Taming the Unstructured: Creating Structured Content from Partially Labeled Schematic Text Sequences

Abstract

Numerous data sources such as classified ads in online newspapers, electronic product catalogs and postal addresses are rife with unstructured text content. Typically such content is characterized by attribute value sequences having a common schema. In addition each sequence is unstructured free text without any separators between the attribute values. Hidden Markov Models (HMMs) have been used for creating structured content from such text sequences by identifying and extracting attribute values occurring in them. Extant approaches to creating “structured content from text sequences” based on HMMs use either completely labeled or completely unlabeled training data. The HMMs resulting from these two dominant approaches present contrasting trade offs w.r.t. labeling effort and recall/precision of the extracted attribute values. In this paper we propose a HMM based algorithm that uses partially labeled training data for creating structured content from text sequences. By exploiting the observation that partially labeled sequences give rise to independent subsequences we compose the HMMs corresponding to these subsequences to create structured content from the complete sequence. An interesting aspect of our approach is that it gives rise to a family of HMMs spanning the trade off spectrum. We present experimental evidence of the effectiveness of our algorithm on real-life data sets and demonstrate that it is indeed possible to bootstrap structured content creation from schematic text data sources using HMMs that require limited labeling effort and do so without compromising on the recall/precision performance metrics.