Wikipedia-based Semantic Interpretation for Natural Language Processing (1401.5697v1)

Abstract: 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.

• The paper introduces ESA, a novel method that represents texts as weighted vectors of Wikipedia concepts, surpassing traditional semantic models.
• The methodology leverages Wikipedia's expansive and current knowledge by applying TF-IDF to derive explicit semantic features for enhanced text representation.
• Empirical evaluations demonstrate ESA's efficacy with a Spearman correlation of 0.75 for semantic similarity and significant improvements in text categorization accuracy.

Wikipedia-based Semantic Interpretation for Natural Language Processing

The paper "Wikipedia-based Semantic Interpretation for Natural Language Processing" by Evgeniy Gabrilovich and Shaul Markovitch introduces an innovative methodology termed Explicit Semantic Analysis (ESA) for semantic interpretation of natural language texts. ESA utilizes the vast amount of information encoded in Wikipedia to enhance the representation of text semantics, significantly improving the performance across tasks like text categorization and computing semantic relatedness.

Background and Motivation

Traditional methods of representing text semantics, such as the bag of words model, latent semantic analysis (LSA), and the employment of lexical databases like WordNet, have distinct limitations. The bag of words model lacks context sensitivity and struggles with short texts. LSA derives concepts statistically but lacks explicit explanations. WordNet, although structured, covers a limited scope of knowledge, being predominantly lexical with minimal world knowledge. Thus, the need arises for a methodical approach capable of accessing a broader, more elaborate knowledge base.

The ESA Framework

ESA represents the semantics of natural language texts in a high-dimensional space of concepts, each derived from a Wikipedia article. This approach leverages the comprehensive and up-to-date nature of Wikipedia, encoding knowledge principles into the semantic representation model. The procedure is straightforward: given a text fragment, its semantics are interpreted as a weighted vector of Wikipedia concepts, analogous to dimensions in a vector space model. Each concept contributes to this vector based on text relevance computed via term frequency-inverse document frequency (TF-IDF) values.

Empirical Evaluation

The effectiveness of ESA is demonstrated through two significant NLP tasks:

1. Semantic Relatedness: The ESA model offers superior performance in assessing the semantic relatedness of word pairs compared to previous methodologies using WordNet, Roget's Thesaurus, and LSA. The paper reports a Spearman correlation coefficient of 0.75 on the WordSimilarity-353 test, outperforming previous best results.
2. Text Categorization: By enriching text representation with knowledge-based features, ESA significantly enhances text categorization accuracy. Notably, it yields better performance across various datasets, thus confirming the utility of external knowledge sources like Wikipedia in bolstering text classification tasks.

Implications and Future Directions

The proposed methodology not only enhances the performance of traditional NLP tasks but also offers theoretical insights into the potential of encyclopedic knowledge as a semantic resource. The interpretability of the model, owing to its explicit use of known concepts, can bridge the gap between machine-conceived and human-understood semantics, making it more accessible for end-users.

The implications stretch beyond immediate use cases. Given Wikipedia's multilingual nature, ESA can potentially be adapted for cross-lingual tasks and machine translation. Additionally, its application to domain-specific Wikis suggests avenues for targeted semantic enrichment, offering productivity gains in specialized fields like medicine or finance.

Future research could explore more sophisticated uses of Wikipedia's link structure, enhancing second-order semantic relations, and evaluating ESA's effectiveness across a more expansive set of semantic tasks. Furthermore, integrating ESA with other forms of structured world knowledge could yield competitive advancements in AI robustness and contextual understanding, ultimately leading to more nuanced human-computer interactions.