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TDT4310-project-sorted-japanese-example-sentences

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TDT 4310 - Intelligent Text Analysis Project

Sorting japanese sentences by linguistic complexity

Overview

Introduction and motivation
Background
Datasets
Methodology
Evaluation
Conclusion, and further work

Motivation

JMDict	Tatoeba / Tanaka corpus	NHK Easy News	MeCab
Open source dictionary	Multilingual sentence pairs	Easy-to-read news articles	POS and morphological analyzer

Datasets

TF-IDF

Extract the most meaningful words of a document

Sense disambiguation

Pinpoint which sense of the word is used, based on surrounding context and grammar.

Background

Japanese

Three writing systems

hiragana	katakana	kanji

１０ページの５行目をみなさい

Let's start from (the) fifth line on page 10

Multiple readings per kanji

形 - katachi, kata, gyou, kei

Furigana

振 (furi) 仮 (ga) 名 (na)

Background

Data ingestion, preprocessing and disambiguation

Tanaka Corpus

信用█為る(する){して}█と█彼(かれ)[01]█は|1█言う{言った}

NHK News Articles

Scrape -> Extract text -> MeCab + Furigana -> Try disambiguating with POS

Methodology

Note:

Disambiguation here, is not necissarily sense ambiguation, but rather disambiguating the dictionary entry.

Could exploit the english translation to disambiguate all the way down to the word senses.

TF-IDF?

\text{TF-IDF} = \frac{\text{Amount of term in doc}}{\text{Amount of terms in doc}} \cdot log \frac{\text{Amount of docs}}{1 + \text{ Amount of docs containing term}}

\text{TF-DF} = \frac{AVG(\text{Amount of term in doc})}{\text{Amount of terms in doc}} \cdot \frac{\text{ Amount of docs containing term}}{\text{Amount of docs}}

Methodology

Note:

TF-IDF is usually used for finding out how meaningful a word is to a document. Here, we want to do the opposite. The value should have a higher score, if it is more common across several documents.