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

Question Paraphrase Generation for Question Answering System

The queries to a practical Question Answering (QA) system range from keywords, phrases, badly written questions, and occasionally grammatically perfect questions. Among different kinds of question analysis approaches, the pattern matching works well in analyzing such queries. It is costly to build this pattern matching module because tremendous manual labor is needed to expand its coverage to so many variations in natural language questions. This thesis proposes that the costly manual labor should be saved by the technique of paraphrase generation which can automatically generate semantically similar paraphrases of a natural language question. Previous approaches of paraphrase generation either require large scale of corpus and the dependency parser, or only deal with the relation-entity type of simple question queries. By introducing a method of inferring transformation operations between paraphrases, and a description of sentence structure, this thesis develops a paraphrase generation method and its implementation in Chinese with very limited amount of corpus. The evaluation results of this implementation show its ability to aid humans to efficiently create a pattern matching module for QA systems as it greatly outperforms the human editors in the coverage of natural language questions, with an acceptable precision in generated paraphrases

Local Patterns

A pattern is a word consisting of constants from an alphabet Sigma of terminal symbols and variables from a set X. Given a pattern alpha, the decision-problem whether a given word w may be obtained by substituting the variables in alpha for words over Sigma is called the matching problem. While this problem is, in general, NP-complete, several classes of patterns for which it can be efficiently solved are already known. We present two new classes of patterns, called k-local, and strongly-nested, and show that the respective matching problems, as well as membership can be solved efficiently for any fixed k

Pattern Matching with Variables: Fast Algorithms and New Hardness Results

A pattern (i. e., a string of variables and terminals) maps to a word, if this is obtained by uniformly replacing the variables by terminal words; deciding this is NP-complete. We present efficient algorithmsfootnote{The computational model we use is the standard unit-cost RAM with logarithmic word size. Also, all logarithms appearing in our time complexity evaluations are in base 2.} that solve this problem for restricted classes of patterns. Furthermore, we show that it is NP-complete to decide, for a given number k and a word w, whether w can be factorised into k distinct factors; this shows that the injective version (i.e., different variables are replaced by different words) of the above matching problem is NP-complete even for very restricted cases