Wen-Cheng Lin

Resolving Translation Ambiguity and Target Polysemy in Cross-Language Information Retrieval

This paper deals with translation ambiguity and target polysemy problems together. Two monolingual balanced corpora are employed to learn word co-occurrence for translation ambiguity resolution, and augmented translation restrictions for target polysemy resolution. Experiments show that the model achieves 62.92% of monolingual information retrieval, and is 40.80% addition to the select-all model. Combining the target polysemy resolution, the retrieval performance is about 10.11% increase to the model resolving translation ambiguity only.

Merging mechanisms in multilingual information retrieval

This paper considers centralized and distributed architectures for multilingual information retrieval. Several merging strategies, including raw-score merging, round-robin merging, normalized-score merging, and normalized-by-top-k merging, were investigated. The effects of translation penalty on merging was also examined. The experimental results show that the centralized approach is better than the distributed approach. In the distributed approach, the normalized-by-top-k merging with translation penalty outperforms other merging strategies, except for raw-score merging. Because the performances of English to other languages are similar, raw-score merging gives better performance in our experiments. However, raw-score merging is not workable in practice if different IR systems are adopted.

Building a Chinese-English wordnet for translingual applications

A WordNet-like linguistic resource is useful, but difficult to construct. This article proposes a method to integrate five linguistic resources, including English/Chinese sense-tagged corpora, English/Chinese thesauruses, and a bilingual dictionary. Chinese words are mapped into WordNet. A Chinese WordNet and a Chinese-English WordNet are derived by following the structures of WordNet. Experiments with Chinese-English information retrieval are developed to evaluate the applicability of the Chinese-English WordNet. The best model achieves 0.1010 average precision, 69.23% of monolingual information retrieval. It also gains a 10.02% increase relative to a model that resolves translation ambiguity and target polysemy problems together.

Construction of a Chinese-English WordNet and its application to CLIR

This paper integrates five linguistic resources, including Cilin, a Chinese-English dictionary, ASBC corpus, SemCor, and WordNet, to construct a Chinese-English WordNet. The result is employed in Chinese-English information retrieval. Under TREC-6 text collection, TREC topics 301-350, and Smart information retrieval system, the model for CLIR achieves 69.23% of monolingual information retrieval. It also gains 10.02% increase relative to a model that resolves translation ambiguity and target polysemy together using a Chinese-English dictionary.

Integrating textual and visual information for cross-language image retrieval: a trans-media dictionary approach

This paper explores the integration of textual and visual information for cross-language image retrieval. An approach which automatically transforms textual queries into visual representations is proposed. First, we mine the relationships between text and images and employ the mined relationships to construct visual queries from textual ones. Then, the retrieval results of textual and visual queries are combined. To evaluate the proposed approach, we conduct English monolingual and Chinese-English cross-language retrieval experiments. The selection of suitable textual query terms to construct visual queries is the major issue. Experimental results show that the proposed approach improves retrieval performance, and use of nouns is appropriate to generate visual queries.

Merging Results by Predicted Retrieval Effectiveness

In this paper we propose several merging strategies to integrate the result lists of each intermediate run in distributed MLIR. The prediction of retrieval effectiveness was used to adjust the similarity scores of documents in the result lists. We introduced three factors affecting the retrieval effectiveness, i.e., the degree of translation ambiguity, the number of unknown words and the number of relevant documents in a collection for a given query. The results showed that the normalized-by-top-k merging with translation penalty and collection weight outperformed the other merging strategies except for the raw-score merging.

Foreign Name Backward Transliteration in Chinese-English Cross-Language Image Retrieval

In this paper we propose an approach to deal with the Chinese-English cross-language image retrieval problem. Text-based image retrieval and query translation methods were adopted in the experiments. A similarity-based backward transliteration model with candidate filter was proposed to translate proper nouns that have no entries in a bilingual dictionary. The experimental results showed that using similarity-based backward transliteration increased retrieval performances.

A corpus-based relevance feedback approach to cross-language image retrieval

This paper regards images with captions as a cross-media parallel corpus, and presents a corpus-based relevance feedback approach to combine the results of visual and textual runs. Experimental results show that this approach performs well. Comparing with the mean average precision (MAP) of the initial visual retrieval, the MAP is increased from 8.29% to 34.25% after relevance feedback from cross-media parallel corpus. The MAP of cross-lingual image retrieval is increased from 23.99% to 39.77% if combining the results of textual run and visual run with relevance feedback. Besides, the monolingual experiments also show the consistent effects of this approach. The MAP of monolingual retrieval is improved from 39.52% to 50.53% when merging the results of the text and image queries.

Integrating textual and visual information for cross-language image retrieval

This paper explores the integration of textual and visual information for cross-language image retrieval. An approach which automatically transforms textual queries into visual representations is proposed. The relationships between text and images are mined. We employ the mined relationships to construct visual queries from textual ones. The retrieval results of textual and visual queries are combined. We conduct English monolingual and Chinese-English cross-language retrieval experiments to evaluate the proposed approach. The selection of suitable textual query terms to construct visual queries is the major concern. Experimental results show that the proposed approach improves retrieval performance, and nouns are appropriate to generate visual queries.

AN NTU-APPROACH TO AUTOMATIC SENTENCE EXTRACTION FOR SUMMARY GENERATION

Automatic summarization and information extraction are two important Internet services. MUC and SUMMAC play their appropriate roles in the next generation Internet. This paper focuses on the automatic summarization and proposes two different models to extract sentences for summary generation under two tasks initiated by SUMMAC-1. For categorization task, positive feature vectors and negative feature vectors are used cooperatively to construct generic, indicative summaries. For adhoc task, a text model based on relationship between nouns and verbs is used to filter out irrelevant discourse segment, to rank relevant sentences, and to generate the user-directed summaries. The result shows that the NormF of the best summary and that of the fixed summary for adhoc tasks are 0.456 and 0.447. The NormF of the best summary and that of the fixed summary for categorization task are 0.4090 and 0.4023. Our system outperforms the average system in categorization task but does a common job in adhoc task.