# EVE: Explainable Vector Based Embedding Technique Using Wikipedia

## TL;DR

EVE is an unsupervised, interpretable word embedding method based on Wikipedia's structure, designed to improve fundamental NLP tasks while providing human-readable explanations.

## Contribution

The paper introduces EVE, a novel explainable embedding technique leveraging Wikipedia's graph structure, with demonstrated improvements over existing models in key NLP tasks.

## Key findings

- EVE outperforms Word2Vec, FastText, and GloVe in intruder detection.
- EVE effectively clusters related words and separates unrelated ones.
- EVE provides human-interpretable explanations for its embeddings.

## Abstract

We present an unsupervised explainable word embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a word using human-readable labels, thereby it readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed word embedding model, we consider its usefulness in three fundamental tasks: 1) intruder detection - to evaluate its ability to identify a non-coherent vector from a list of coherent vectors, 2) ability to cluster - to evaluate its tendency to group related vectors together while keeping unrelated vectors in separate clusters, and 3) sorting relevant items first - to evaluate its ability to rank vectors (items) relevant to the query in the top order of the result. For each task, we also propose a strategy to generate a task-specific human-interpretable explanation from the model. These demonstrate the overall effectiveness of the explainable embeddings generated by EVE. Finally, we compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across the three tasks, and report improvements over the state-of-the-art.