Sistem Temu Kembali Informasi dengan Pemeringkatan Metode Vector Space Model

The objective of designing information retrieval system (IRS) with Vector Space Model (VSM) Method is to facilitate users to search Indonesian documents. IRS Software is designed to provide search results with the optimum number of documents (low recall) and accuracy (high precision) with VSM method that users may get fast and accurate results. VSM method provides a different credit for each document stored in a database which in turns to determine the document most similar to the query, where the documents with the highest credits are placed on the top of the search results. The evaluation of search results with IRS is conducted under recall and precision tests. This study fascinatingly creates a system which can preprocess (tokenizing, filtering, and stemming) within computation time of four minutes forty-one seconds

Rancang Bangun Information Retrieval System (IRS) Bahasa Jawa Ngoko pada Palintangan Penjebar Semangad dengan Metode Vector Space Model (VSM)

Bahasa Jawa adalah bahasa daerah yang paling banyak digunakan di Indonesia yang mulai ditinggalkan. Perlunya pelestarian bahasa jawa dalam bentuk online yang bisa diakses bagi penggunanya sehingga akanmemudahkan dalam pencarian dokumen teks khususnya dokumen bahasa jawa ngoko. Software IRS dirancang untuk memberikan hasil pencarian dokumen dalam jumlah yang optimal (recall rendah) dan akurat (precision tinggi) menggunakan metode VSM, sehingga user akan mendapatkan hasil pencarian cepat dan akurat. Metode VSM akan melakukan pembobotan tiap dokumen yang ada pada database sehingga antar dokumen memiliki bobot yang berbeda untuk menentukan dokumen mana yang paling mirip (similar) dengan query, dokumen dengan bobot tertinggi menempati ranking teratas dalam hasil pencarian. Evaluasi hasil pencarian IRS dilakukan dengan uji recall dan precision. Studi kasus yang telah dilakukan menggunakan IRS ini didapatkan hasil sistem mampu melakukan proses preprosesing (tokenisasi, filtering, dan stemming) dengan waktu komputasi 18 detik. Sistem mampu melakukan pencarian dokumen dan menampilkan hasil pencarian dokumen dalam waktu komputasi rata-rata 2 detik, memiliki rata-rata recall 0,04 dan rata-rata precision 0,84. Sistem dilengkapi dengan bobot tiap dokumen dan letakknya yang akan memudahkan user dalam pencarian dokumen teks bahasa Indonesia

High-performance Word Sense Disambiguation with Less Manual Effort

Supervised learning is a widely used paradigm in Natural Language Processing. This paradigm involves learning a classifier from annotated examples and applying it to unseen data. We cast word sense disambiguation, our task of interest, as a supervised learning problem. We then formulate the end goal of this dissertation: to develop a series of methods aimed at achieving the highest possible word sense disambiguation performance with the least reliance on manual effort. We begin by implementing a word sense disambiguation system, which utilizes rich linguistic features to better represent the contexts of ambiguous words. Our state-of-the-art system captures three types of linguistic features: lexical, syntactic, and semantic. Traditionally, semantic features are extracted with the help of expensive hand-crafted lexical resources. We propose a novel unsupervised approach to extracting a similar type of semantic information from unlabeled corpora. We show that incorporating this information into a classification framework leads to performance improvements. The result is a system that outperforms traditional methods while eliminating the reliance on manual effort for extracting semantic data. We then proceed by attacking the problem of reducing the manual effort from a different direction. Supervised word sense disambiguation relies on annotated data for learning sense classifiers. However, annotation is expensive since it requires a large time investment from expert labelers. We examine various annotation practices and propose several approaches for making them more efficient. We evaluate the proposed approaches and compare them to the existing ones. We show that the annotation effort can often be reduced significantly without sacrificing the performance of the models trained on the annotated data

Using Rhetorical Figures and Shallow Attributes as a Metric of Intent in Text

In this thesis we propose a novel metric of document intent evaluation based on the detection and classification of rhetorical figure. In doing so we dispel the notion that rhetoric lacks the structure and consistency necessary to be relevant to computational linguistics. We show how the combination of document attributes available through shallow parsing and rules extracted from the definitions of rhetorical figures produce a metric which can be used to reliably classify the intent of texts. This metric works equally well on entire documents as on portions of a document