A comparison of standard spell checking algorithms and a novel binary neural approach

In this paper, we propose a simple, flexible, and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning, and associative matching in the AURA neural system. We integrate Hamming Distance and n-gram algorithms that have high recall for typing errors and a phonetic spell-checking algorithm in a single novel architecture. Our approach is suitable for any spell checking application though aimed toward isolated word error correction, particularly spell checking user queries in a search engine. We use a novel scoring scheme to integrate the retrieved words from each spelling approach and calculate an overall score for each matched word. From the overall scores, we can rank the possible matches. In this paper, we evaluate our approach against several benchmark spellchecking algorithms for recall accuracy. Our proposed hybrid methodology has the highest recall rate of the techniques evaluated. The method has a high recall rate and low-computational cost

Memories for Life: A Review of the Science and Technology

This paper discusses scientific, social and technological aspects of memory. Recent developments in our understanding of memory processes and mechanisms, and their digital implementation, have placed the encoding, storage, management and retrieval of information at the forefront of several fields of research. At the same time, the divisions between the biological, physical and the digital worlds seem to be dissolving. Hence opportunities for interdisciplinary research into memory are being created, between the life sciences, social sciences and physical sciences. Such research may benefit from immediate application into information management technology as a testbed. The paper describes one initiative, Memories for Life, as a potential common problem space for the various interested disciplines

An Integrated Neural IR System

Abstract. Over the years the amount and range of electronic text stored on the WWW has expanded rapidly, overwhelming both users and tools designed to index and search the information. It is impossible to index the WWW dynamically at query time due to the sheer volume so the index must be pre-compiled and stored in a compact but incremental data structure as the information is ever-changing. Much of the text is unstructured so a data structure must be constructed from such text, storing associations between words and the documents that contain them. The index must be able to index ne-grained word-based associations and also handle more abstract concepts such as synonym groups. A search tool is also required to link to the index and enable the user to pinpoint their required information. We describe such a system we have developed in an integrated hybrid neural architecture and evaluate our system against the benchmark SMART system for retrieval accuracy: recall and precision.