Ming-Hung Hsu

Query expansion with conceptnet and wordnet: an intrinsic comparison

This paper compares the utilization of ConceptNet and WordNet in query expansion. Spreading activation selects candidate terms for query expansion from these two resources. Three measures including discrimination ability, concept diversity, and retrieval performance are used for comparisons. The topics and document collections in the ad hoc track of TREC-6, TREC-7 and TREC-8 are adopted in the experiments. The results show that ConceptNet and WordNet are complementary. Queries expanded with WordNet have higher discrimination ability. In contrast, queries expanded with ConceptNet have higher concept diversity. The performance of queries expanded by selecting the candidate terms from ConceptNet and WordNet outperforms that of queries without expansion, and queries expanded with a single resource.

Information retrieval with commonsense knowledge

This paper employs ConceptNet, which covers a rich set of commonsense concepts, to retrieve images with text descriptions by focusing on spatial relationships. Evaluation on test data of the 2005 ImageCLEF shows that integrating commonsense knowledge in information retrieval is feasible.

Tag Normalization and Prediction for Effective Social Media Retrieval

In this paper, we propose a tag normalization algorithm to unify the userspsila annotations. Meanwhile, we explore some general phenomena in a social annotation system and propose a supervised tag prediction model to predict the stabilized tag set of a resource, with feedback of a small amount of user annotation records. The experiments show that a large potion of the stabilized tag set is predicted, and it is feasible to reduce the requirement of sufficient user annotations in the applications of social annotations.

Efficient and effective prediction of social tags to enhance web search

As the web has grown into an integral part of daily life, social annotation has become a popular manner for web users to manage resources. This method of management has many potential applications, but it is limited in applicability by the cold-start problem, especially for new resources on the web. In this article, we study automatic tag prediction for web pages comprehensively and utilize the predicted tags to improve search performance. First, we explore the stabilizing phenomenon of tag usage in a social bookmarking system. Then, we propose a two-stage tag prediction approach, which is efficient and is effective in making use of early annotations from users. In the first stage, content-based ranking, candidate tags are selected and ranked to generate an initial tag list. In the second stage, random-walk re-ranking, we adopt a random-walk model that utilizes tag co-occurrence information to re-rank the initial list. The experimental results show that our algorithm effectively proposes appropriate tags for target web pages. In addition, we present a framework to incorporate tag prediction in a general web search. The experimental results of the web search validate the hypothesis that the proposed framework significantly enhances the typical retrieval model.

A method to predict social annotations

This paper predicts the stabilized tag set of a resource, with feedback of a small amount of user annotations, aiming to reduce the requirement of sufficient user annotations and to resolve the cold-start problem in a social annotation system.

Identification of Relevant and Novel Sentences Using Reference Corpus

The major challenging issue to determine the relevance and the novelty of sentences is the amount of information used in similarity computation among sentences. An information retrieval (IR) with reference corpus approach is proposed. A sentence is considered as a query to a reference corpus, and similarity is measured in terms of the weighting vectors of document lists ranked by IR systems. Two sentences are regarded as similar if they are related to the similar document lists returned by IR systems. A dynamic threshold setting method is presented. Besides IR with reference corpus, we also use IR systems to retrieve sentences from given sentences. The corpus-based approach with dynamic thresholds outperforms direct retrieval approach. The average F-measure of relevance and novelty detection using Okapi system was 0.212 and 0.207, 57.14% and 58.64% of human performance, respectively.

Multilingual relevant sentence detection using reference corpus

IR with reference corpus is one approach when dealing with relevant sentences detection, which takes the result of IR as the representation of query (sentence). Lack of information and language difference are two major issues in relevant detection among multilingual sentences. This paper refers to a parallel corpus for information expansion and translation, and introduces different representations, i.e. sentence-vector, document-vector and term-vector. Both sentence-aligned and document-aligned corpora, i.e., Sinorama corpus and HKSAR corpus, are used. The factors of aligning granularity, the corpus domain, the corpus size, the language basis, and the term selection strategy are addressed. The experiment results show that MRR 0.839 is achieved for similarity computation between multilingual sentences when larger finer grain parallel corpus of the same domain as test data is adopted. Generally speaking, the sentence-vector approach is superior to the term-vector approach when sentence-aligned corpus is employed. The document-vector approach is better than the term-vector approach if document-aligned corpus is used. Considering the language issue, Chinese basis is more suitable to English basis in our experiments. We also employ the translated TREC novelty test bed to evaluate the overall performance. The experimental results show that multilingual relevance detection has 80% of the performance of monolingual relevance detection. That indicates the feasibility of IR with reference corpus approach in relevant sentence detection.