Advanced Analytics with Spark

Chapter 6. Understanding Wikipedia with Latent Semantic Analysis

Sandy Ryza

Where are the Snowdens of yesteryear?

Capt. Yossarian

Most of the work in data engineering consists of assembling data into some sort of queryable format. We can query structured data with formal languages. For example, when this structured data is tabular, we can use SQL. While it is by no means an easy task in practice, at a high level, the work of making tabular data accessible is often straightforward—pull data from a variety of data sources into a single table, perhaps cleansing or fusing intelligently along the way. Unstructured text data presents a whole different set of challenges. The process of preparing data into a format that humans can interact with is not so much “assembly,” but rather “indexing” in the nice case or “coercion” when things get ugly. A standard search index permits fast queries for the set of documents that contains a given set of terms. Sometimes, however, we want to find documents that relate to the concepts surrounding a particular word whether or not the documents contain that exact string. Standard search indexes often fail to capture the latent structure in the text’s subject matter.

Latent Semantic Analysis (LSA) is a technique in natural language processing and information retrieval that seeks to better understand a corpus of documents and the relationships between the words in those documents. It attempts to distill the corpus into a set of relevant concepts. Each ...