Learning to generate one-sentence biographies from Wikidata

We investigate the generation of one-sentence Wikipedia biographies from facts derived from Wikidata slot-value pairs. We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries. Our model incorporates a novel secondary objective that helps ensure it generates sentences that contain the input facts. The model achieves a BLEU score of 41, improving significantly upon the vanilla sequence-to-sequence model and scoring roughly twice that of a simple template baseline. Human preference evaluation suggests the model is nearly as good as the Wikipedia reference. Manual analysis explores content selection, suggesting the model can trade the ability to infer knowledge against the risk of hallucinating incorrect information

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Information Extraction in Illicit Domains

Extracting useful entities and attribute values from illicit domains such as human trafficking is a challenging problem with the potential for widespread social impact. Such domains employ atypical language models, have `long tails' and suffer from the problem of concept drift. In this paper, we propose a lightweight, feature-agnostic Information Extraction (IE) paradigm specifically designed for such domains. Our approach uses raw, unlabeled text from an initial corpus, and a few (12-120) seed annotations per domain-specific attribute, to learn robust IE models for unobserved pages and websites. Empirically, we demonstrate that our approach can outperform feature-centric Conditional Random Field baselines by over 18\% F-Measure on five annotated sets of real-world human trafficking datasets in both low-supervision and high-supervision settings. We also show that our approach is demonstrably robust to concept drift, and can be efficiently bootstrapped even in a serial computing environment.Comment: 10 pages, ACM WWW 201

A novel alignment algorithm for effective web data extraction from singleton-item pages

Automatic data extraction from template pages is an essential task for data integration and data analysis. Most researches focus on data extraction from list pages. The problem of data alignment for singleton item pages (singleton pages for short), which contain detail information of a single item is less addressed and is more challenging because the number of data attributes to be aligned is much larger than list pages. In this paper, we propose a novel alignment algorithm working on leaf nodes from the DOM trees of input pages for singleton pages data extraction. The idea is to detect mandatory templates via the longest increasing sequence from the landmark equivalence class leaf nodes and recursively apply the same procedure to each segment divided by mandatory templates. By this divide-and-conquer approach, we are able to efficiently conduct local alignment for each segment, while effectively handle multi-order attribute-value pairs with a two-pass procedure. The results show that the proposed approach (called Divide-and-Conquer Alignment, DCA) outperforms TEX (Sleiman and Corchuelo 2013) and WEIR (Bronzi et al. VLDB 6(10):805�816 2013) 2% and 12% on selected items of TEX and WEIR dataset respectively. The improvement is more obvious in terms of full schema evaluation, with 0.95 (DCA) versus 0.63 (TEX) F-measure, on 26 websites from TEX and EXALG (Arasu and Molina 2003)

An Unsupervised Technique to Extract Information from Semi-structured Web Pages

We propose a technique that takes two or more web pages generated by the same server-side template and tries to learn a regular expression that represents it and helps extract relevant information from similar pages. Our experimental results on real-world web sites demonstrate that our technique outperforms others in terms of both effectiveness and efficiency and is not affected by HTML errors.Ministerio de Ciencia y Tecnología TIN2007-64119Junta de Andalucía P07-TIC-2602Junta de Andalucía P08-TIC-4100Ministerio de Ciencia e Innovación TIN2008-04718-EMinisterio de Ciencia e Innovación TIN2010- 21744Ministerio de Economía, Industria y Competitividad TIN2010-09809-EMinisterio de Ciencia e Innovación TIN2010-10811-EMinisterio de Ciencia e Innovación TIN2010-09988-EMinisterio de Economía y Competitividad TIN2011-15497-

Generating Natural Language from Linked Data:Unsupervised template extraction

We propose an architecture for generating natural language from Linked Data that automatically learns sentence templates and statistical document planning from parallel RDF datasets and text. We have built a proof-of-concept system (LOD-DEF) trained on un-annotated text from the Simple English Wikipedia and RDF triples from DBpedia, focusing exclusively on factual, non-temporal information. The goal of the system is to generate short descriptions, equivalent to Wikipedia stubs, of entities found in Linked Datasets. We have evaluated the LOD-DEF system against a simple generate-from-triples baseline and human-generated output. In evaluation by humans, LOD-DEF significantly outperforms the baseline on two of three measures: non-redundancy and structure and coherence.