In this paper, we propose a linguistically-motivated query expansion framework that recognizes and encodes significant query constituents characterizing query intent in order to improve retrieval performance. Concepts-of-Interest are recognized as the core concepts that represent the gist of the search goal whilst the remaining query constituents which serve to specify the search goal and complete the query structure are classified as descriptive, relational or structural. Acknowledging the need to form semantically-associated base pairs for the purpose of extracting related potential expansion concepts, an algorithm which capitalizes on syntactical dependencies to capture relationships between adjacent and non-adjacent query concepts is pr...
Query expansion, in the form of pseudo-relevance feedback or relevance feedback, is a common techniq...
This study is in the area of automatic query expansion - automatically expanding query terms with re...
Query understanding has been well studied in the areas of information retrieval and spoken language ...
Poor information retrieval performance has often been attributed to the query-document vocabulary mi...
A user's query is considered to be an imprecise description of their information need. Automatic que...
A user’s query is considered to be an imprecise description of their information need. Automatic que...
Many successful query expansion techniques ignore informa-tion about the term dependencies that exis...
Many successful query expansion techniques ignore information about the term dependencies that exist...
In This paper, we present a survey of important work done on automatic query expansion. Automatic qu...
We develop a deductive data model for concept-based query expansion. It is based on three abstractio...
Abstract—Effective search in structured information based on textual user input is of high importanc...
Abstract — Query Expansion (QE) is one of the most important mechanisms in the Information Retrieval...
Query Expansion using Pseudo Relevance Feedback is a useful and a popular technique for reformulatin...
Natural language expressions are more familiar to users than choosing keywords for queries. Given th...
The aim of this masters thesis is to examine query expansion. Query expansion is the process of addi...
Query expansion, in the form of pseudo-relevance feedback or relevance feedback, is a common techniq...
This study is in the area of automatic query expansion - automatically expanding query terms with re...
Query understanding has been well studied in the areas of information retrieval and spoken language ...
Poor information retrieval performance has often been attributed to the query-document vocabulary mi...
A user's query is considered to be an imprecise description of their information need. Automatic que...
A user’s query is considered to be an imprecise description of their information need. Automatic que...
Many successful query expansion techniques ignore informa-tion about the term dependencies that exis...
Many successful query expansion techniques ignore information about the term dependencies that exist...
In This paper, we present a survey of important work done on automatic query expansion. Automatic qu...
We develop a deductive data model for concept-based query expansion. It is based on three abstractio...
Abstract—Effective search in structured information based on textual user input is of high importanc...
Abstract — Query Expansion (QE) is one of the most important mechanisms in the Information Retrieval...
Query Expansion using Pseudo Relevance Feedback is a useful and a popular technique for reformulatin...
Natural language expressions are more familiar to users than choosing keywords for queries. Given th...
The aim of this masters thesis is to examine query expansion. Query expansion is the process of addi...
Query expansion, in the form of pseudo-relevance feedback or relevance feedback, is a common techniq...
This study is in the area of automatic query expansion - automatically expanding query terms with re...
Query understanding has been well studied in the areas of information retrieval and spoken language ...