The term “semantic” pertains to the study of meaning in language—encompassing words, phrases, sentences, and texts. It involves interpreting the significance, relationships, and implications of linguistic elements.
In the context of the earlier posts:
- Nepali Dictionary: This tool provides a foundational Nepali language dictionary. It offers features including translation and transliteration techniques to enhance search capabilities.
- Nepali Rhyming Dictionary: This tool focuses on string matching, text morphing, and fuzzy fits to aid in finding rhyming words and exploring text patterns.
Building on these foundations, our current focus is “Semantic Analysis of Nepali Words.” This involves:
- Understanding Meanings: Analyzing the meanings of Nepali words and phrases.
- Establishing Relationships: Identifying how words are related through synonyms, antonyms, and other semantic connections.
- Contextual Use: Exploring how the meaning of words shifts depending on their context.
- Word Connections: Using cosine similarity to map and visualize the relationships between words based on their meanings.
Network Diagrams: Fun, if only they weren't so memory intensive
This analysis seeks to enhance our understanding of Nepali vocabulary by revealing the interactions and relationships among words. Initially, I aimed to create a comprehensive word map network for the entire dictionary, which includes over 60,000 words. However, I soon discovered that this project was too demanding for my computer. As a result, what follows below is just a preliminary glimpse into this NLP analysis.
What's in the Notebook?
In natural language processing (NLP), understanding and analyzing the semantic relationships between words is crucial for many applications, from machine translation to text generation. For languages like Nepali, which have rich and complex structures, this task can be both fascinating and challenging. In this google collaboratory python notebook, we’ll try to an organic semantic network for Nepali words using Python, exploring how to build a system that uncovers semantic relationships and provides meaningful relationship between the Nepali words.
The notebook starts by importing tools that help with data handling and text analysis. It uses libraries (Natural Language Toolkit) to read data from the web, work with text, and calculate how similar different words are. Then there are functions that looks up a specific word in a dataset that searches for the word in various columns and tries to find its meaning. If it finds the word, it then looks for words with similar meanings. Another function takes the list of related words and sorts them based on how closely their meanings match the original word. This is done using a technique that measures similarity.
Displaying Results: The final function creates a web-friendly list of these sorted words. It shows each word and, when you hover over it, displays its meaning. In summary, the code downloads a Nepali dictionary file, loads it into the program, and then uses the functions to find and display related words for a given word (“सपना” in this example).
Here is the link to the Colab Notebook – if you want to try it out.
