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The paper presents RuBQ, the first Russian knowledge base question answering (KBQA) dataset.
Entity linking, the task of mapping textual mentions to known entities, has recently been tackled using contextualized neural networks.
While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information.
However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages.
Tasking machines with understanding receipts can have important applications such as enabling detailed analytics on purchases, enforcing expense policies, and inferring patterns of purchase behavior on large collections of receipts.
They perform better on Wikipedia text than on real-world text such as news or twitter.
We find that the shallow features achieve state-of-the-art results on both tasks, significantly outperforming performances of the deep semantic features on the five-level classification task.
In this paper, we design headword-oriented entity linking (HEL), a specialized entity linking problem in which only the headwords of the entities are to be linked to knowledge bases; mention scopes of the entities do not need to be identified in the problem setting.
We propose a new approach to address the scarcity of training data that extends the CONTES method by corpus selection, pre-processing and weak supervision strategies, which can yield high-performance results without any manually annotated examples.