[AI from Scratch] Episode 243: Morphological Analysis

Recap and Today’s Theme

Hello! In the previous episode, we explored text data preprocessing, covering techniques such as tokenization, stopword removal, part-of-speech tagging, and n-gram creation. These methods are fundamental in transforming text into a format that NLP models can effectively process.

Today, we will focus on morphological analysis, particularly in the context of Japanese text. Morphological analysis is the process of segmenting Japanese text into words and assigning part-of-speech information to each word. In languages like Japanese, where words are not separated by spaces, morphological analysis becomes a crucial preprocessing step. This episode explains the basic concepts, methods, and tools associated with morphological analysis.

What is Morphological Analysis?

1. Definition of Morphological Analysis

Morphological analysis is the process of breaking down text into its smallest meaningful units, called morphemes, and assigning part-of-speech information to each. In Japanese, morphemes can include nouns, verbs, adjectives, particles, and inflections. For example, in the sentence “私は学生です” (I am a student), morphological analysis divides it into morphemes like “私” (I), “は” (topic marker), “学生” (student), and “です” (is/am). This process helps reveal the meaning and role of each word, making it possible for computers to interpret Japanese text.

2. Characteristics of Japanese and the Importance of Morphological Analysis

Unlike English, Japanese does not use spaces between words, making it difficult for computers to distinguish word boundaries without further processing. Morphological analysis is essential to segment and label these words correctly, enabling NLP models to interpret the text accurately. The output of morphological analysis includes both the surface form of each word and its part-of-speech information, facilitating tasks like grammatical analysis and understanding sentence structure.

Morphological Analysis Methods

1. Basic Steps in Morphological Analysis

Morphological analysis generally involves the following steps:

1. Segmentation into Morphemes: Splitting text into morphemes. In Japanese, algorithms are required to detect word boundaries accurately.
2. Part-of-Speech Tagging: Assigning part-of-speech information (e.g., noun, verb, particle) to each morpheme.
3. Retrieving Surface Form and Base Form: Extracting the surface form (as it appears in the text) and the base form (dictionary form) for each morpheme.

2. Algorithms for Morphological Analysis

Several algorithms are used to implement morphological analysis. The most common ones include the Longest Matching Method and machine learning-based approaches.

a. Longest Matching Method

The Longest Matching Method identifies the longest matching substring in the text as a word using a dictionary. The algorithm scans from the leftmost part of the text and selects the longest substring that matches a dictionary entry. This method is simple and fast but may struggle when encountering words not in the dictionary or when multiple words of the same length exist.

b. Machine Learning-Based Methods

Modern morphological analysis often relies on machine learning and deep learning. These methods train models using large datasets, allowing them to determine optimal segmentation and part-of-speech tagging based on context. Popular approaches include Conditional Random Fields (CRF) and Bidirectional LSTM (Long Short-Term Memory) models. These models are capable of handling the contextual dependencies within sequences, achieving higher accuracy in morphological analysis.

Tools for Morphological Analysis

1. MeCab

MeCab is a widely used Japanese morphological analysis tool. It is based on the Longest Matching Method and offers lightweight, high-speed processing. MeCab allows customization, including changing dictionaries and adding user-defined dictionaries, making it versatile for both research and practical applications. It can be easily integrated into programming languages such as Python.

2. Janome

Janome is a morphological analysis library implemented entirely in Python. It has few dependencies on external libraries, making it easy to use within Python environments. Janome is suitable for small projects and prototyping, offering a simple way for Python users to perform morphological analysis.

3. Sudachi

Sudachi is a tool developed for advanced Japanese morphological analysis, supporting multiple segmentation modes (fine, standard, coarse). This flexibility enhances accuracy, particularly when handling katakana words and proper nouns. Sudachi’s ability to adjust segmentation precision makes it a powerful tool for handling diverse text types in Japanese.

Applications of Morphological Analysis

1. Text Mining

Morphological analysis enables text mining, extracting meaningful information from text. For instance, it can analyze customer reviews to extract frequently occurring keywords, providing insights into customer opinions.

2. Query Analysis in Search Engines

Search engines use morphological analysis to interpret user queries. By segmenting and understanding the meaning of each query term, search engines can provide more relevant search results.

3. Sentiment Analysis

Morphological analysis is also used in sentiment analysis. By breaking down text into words and evaluating the emotional tone of each word, the overall sentiment of a post or review can be inferred. This allows for the identification of user opinions or sentiments expressed in social media posts or product reviews.

Challenges and Considerations in Morphological Analysis

1. Dictionary Selection

The accuracy of morphological analysis depends heavily on the dictionary used. Standard dictionaries may not include specialized terms or new words, so user-defined dictionaries may be necessary for specific fields or domains.

2. Handling Homonyms and Polysemous Words

Japanese contains many homonyms and polysemous words, which pose challenges for morphological analysis. It is essential to accurately segment and tag words based on their context. While machine learning approaches can leverage contextual information, traditional dictionary-based methods may struggle, leading to misanalysis.

3. Dealing with Unknown Words

Words not registered in the dictionary (unknown words) present difficulties in analysis. This is particularly true for new proper nouns, slang, and loanwords. Regular dictionary updates and supplementation are crucial for improving the accuracy of morphological analysis.

Summary

In this episode, we explored the basics of morphological analysis, its importance in Japanese NLP, and the main tools available. Morphological analysis is indispensable for Japanese NLP and has applications in text mining, sentiment analysis, and query interpretation for search engines. By selecting the appropriate tools and algorithms, more accurate analysis can be achieved.

Next Episode Preview

Next time, we will discuss the Bag-of-Words model, examining how text is represented using word frequency and exploring its advantages and limitations. Stay tuned!

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Notes

1. Longest Matching Method: An algorithm that extracts the longest matching substring from the text as a morpheme.
2. Conditional Random Fields (CRF): A machine learning method for labeling sequence data.
3. Bidirectional LSTM: An extended version of the Long Short-Term Memory model that considers both forward and backward dependencies in sequence data.