Improving pseudo-relevance feedback in web information retrieval using web page segmentation

Yu, Song; Wen, Ji-Rong; Ma, Wei‐Ying

doi:10.1145/775153.775155

Cited by 91 publications

(97 citation statements)

References 11 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…• Co-occurrence-based techniques employ terms highly cooccurring with the initial query retrieved from a corpus (e.g., entire documents [34] or lexical affinity relationships [5]), resulting in an increase of retrieval precision [25]. • Relevance feedback techniques analyse the documents retrieved from the initial query in order to extract related information, in a supervised [7] or unsupervised fashion [27,47]. • Brute-force techniques [17] recursively construct queries from an initial one by adding new terms from a repository of common words until the amount of results is below the maximum number of indexed resources.…”

Section: Query Expansionmentioning

confidence: 99%

Automatic extraction of acronym definitions from the Web

Sánchez

Isern

2009

Appl Intell

View full text Add to dashboard Cite

Acronyms are widely used to abbreviate and stress important concepts. The discovery of the definitions associated to an acronym is an important matter in order to support language processing and knowledge-related tasks as information retrieval, ontology mapping or question answering. Acronyms represent a very dynamic and unbounded topic that is constantly evolving. Manual attempts to compose a global scale dictionary of acronym-definition pairs result in an overwhelming amount of work and limited results. Attending these shortcomings, this paper presents an automatic and unsupervised methodology to generate acronyms and extract their potential definitions from the Web. The method has been designed to minimise the set of constraints, offering a domain and -partially-language independent solution, and to exploit the Web in order to create large and general acronym-definition sets. Results have been manually evaluated against the largest manually built acronym repository: Acronym Finder. The evaluation shows that the proposed approach is able to improve the coverage of manual attempts maintaining a high precision.

show abstract

Section: Query Expansionmentioning

confidence: 99%

Automatic extraction of acronym definitions from the Web

Sánchez

Isern

2009

Appl Intell

View full text Add to dashboard Cite

show abstract

“…For example, Yu et al (2003), investigated a technique in which expansion terms are selected from vision-based segments of a Web page instead of the whole page. Generally speaking, entire Web pages often contain multiple topics and a lot of irrelevant information relating to navigation or decoration within a page.…”

Section: Related Workmentioning

confidence: 99%

Improving mobile search using content enrichment

Church

Smyth

2007

Artif Intell Rev

View full text Add to dashboard Cite

Providing an effective mobile search service is a difficult task given the unique characteristics of the mobile space. Small-screen devices with limited input and interaction capabilities do not make ideal search devices. In addition, mobile content, by its concise nature, offers limited indexing opportunities, which makes it difficult to build high-quality mobile search engines and indexes. In this paper we consider the issue of limited page content by evaluating a heuristic content enrichment framework that uses standard Web resources as a source of additional indexing knowledge. We present an evaluation using a mobile news service that demonstrates significant improvements in search performance compared to a benchmark mobile search engine.

show abstract

“…Their results showed significant improvement in retrieval effectiveness by applying RF mechanisms. More recently, pseudo-relevance feedback in web information retrieval by using Web Page Segmentation has been also introduced [7].…”

Section: Related Workmentioning

confidence: 99%

ICICLE: A semantic-based retrieval system for WWW images

et al. 2006

View full text Add to dashboard Cite

In this paper, we present ICICLE (Image ChainNet and Incremental Clustering Engine), a prototype system that we have developed to efficiently and effectively retrieve WWW images based on image semantics. ICICLE has two distinguishing features. First, it employs a novel image representation model called Weight ChainNet to capture the semantics of the image content. A new formula, called list space model, for computing semantic similarities is also introduced. Second, to speed up retrieval, ICICLE employs an incremental clustering mechanism, ICC (Incremental Clustering on ChainNet), to cluster images with similar semantics into the same partition. Each cluster has a summary representative and all clusters' representatives are further summarized into a balanced and full binary tree structure. We conducted an extensive performance study to evaluate ICICLE. Compared with some recently proposed methods, our results show that ICICLE provides better recall and precision. Our clustering technique ICC facilitates speedy retrieval of images without sacrificing recall and precision significantly.

show abstract

Improving pseudo-relevance feedback in web information retrieval using web page segmentation

Cited by 91 publications

References 11 publications

Automatic extraction of acronym definitions from the Web

Automatic extraction of acronym definitions from the Web

Improving mobile search using content enrichment

ICICLE: A semantic-based retrieval system for WWW images

Contact Info

Product

Resources

About