Lexigraph is a full-stack thesaurus-based vocabulary building application.

The application displays lexical relationships between a new word and its synonyms (as pertained to each part-of-speech definition) to aid vocabulary learning. In other words, users can comprehend new words through familiar ones. To achieve this, the application utilizes WordNet API from NLTK and visualizes lexical connections between a search word its synonyms as a graph. Users can interact with the graph to indicate known words and unknowns. In the meantime, a clustering algorithm in the backend partitions a vocabulary space (Google's Web Trillion Word Corpus) based on the distribution of known words and assign each cluster an estimated known probability. Using these probability assignments, the application will automatically highlight "known" synonyms in all subsequent search visualization.

This is a three-week capstone project for CMU 15112: Fundamentals of Programming and Computer Science.

Program: Desktop Application
Implementation: Python, Tkinter
Date: 2018


In the main interface, users can choose the following modes to interact with new vocabularies.
  • LEXIGRAPH: visualizes the lexical relationships between a word and its synonyms.
  • explore: get the latest usage from Marriam Webster
  • search: look up a new word and return the result as a lexigraph
  • dictionary: conventional dictionary definition provided by WordNet
  • pair: timed paring game for learned synonyms
  • archive: find all previously searched vocabularies

Looking Up A New Word

Type in a word in the search mode to obtain a lexigraph. The synonyms are organized by search word's part-of-speech (POS) tags. Hover over each POS tag to see definitions.

Interact to Indicate Word Familiarity

Click to add (highlight) or subtract (unhighlight) synonyms to indicate whether a word is known or otherwise. Lexigraph will remember words indicated as familiar or unfamiliar by the user

Automatic Word Recommendation

Based on real-time user interactions, the application estimate words that are likely to be known by users and automatically recommend (highlight) them in subsequent searches.

Conventional Dictionary View

Allows for conventional display of dictionary definitions.

Get The Latest Usage from The Web

To get the latest example usage from Merriam Webster.

Paring Game to Strengthen Retention

This is a timed paring game in which users are tasked to pair new vocabularies with known synonyms.

Review Learned Words

All previously searched words are stored in the archive for easy retrieval.

Backend Cluster Algorithm

The cluster algorithm in the backend estimates the probabilities of a word known by a given user. The design is based on the premise that words commonly used (i.e., higher frequency in a given corpus) are less difficult than rarer words. Therefore, a vocabulary space can be sorted by word frequencies, and by such proxy, the level of difficulty. In this prototype, a sorted list of 30,000 words from Google's Web Trillion Word Corpus forms a constrained vocabulary space. Based on real-time user interactions, the cluster algorithm partitions the vocabulary space and assigns a probability to each section according to the distribution densities of user-indicated known words. For instance, a region densely populated with user-indicated known words has a higher known probability. In a subsequent search, synonym with a known probability above mean is automatically highlighted to aid user's comprehension of a new word.