with chatnoir-pyterrier..- Automatic News Bias Classification for Strengthening Democracy [Demo]..- Combining Knowledge Graphs and Retrieval Augmented Generation for Enterprise Resource Planning..- Adapting LLMs for Domain-Specific Retrieval: A Case Study in Nuclear Safety..- Jina Embeddings V3: Multilingual Text Encoder with Low-Rank Adaptations..- PlotEdit: Natural Language-Driven Accessible Chart Editing in PDFs via Multimodal LLM Agents..- RURAGE: Robust Universal RAG Evaluator for Fast and Affordable QA Performance Testing..- Personalizing Enterprise Search with LLM Populated Attributes in Graph Models..- Leveraging LLMs for Energy Forecasting: The AcegasApsAmga Case Study..- Contextualizing Spotify’s Audiobook List Recommendations with Descriptive Shelves..- Text2Playlist: Generating Personalized Playlists from Text on Deezer..- Hierarchical prefixes for long document representations..- On the Longitudinal Impact of Exposure Bias in Recommender Systems..- Combining dissimilarity spaces to improve approximate similarity search..- Towards Query Obfuscation Strategies for Information Retrieval..- Automatic evaluation of online news outlets’ reliability..- Cooperative and Competitive LLM-based Multi-Agent Systems for Recommendation..- SynKGP: Knowledge Graph Population with Syntactic-LLM Hybridation for Question-Answering..- Understanding Numerical Contexts in Text by Asking Quantitative Questions..- Mitigating Gender Bias in Information Retrieval Systems..- Towards Intent-Driven Transparency in Conversational Search Systems..- A Framework for Robust Query Performance Prediction Using Contextual Relationships..- Enhancing Generative Models for Scientific Text Simplification..- Explainable Information Retrieval..- Fairness in Information Access: Conceptual Foundations and New Directions..- Enhancing Reproducibility and Replicability in Information Retrieval: A Path Towards Scientific Integrity and Effective Research..- Advanced Methods for Visual Information Retrieval and Exploration in Large Multimedia Collections..- Tutorial on conversational search and recsys..- Large Language Models are Human-like Annotators..- Rapid Prototyping for AI-based Applications: A Hands-on Tutorial for Connecting the Dots..- KEIR @ ECIR 2025: The Second Workshop on Knowledge-Enhanced Information Retrieval..- The Second Search Futures Workshop at ECIR’25..- QPP++ 2025: Query Performance Prediction and its Applications in the Era of Large Language Models..- The First Workshop on Scholarly Information Access (SCOLIA)..- Workshop on Evaluation of Retrieval-Augmented Generation Systems..- ROMCIR 2025: Overview of the 5th Workshop on Reducing Online Misinformation through Credible Information Retrieval..- The 8th International Workshop on Narrative Extraction from Texts (Text2Story’25) (Full Day)..- 3rd International Workshop on Geographic Information Extraction from Texts (GeoExT 2025)..- The Second International Workshop on Open Web Search (WOWS)..- ELOQUENT CLEF Shared Tasks for Evaluation of Generative Language Model Quality, 2nd edition..- LifeCLEF 2025 Teaser: Challenges on Species Presence Prediction and Identification, and Individual Animal Identification..- LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance..- CLEF 2025 JOKER Lab: Humour in the Machine..- ImageCLEF 2025: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications..- BioASQ at CLEF2025: The thirteenth edition of the large-scale biomedical semantic indexing and question answering challenge..- eRisk 2025: Contextual and Conversational Approaches for Depression Challenges..- CLEF 2025 SimpleText Track: Simplify Scientific Text (and Nothing More)..- Overview of PAN 2025: Generative AI Detection, Multilingual Text Detoxification, Multi-Author Writing Style Analysis, and Generative Plagiarism Detection..- EXIST 2025: Learning with Disagreement for Sexism Identification and Characterization in Tweets, Memes, and TikTok Videos..- QuantumCLEF 2025 - The Second Edition of the Quantum Computing Lab at CLEF..- Overview of Touch´e 2025: Argumentation Systems..- The CLEF-2025 CheckThat! Lab: Subjectivity, Fact-Checking, Claim Extraction & Normalization, and Retrieval..- TalentCLEF at CLEF2025: Skill and Job Title Intelligence for Human Capital Management.

Read more less

Book Synopsis.- Lexical and Morphological Resources..- Formalizing Abstract Nouns with “-pen” in Rromani..- Recognizing Verbs in Medieval Latin..- Formalizing Persian Verbs Conjugation..- Exposing Diminutive and Pejorative Verbs in Croatian..- Syntactic Resources. .- Local Syntactic Grammars for Quechua Negation and Negative Sentences..- Conditional Clauses versus Concessive Clauses in Spanish: An Automatic Treatment with the NooJ Platform..- Automatic Grammatical Disambiguation in Belarusian and Russian Legal Domain..- Corpus Linguistics and Discourse Analysis. .- Corpus-Based Exploration of Defense-Related Political Speeches..- A Collocation Analysis of Lemma in the Indonesian Translation of the Holy Quran..- Using NooJ in Action Research: the Case of Personal Expression in Professional Situations..- How (Not) to Talk About Femicide and Gender-based Violence: An Analysis of Narrative Strategies Adopted by Italian Newspapers..- Syntactic Analysis of Media Reporting on Femicides and Gender-Based Violence in Modern Italy..- Exploring Metanarrative Cues in Literary Texts with NooJ: the Case of Les Amours de Psyché et De Cupidon by Jean de La Fontaine..- Natural Language Processing Applications..- From the social network data to the document-oriented NoSQL Data warehouse using the NooJ platform..- Annotation of Qualitative Linguistic Features of Text Readability in Institutional Italian Texts..- Automatic Processing of Free Word Combinations Containing Quantitative Expressions in NooJ.

Read more less

Book Synopsis.- Speech..- Lightweight Target-Speaker-Based Overlap Transcription for Practical Streaming ASR..- An Empirical Analysis of Discrete Unit Representations in Speech Language Modeling Pre-training..- Optimizing ASR Models with Semantic Information..- Efficient Enhancement of Norwegian ASR Model..- Towards Stable and Personalised Profiles for Lexical Alignment in Spoken Human-Agent Dialogue..- Audio–Vision Contrastive Learning for Phonological Class Recognition..- TOSD-Net: A CNN-Transformer Architecture for Robust Frame-Level Overlapping Speech Detection in Diverse Acoustic Conditions..- An Exploration of ECAPA-TDNN and x-vector Speaker Representations in Zero-shot Multi-speaker TTS..- Emotion-Aware Speech-Driven Facial Avatar Animation via Joint Blendshape Prediction and Emotion Recognition..- Beyond Static Emotions: Leveraging Multitask Learning to Model Dynamics of Dimensional Affect in Speech..- Implicit Speaker Group Encoding in Self-supervised Speech Recognition Models..- Combining Temporal Visual Dynamics and Audio Representations for Robust Speaker Identification..- Sentences vs Phrases in Neural Speech Synthesis: the Phrases Strike Back..- Evaluating Phoneme-Level Pretraining in Czech Text-to-Speech Synthesis..- Unifying Global and Near-Context Biasing in a Single Trie Pass..- Synthesising Cross-Speaker Data for Low-Resource Pathological Speech Recognition with PEFT..- Multilingual Stutter Event Detection for English, German, and Mandarin Speech..- How Far Can Synthetic Speech Go? Enhancing ASR in Low-Resource Scenarios via Voice Cloning..- Enhancing Detection of Parkinson-induced Dysarthria with Cross-lingual Transfer Learning..- Vocoder-Free Non-Parallel Conversion of Whispered Speech With Masked Cycle-Consistent Generative Adversarial Networks..- Detection of Cognitive Disorders Using ASR-Based Nonsense Words Repetition..- Mind the Gap: Entity-Preserved Context-Aware ASR for Structured Transcriptions..- Boosting CTC-Based ASR Using LLM-Based Intermediate Loss Regularization..- Robust Disfluency Labeling in Spontaneous Speech: Insights from Diverse Hungarian Corpora Including Mentally Ill Speakers..- ParCzech4Speech: A New Speech Corpus Derived from Czech Parliamentary Data..- Towards an Accurate Domain-Specific ASR: Transcription for Pathology..- Automated Speaking Assessment for L2 Learners of Czech..- Inclusive ASR for Critical Public Services: Debiasing with Actor-Simulated Speech..- RECA-PD: A Robust Explainable Cross-Attention Method for Speech-based Parkinson's Disease Classification..- Systematic FAIRness Assessment of Open Voice Biomarker Datasets for Mental Health and Neurodegenerative Diseases..- When Silence Speaks: Understanding Open-Ended Responses via LLMs in Therapeutic Voice Interaction..- Multilingual Domain Adaptation for Speech Recognition Using LLMs..- Using Cross-attention For Conversational ASR Over The Telephone.

Read more less

Book Synopsis.- Text..- Product Recommendation with Prospect Theoretic Self-Aligned LLM Systems..- Tracking Mental Health Indicators on Social Media Before and After Diagnosis..- Large Language Models for Czech Aspect-Based Sentiment Analysis..- Few-shot Cross-lingual Aspect-Based Sentiment Analysis with Sequence-to-Sequence Models..- Flexing in 73 Languages: A Single Small Model for Multilingual Inflection..- Plant in Cupboard, Orange on Rably, Inat Aphone. Benchmarking Incremental Learning of Situation and Language Model using a Text-Simulated Situated Environment..- Refining Czech GEC: Insights from a Multi-Experiment Approach..- Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Language..- CRNLI: A Textual Entailment Dataset in the Chemistry Domain..- Scale-Free Characteristics of Legal Texts and the Limitations of LLMs..- Investigating the Effect of Parallel Data in the Cross-Lingual Transfer for Vision-Language Encoders..- Toward Quantifying How The Burden of Problems Reported By Patients Evolves in Parkinson's Disease: An Exploratory Analysis..- Computing Patient Similarity Based on Unstructured Clinical Notes..- Enhancing ASR Accuracy for Speakers with Parkinson’s Disease Using Instruction-Tuned LLMs..- Automatic Semantic Tagging of Estonian Spatial Adverbials for Valency Pattern Mining..- Automatic Cognitive Disorder Detection through Semantic Analysis of Verbal Image Descriptions..- Evaluating Prompt-Based and Fine-Tuned Approaches to Czech Anaphora Resolution..- Multilingual Implicit Discourse Relation Recognition via Abstract Object-Enhanced Chain-of-Thought Prompting..- Enhancing Masked Language Modeling in BERT Models Using Pretrained Static Embeddings..- Knowledge Representation Approaches for Educational Question Generation..- Leveraging Fine-Tuned State-of-the-Art LLMs for Symptom Classification of Patient-Reported Problems in Parkinson's Disease..- Gold Data and Multiple Understanding of Discourse Relations..- Are We There yet? A Thorough Evaluation of POS Tagging on Czech..- Morphological Segmentation with Neural Networks: Performance Effects of Architecture, Data Size, and Cross-Lingual Transfer in Seven Languages..- Verb Motility Dynamics Reveals Cognitive Impairment in Parkinson’s Disease: A Speech-Language Fusion Approach..- Dialogue..- Corpus of Cross-lingual Dialogues with Minutes and Detection of Misunderstandings..- Parameter vs. Sample Efficiency in Multi-intent Recognition for Dialogue Understanding: Benchmarking Small Open LLMs.

Read more less