Integrated Learning of Features and Ranking Function in Information Retrieval

2019 ◽  
Author(s):  
Yifan Nie ◽  
Jiyang Zhang ◽  
Jian-Yun Nie
Keyword(s):  
Information Retrieval ◽  
Ranking Function ◽  
Integrated Learning

scholarly journals Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus

2019 ◽  
Vol 15 (3) ◽  
pp. 1527
Author(s):  
Shaiful Bakhtiar Bin Rodzman ◽  
Normaly Kamal Ismail ◽  
Nurazzah Abd Rahman ◽  
Syed Ahmad Aljunid ◽  
Zulhilmi Mohamed Nor ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Information Retrieval ◽  
Vector Space ◽  
Fuzzy Logic Controller ◽  
Vector Space Model ◽  
Ranking Function ◽  
Specific Information ◽  
Inference System ◽  
Space Model ◽  
Domain Specific

<span>Ranking function is a predictive algorithm that is used to establish a simple ordering of documents according to its relevance. This step is critical because the results’ quality of a Domain Specific Information Retrieval (IR) such as Hadith Information Retrieval is fundamentally dependent of the ranking function. A Hierarchical Fuzzy Logic Controller of <em>Mamdani</em>-type Fuzzy Inference System has been built to define the ranking function, based on the Malay Information retrieval’s BM25 Model. The model examines three-inputs (Ontology BM25 Score, Fabrication Rate of Hadith and Shia Rate of Hadith) and four-output values of Final Ranking Score which consist of three triangular membership functions. The proposed system has outperformed the BM25 original score and the Vector Space Model (VM) on 16 queries, while the BM25 original score and Vector Space Model only yield better result in 9 and 2 queries respectively on the P@10, %no measures and MAP. P@10 represent the values of Precision at Rank 10 P@10), %no measures represent the percentage of queries with no relevant documents in the top ten retrieved and MAP represents Mean Average Precision of the queries. The results show the proposed system have capability to demote negative documents and move up the relevant documents in the ranking list and its capability to recall unseen document with the application of ontology in text retrieval. For the future works, the researcher would like to apply the usage of other Malay Semantic elements and another corpus for positive ranking indicator.</span>

Discovering Ranking Functions for Information Retrieval

2011 ◽  
pp. 377-381
Author(s):  
Weiguo Fan ◽  
Praveen Pathak
Keyword(s):  
Information Retrieval ◽  
World Wide ◽  
Adaptive Algorithms ◽  
Relevant Information ◽  
Ranking Function ◽  
Ranking Functions ◽  
Document Collections ◽  
Retrieval Systems ◽  
The World ◽  
Document Collection

The field of information retrieval deals with finding relevant documents from a large document collection or the World Wide Web in response to a user’s query seeking relevant information. Ranking functions play a very important role in the retrieval performance of such retrieval systems and search engines. A single ranking function does not perform well across different user queries, and document collections. Hence it is necessary to “discover” a ranking function for a particular context. Adaptive algorithms like genetic programming (GP) are well suited for such discovery.

Generalization Bounds for Certain Class of Ranking Algorithm

2011 ◽  
Vol 267 ◽  
pp. 456-461
Author(s):  
Wei Gao ◽  
Yun Gang Zhang
Keyword(s):  
Information Retrieval ◽  
Ranking Function ◽  
Ranking Algorithm ◽  
Weak Stability ◽  
Undirected Edge ◽  
Generalization Bounds ◽  
Truth Function ◽  
Instance Space ◽  
The Given

The quality of ranking determines the success or failure of information retrieval and the goal of ranking is to learn a real-valued ranking function that induces a ranking or ordering over an instance space. We focus on a ranking setting which uses truth function to label each pair of instances and the ranking preferences are given randomly from some distributions on the set of possible undirected edge sets of a graph. The contribution of this paper is the given generalization bounds for such ranking algorithm via strong and weak stability. Such stabilities have lower demand than uniform stability and fit for more real applications.

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