![]() ![]() Analyses show that our new task is semantically challenging, capturing content overlap beyond lexical similarity and complements cross-document coreference with proposition-level links, offering potential use for downstream tasks.read more read lessĪbstract: Phrase representations derived from BERT often do not exhibit complex phrasal compositionality, as the model relies instead on lexical similarity to determine semantic relatedness. We employ crowd-workers for constructing a dataset of QA-based alignments, and present a baseline QA alignment model trained over our dataset. Our setting exploits QA-SRL, utilizing question-answer pairs to capture predicate-argument relations, facilitating laymen annotation of cross-text alignments. We go beyond clustering coreferring mentions, and instead model overlap with respect to redundancy at a propositional level, rather than merely detecting shared referents. In order to explicitly represent content overlap, we propose to align predicate-argument relations across texts, providing a potential scaffold for information consolidation. Current methods confronting consolidation struggle to fuse overlapping information. The results of our experiments on data logs of two real search engines show that the proposed method outperforms some well-known algorithms by at least 14% with respect to precision and P 10 parameters.read more read lessĪbstract: Multi-text applications, such as multi-document summarization, are typically required to model redundancies across related texts. The hybrid graph, created by consolidating the query community and query-flow graphs, takes into account the lexical similarity as well as the reformulation diversity to suggest queries. The second layer is enriched by a query-flow graph which models the transitional patterns made by users inside sessions. To reduce the overhead of clustering while preserving its performance, we utilize locality-sensitive hashing of k -shingles to represent queries in a space with smaller and fixed dimensions. This graph is generated through clustering similar queries which tend to convey the same meaning. In this paper, we propose a novel three-layer query recommendation method which is benefited from a query community graph in the first layer. A proper solution for query recommendation is to analyze the users’ behaviors and mimic the query transition patterns adopted by different users who are succeeded in finding their needed information. ![]() Abstract: Search engines recommend queries to improve the satisfaction level of users by shortening their search task. ![]()
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