7,929 research outputs found
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases
A critical challenge in constructing a natural language interface to database
(NLIDB) is bridging the semantic gap between a natural language query (NLQ) and
the underlying data. Two specific ways this challenge exhibits itself is
through keyword mapping and join path inference. Keyword mapping is the task of
mapping individual keywords in the original NLQ to database elements (such as
relations, attributes or values). It is challenging due to the ambiguity in
mapping the user's mental model and diction to the schema definition and
contents of the underlying database. Join path inference is the process of
selecting the relations and join conditions in the FROM clause of the final SQL
query, and is difficult because NLIDB users lack the knowledge of the database
schema or SQL and therefore cannot explicitly specify the intermediate tables
and joins needed to construct a final SQL query. In this paper, we propose
leveraging information from the SQL query log of a database to enhance the
performance of existing NLIDBs with respect to these challenges. We present a
system Templar that can be used to augment existing NLIDBs. Our extensive
experimental evaluation demonstrates the effectiveness of our approach, leading
up to 138% improvement in top-1 accuracy in existing NLIDBs by leveraging SQL
query log information.Comment: Accepted to IEEE International Conference on Data Engineering (ICDE)
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