APLIKASI TRANSLATOR BAHASA DAYAK NGAJU – INDONESIA DENGAN MENGGUNAKAN TEKNOLOGI STRING PARSING

Bahasa Dayak adalah bahasa penduduk asli pulau Kalimantan yaitu suku dayak. Adapun bahasa Dayak ngaju adalah bahasa dayak yang digunakan oleh suku dayak yang tinggal di kalimantan tengah .Dewasa ini perkembangan teknologi sangat pesat terutama di bidang pendidikan khususnya belajar mengajar. Metode belajar yang klasikal dengan menggunakan buku dirasa dianggap membosankan maka perlu dibuat adanya aplikasi yang dapat membantu dalam menterjemahkan kata dan kalimat dari bahasa Dayak ngaju menuju bahasa Indonesia ataupun sebaliknya. Maka dari itu dibuatlah aplikasi Translator Bahasa Dayak ngaju–Indonesia dengan menggunakan teknologi String Parsing. String parsing adalah teknik untuk mengurai string kedalam token-token berdasarkan pembatas atau delimiter yang diberikan. Dimana dengan adanya aplikasi Translator Bahasa Dayak ngaju-Indonesia ini untuk menterjemahahkan kata dan kalimat dari bahasa Dayak ngaju menuju bahasa Indonesia ataupun sebaliknya. Sehingga dengan adanya aplikasi Translator bahasa Dayak ngaju-Indonesia ini setiap orang dapat dengan mudah menterjemah kan kata dan kalimat dari bahasa Dayak ngaju menuju bahasa Indonesia ataupun sebaliknya dari bahasa Indonesia menuju Bahasa Dayak ngaju. Kata kunci : Java, Penerjemah, SQLite, String parsing, Translato

Estimating Compact Yet Rich Tree Insertion Grammars

We present a Bayesian nonparametric model for estimating tree insertion grammars (TIG), building upon recent work in Bayesian inference of tree substitution grammars (TSG) via Dirichlet processes. Under our general variant of TIG, grammars are estimated via the Metropolis-Hastings algorithm that uses a context free grammar transformation as a proposal, which allows for cubic-time string parsing as well as tree-wide joint sampling of derivations in the spirit of Cohn and Blunsom (2010). We use the Penn treebank for our experiments and find that our proposal Bayesian TIG model not only has competitive parsing performance but also finds compact yet linguistically rich TIG representations of the data.Engineering and Applied Science

Propositionalisation of multiple sequence alignments using probabilistic models

Multiple sequence alignments play a central role in Bioinformatics. Most alignment representations are designed to facilitate knowledge extraction by human experts. Additionally statistical models like Profile Hidden Markov Models are used as representations. They offer the advantage to provide sound, probabilistic scores. The basic idea we present in this paper is to use the structure of a Profile Hidden Markov Model for propositionalisation. This way we get a simple, extendable representation of multiple sequence alignments which facilitates further analysis by Machine Learning algorighms

Character String Analysis and Customer Path in Stream Data

This purpose of this study is to propose a knowledge-discovery system that can abstract helpful information from character strings representing shopper visits to product sections associated with positive and negative purchasing events by applying character string parsing technologies to stream data describing customer purchasing behavior inside a store. Taking data that traced customers\u27 movements we focus on the number of times customers stop by particular product sections, and by representing those visits in the form of character strings, we propose a way to efficiently handle large stream data. During our experiment, we abstract store-section visiting patterns that characterize customers who purchase a relatively larger volume of items, and are able to show the usefulness of these visiting patterns. In addition, we examine index functions, calculation time, and prediction accuracy, and clarify technological issues warranting further research. In the present study, we demonstrate the feasibility of employing stream data in the marketing field and the usefulness of the employing character parsing techniques.IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008, 15-19 December 2008, Pisa, Ital