mARC: Memory by Association and Reinforcement of Contexts

This paper introduces the memory by Association and Reinforcement of Contexts (mARC). mARC is a novel data modeling technology rooted in the second quantization formulation of quantum mechanics. It is an all-purpose incremental and unsupervised data storage and retrieval system which can be applied to all types of signal or data, structured or unstructured, textual or not. mARC can be applied to a wide range of information clas-sification and retrieval problems like e-Discovery or contextual navigation. It can also for-mulated in the artificial life framework a.k.a Conway "Game Of Life" Theory. In contrast to Conway approach, the objects evolve in a massively multidimensional space. In order to start evaluating the potential of mARC we have built a mARC-based Internet search en-gine demonstrator with contextual functionality. We compare the behavior of the mARC demonstrator with Google search both in terms of performance and relevance. In the study we find that the mARC search engine demonstrator outperforms Google search by an order of magnitude in response time while providing more relevant results for some classes of queries

Review Paper on Named Entity Recognition and Attribute Extraction using Machine Learning

Named entity recognition (NER) is a subsidiary task under information extraction that aims at locating and classifying named entities in the text provided into pre-defined categories such as the names of people, locations, organizations, etc. In focused NER, once the entities are recognized we further aim at finding the most important named entities among all the others in a document, which we refer to as focused named entity recognition. We implement this using a classifier approach, i.e. Naïve Bayes classification, and we show that these focused named entities are useful for many natural language processing applications, such as document summarization, search result ranking, and entity detection and tracking. Attribute extraction on the other hand, involves automatic selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive problem you are working on. We try to implement an approach to extract the entities’ attributes from unstructured text corpus. The proposed method is an unsupervised machine learning method that extracts the entity attributes utilizing deep belief network (DBN), we work on training data sets that we extract via web scraping tools, and test files for the same. Our goal can be twofold in this respect, firstly we can aim at simply organizing information so that it is useful to people, or put it in a semantically precise form to make further inferences

A survey on recent advances in named entity recognition

Named Entity Recognition seeks to extract substrings within a text that name real-world objects and to determine their type (for example, whether they refer to persons or organizations). In this survey, we first present an overview of recent popular approaches, but we also look at graph- and transformer- based methods including Large Language Models (LLMs) that have not had much coverage in other surveys. Second, we focus on methods designed for datasets with scarce annotations. Third, we evaluate the performance of the main NER implementations on a variety of datasets with differing characteristics (as regards their domain, their size, and their number of classes). We thus provide a deep comparison of algorithms that are never considered together. Our experiments shed some light on how the characteristics of datasets affect the behavior of the methods that we compare.Comment: 30 page

A Review on Human-Computer Interaction and Intelligent Robots

In the field of artificial intelligence, human–computer interaction (HCI) technology and its related intelligent robot technologies are essential and interesting contents of research. From the perspective of software algorithm and hardware system, these above-mentioned technologies study and try to build a natural HCI environment. The purpose of this research is to provide an overview of HCI and intelligent robots. This research highlights the existing technologies of listening, speaking, reading, writing, and other senses, which are widely used in human interaction. Based on these same technologies, this research introduces some intelligent robot systems and platforms. This paper also forecasts some vital challenges of researching HCI and intelligent robots. The authors hope that this work will help researchers in the field to acquire the necessary information and technologies to further conduct more advanced research