# Wikipedia-based Semantic Interpretation for Natural Language Processing

arXiv: https://arxiv.org/abs/1401.5697 · HTML: https://mlsanity.com/paper/1401.5697/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2014.

## Built on by (most cited first, 9 of 9)

- [WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia](https://mlsanity.com/paper/2111.08543/) (2021)
- [Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization](https://mlsanity.com/paper/2206.01532/) (2022)
- [DREQ: Document Re-Ranking Using Entity-based Query Understanding](https://mlsanity.com/paper/2401.05939/) (2024)
- [Wikipedia in the Era of LLMs: Evolution and Risks](https://mlsanity.com/paper/2503.02879/) (2025)
- [A Symbolic Representation of Human Posture for Interpretable Learning and Reasoning](https://mlsanity.com/paper/2210.08998/) (2022)
- [Estimating Text Similarity based on Semantic Concept Embeddings](https://mlsanity.com/paper/2401.04422/) (2024)
- [QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking](https://mlsanity.com/paper/2510.11589/) (2025)
- [Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution](https://mlsanity.com/paper/2307.00925/) (2023)
- [REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking](https://mlsanity.com/paper/2510.11592/) (2025)

## Abstract (arXiv)

Adequate representation of natural language semantics requires access to vast amounts of common sense and domain-specific world knowledge. Prior work in the field was based on purely statistical techniques that did not make use of background knowledge, on limited lexicographic knowledge bases such as WordNet, or on huge manual efforts such as the CYC project. Here we propose a novel method, called Explicit Semantic Analysis (ESA), for fine-grained semantic interpretation of unrestricted natural language texts. Our method represents meaning in a high-dimensional space of concepts derived from Wikipedia, the largest encyclopedia in existence. We explicitly represent the meaning of any text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our method on text categorization and on computing the degree of semantic relatedness between fragments of natural language text. Using ESA results in significant improvements over the previous state of the art in both tasks. Importantly, due to the use of natural concepts, the ESA model is easy to explain to human users.