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This tutorial will cover relevant and interesting topics on applying deep learning on graphs techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, GNN-based encoder-decoder models for NLP, and the applications of GNNs in various NLP tasks (e.g., information extraction, machine translation and ...
Jul 24, 2024 · Natural Language Processing. Much of the information that can help transform enterprises is locked away in text, like documents, tables, and charts. We’re building advanced AI systems that can parse vast bodies of text to help unlock that data, but also ones flexible enough to be applied to any language problem.
The IBM Watson Natural Language Processing Library helps to develop enterprise-ready solutions through robust AI models, extensive language coverage and scalable container orchestration. The library form provides the flexibility to deploy natural language AI in any environment, and clients can take advantage of the improved performance on Intel fourth-gen Xeon Scalable Processors.
New approaches to Knowledge Graph development use a combination of extraction methods and state-of-the-art Natural Language Processing (NLP) techniques. Recently, considerable literature in this space has centered around the use of Graph Neural Networks (GNNs) to learn powerful embeddings which leverage topological structures in the KGs.
Abstract. This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and ...
自然语言处理，英文Natural Language Processing，简写NLP。NLP这个概念本身过于庞大，可以把它分成“自然语言”和“处理”两部分。先来看自然语言。区分于计算机语言，自然语… 查看全部内容
Feb 15, 2023 · Computer Vision. Modern computer vision systems have superhuman accuracy when it comes to image recognition and analysis, but they don’t really understand what they see. At IBM Research, we’re designing AI systems with the ability to see the world like we do.
The Natural Language Processing (NLP) community has, in recent years, demonstrated a notable focus on improving higher scores on standard benchmarks and taking the lead on community-wide leaderboards (e.g., GLUE, SentEval).
Aug 22, 2023 · Retrieval-augmented generation (RAG) is an AI framework for improving the quality of LLM-generated responses by grounding the model on external sources of knowledge to supplement the LLM’s internal representation of information. Implementing RAG in an LLM-based question answering system has two main benefits: It ensures that the model has ...
Recent years have seen important advances in the quality of state-of-the-art models, but this has come at the expense of models becoming less interpretable. This survey presents an overview of the current state of Explainable AI (XAI), considered within the domain of Natural Language Processing (NLP).
