Colin Layfield

Search Results Clustering without External Resources

Our unsupervised Search Results Clustering (SRC) system partitions into clusters the top-n results returned by a search engine. We present the results of experiments with our SRC system that performs incremental clustering on document titles and snippets only and does not use external resources, yet which outperforms the best performers to date on the SemEval-2013 Task 11 gold standard. We include Latent Semantic Analysis (LSA) as an optional step, using the snippets themselves as the background corpus. We demonstrate that better results are achieved by leaving the query terms out of the clustering process, and that currently, the version without LSA outperforms the version with LSA.

Experiments with Document Retrieval from Small Text Collections Using Latent Semantic Analysis or Term Similarity with Query Coordination and Automatic Relevance Feedback

Users face the Vocabulary Gap problem when attempting to retrieve relevant textual documents from small databases, especially when there are only a small number of relevant documents, as it is likely that different terms are used in queries and relevant documents to describe the same concept. To enable comparison of results of different approaches to semantic search in small textual databases, the PIKES team constructed an annotated test collection and Gold Standard comprising 35 search queries and 331 articles. We present two different possible solutions. In one, we index an unannotated version of the PIKES collection using Latent Semantic Analysis (LSA) retrieving relevant documents using a combination of query coordination and automatic relevance feedback. Although we outperform prior work, this approach is dependent on the underlying collection, and is not necessarily scalable. In the second approach, we use an LSA Model generated by SEMILAR from a Wikipedia dump to generate a Term Similarity Matrix (TSM). Queries are automatically expanded with related terms from the TSM and are submitted to a term-by-document matrix Vector Space Model of the PIKES collection. Coupled with a combination of query coordination and automatic relevance feedback we also outperform prior work with this approach. The advantage of the second approach is that it is independent of the underlying document collection.

With LSA Size DOES Matter

Latent Semantic Analysis (LSA) is a technique from the field of Natural Language Processing that enables comparison of semantic similarities between documents using vector operations. This technique has been used in areas from Information Retrieval (IR) to the automated assessment of essays. One property used in document comparison is size. The general philosophy is that more text is better although few concrete examples or guidelines exist that demonstrate this. This paper shows, via a novel concrete example taken from real world data, that larger documents do imply more accurate semantic similarity comparisons.

Multi-Lingual LSA with Serbian and Croatian: An Investigative Case Study

One of the challenges in information retrieval is attempting to search a corpus of documents that may contain multiple languages. This exploratory study expands upon earlier research employing Latent Semantic Analysis (so called Multi-Lingual Latent Semantic Indexing, or ML-LSI/LSA). We experiment using this approach, and a new one, in a multi-lingual context utilising two similar languages, namely Serbian and Croatian. Traditionally, with an LSA approach, a parallel corpus would be needed in order to train the system by combining identical documents in two languages into one document. We repeat that approach and also experiment with creating a semantic space using the parallel corpus on its own without merging the documents together to test the hypothesis that, with very similar languages, the merging of documents may not be required for good results.