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Xiao Ling

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3 papers

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IJCAI Conference 2013 Conference Paper

Synthesizing Union Tables from the Web

Xiao Ling
Alon Halevy
Fei Wu
Cong Yu

Several recent works have focused on harvesting HTML tables from the Web and recovering their semantics [Cafarella et al. , 2008a; Elmeleegy et al. , 2009; Limaye et al. , 2010; Venetis et al. , 2011]. As a result, hundreds of millions of high quality structured data tables can now be explored by the users. In this paper, we argue that those efforts only scratch the surface of the true value of structured data on the Web, and study the challenging problem of synthesizing tables from the Web, i. e. , producing never-before-seen tables from raw tables on the Web. Table synthesis offers an important semantic advantage: when a set of related tables are combined into a single union table, powerful mechanisms, such as temporal or geographical comparison and visualization, can be employed to understand and mine the underlying data holistically. We focus on one fundamental task of table synthesis, namely, table stitching. Within a given site, many tables with identical schemas can be scattered across many pages. The task of table stitching involves combining such tables into a single meaningful union table and identifying extra attributes and values for its rows so that rows from different original tables can be distinguished. Specifically, we first define the notion of stitchable tables and identify collections of tables that can be stitched. Second, we design an effective algorithm for extracting hidden attributes that are essential for the stitching process and for aligning values of those attributes across tables to synthesize new columns. We also assign meaningful names to these synthesized columns. Experiments on real world tables demonstrate the effectiveness of our approach.

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AAAI Conference 2012 Conference Paper

Fine-Grained Entity Recognition

Xiao Ling
Daniel Weld

Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e. g. , person, organization, location or miscellaneous. In order to intelligently understand text and extract a wide range of information, it is useful to more precisely determine the semantic classes of entities mentioned in unstructured text. This paper defines a fine-grained set of 112 tags, formulates the tagging problem as multi-class, multi-label classification, describes an unsupervised method for collecting training data, and presents the FIGER implementation. Experiments show that the system accurately predicts the tags for entities. Moreover, it provides useful information for a relation extraction system, increasing the F1 score by 93%. We make FIGER and its data available as a resource for future work.

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AAAI Conference 2010 Conference Paper

Temporal Information Extraction

Xiao Ling
Daniel Weld

Research on information extraction (IE) seeks to distill relational tuples from natural language text, such as the contents of the WWW. Most IE work has focussed on identifying static facts, encoding them as binary relations. This is unfortunate, because the vast majority of facts are fluents, only holding true during an interval of time. It is less helpful to extract PresidentOf(Bill-Clinton, USA) without the temporal scope 1/20/93 - 1/20/01. This paper presents TIE, a novel, information-extraction system, which distills facts from text while inducing as much temporal information as possible. In addition to recognizing temporal relations between times and events, TIE performs global inference, enforcing transitivity to bound the start and ending times for each event. We introduce the notion of temporal entropy as a way to evaluate the performance of temporal IE systems and present experiments showing that TIE outperforms three alternative approaches.

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