Our work provides quantitative results and error analysis for each task, and identifies in detail the reasoning required to generate SQL expressions from natural language questions.
#Smartwrap gps full
In a traditional machine learning design manner, we extract various features specific to each task, apply a neural model, and then compose a full pipeline which constructs the SQL query from its parts. We build our model by dividing question answering into a sequence of tasks, including table retrieval and question element classification, and conduct experiments to measure the performance of each task. Each QA training instance comprises a table, a natural language question, and a corresponding structured SQL query. Our dataset is novel in that every question is paired with a table of a different signature, so learning must automatically generalize across domains. The dataset is derived from commonly asked questions on the web, and their corresponding answers found in tables on websites.
In this paper, we describe a dataset and baseline results for a question answering system that utilizes web tables. Question answering (QA) provides answers to a wide range of questions but is still limited in the complexity of reasoning and the breadth of accessible data sources. We also report some evidence that web users will contribute dataset semantics to the system without pay, motivated either to improve web accessibility or to expedite their own scraping tasks. From the MTurk work we derive estimates for the costs of eliciting wrappers from the crowdworkers for a large proportion of the sampled datasets, and an estimate of what proportion of web datasets can be wrapped by crowdworkers using the tool. We also validate the SmartWrap approach to acquiring wrappers for a large part of the web by evaluating wrappers contributed by MTurk users using the tool. We present a user study validating that users with a variety of technical backgrounds were able to use it to construct wrappers. To engage with nontechnical end users, we designed the tool to use very simple interactions.
#Smartwrap gps manual
We present the design of a tool we call SmartWrap that directs the manual scraping work of everyday end users, explicitly including nonprogrammers, towards the construction of reusable programs, called wrappers, that map the scraped website into a structured dataset. Web users constitute a huge population of potential workers, but most are not programmers and may have difficulty understanding and communicating the abstractions involved in labeling web datasets.
In this work, we explore the possibility of understanding the semantics of web datasets by asking sighted web users to manually scrape web pages into spreadsheets. The web contains many datasets presented visually, whose lack of semantic markup renders them difficult to understand and navigate using a screen reader.
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