A variety of methods have been proposed for attribute-value extraction from semistructured text with consistent templates (strict semi-text). However, when the templates in semi-structured text are inconsistent (weak semi-text), these methods will work poorly. To overcome the templateinconsistent problem, in this paper, we proposed a novel method to leverage sitelevel knowledge for attribute-va...

Bottom Clause Propositionalization (BCP) is a recent propositionalization method which allows fast relational learning. Propositional learners can use BCP to obtain accuracy results comparable with Inductive Logic Programming (ILP) learners. However, differently from ILP learners, what has been learned cannot normally be represented in first-order logic. In this paper, we propose an approach an...

The extraction of multi-attribute objects from the deep web is the bridge between the unstructured web and structured data. Existing approaches either induce wrappers from a set of human-annotated pages or leverage repeated structures on the page without supervision. What the former lack in automation, the latter lack in accuracy. Thus accurate, automatic multi-attribute object extraction has r...

The accuracy and coverage of existing methods for extracting attributes of instances from text in general, and Web search queries in particular, are limited by two main factors: availability of input textual data to which the methods can be applied, and inherent limitations of the underlying assumptions and algorithms being used. This paper proposes a weakly-supervised approach for the acquisit...

Product Attribute Extraction is the task of automatically discovering attributes of products from text descriptions. In this paper, we propose a new approach which is both unsupervised and domain independent to extract the attributes. With our approach, we are able to achieve 92% precision and 62% recall in our experiments. Our experiments with varying dataset sizes show the robustness of our a...

This paper presents a named entity extraction system for detecting attributes in product titles of eCommerce retailers like Walmart. The absence of syntactic structure in such short pieces of text makes extracting attribute values a challenging problem. We find that combining sequence labeling algorithms such as Conditional Random Fields and Structured Perceptron with a curated normalization sc...

We describe various syntactic and semantic conditions for finding abstract nouns which refer to concepts of adjectives from a text, in an attempt to explore the creation of a thesaurus from text. Depending on usages, six kinds of syntactic patterns are shown. In the syntactic and semantic conditions an omission of an abstract noun is mainly used, but in addition, various linguistic clues are ne...

We propose a method to describe how a person is dressed, using an innovative way to extract Visual Information exploiting the Human Pose Estimation. Given the lack of algorithms in this field, we aims to pave the way giving a baseline and publishing a detailed dataset for future comparisons. In particular in this study we show how using the Human Pose Estimation, we are able to extract the esse...