We explore the use of the landing page content in sponsored search ad selection. Specifically, we compare the use of the ad’s intrinsic content to augmenting the ad with the whole, or parts, of the landing page. We explore two types of extractive summarization techniques to select useful regions from the landing pages: out-of-context and in-context methods. Out-of-context methods select salient regions from the landing page by analyzing the content alone, without taking into account the ad associated with the landing page. In-context methods use the ad context (including its title, creative, and bid phrases) to help identify regions of the landing page that should be used by the ad selection engine. In addition, we introduce a simple yet effective unsupervised algorithm to enrich the ad context to further improve the ad selection. Experimental evaluation confirms that the use of landing pages can significantly improve the quality of ad selection. We also find that our extractive summarization techniques reduce the size of landing pages substantially, while retaining or even improving the performance of ad retrieval over the method that utilize the entire landing page.

In this paper, we describe our participation in the WikipediaMM task at CLEF 2009. Our main efforts concern the expansion of the image metadata from the Wikipedia abstracts collection DBpedia. Since the metadata is short for retrieval by query words, we decided to expand the metadata using a typical query expansion method. In our experiments, we use the Rocchio algorithm for document expansion. Our best run is in the 26th rank of all 57 runs which is under our expectation, and we think that the main reason is that our document expansion method uses all the words from the metadata documents which contain words which are unrelated to the content of the images. Compared with our text retrieval baseline, our best document expansion run improves MAP by 11.17%. As one of our conclusions, we think that the document expansion can play an effective factor in the image metadata retrieval task. Our content-based image retrieval uses the same approach as in our participation in ImageCLEF 2008.

Abstract. We describe and analyze our participation in the WikipediaMM task at ImageCLEF 2010. Our approach is based on text-based image retrieval using information retrieval techniques on the metadata documents of the images. We submitted two English monolingual runs and one multilingual run. The monolingual runs used the query to retrieve the metadata document with the query and document in the same language; the multilingual run used queries in one language to search the metadata provided in three languages. The main focus of our work was using the English query to retrieve images based on the English metadata. For these experiments the English metadata data was expanded using an external resource- DBpedia. This study expanded on our application of document expansion in our previous participation in Image-CLEF 2009. In 2010 we combined document expansion with a document reduction technique which aimed to include only topically important words to the metadata. Our experiments used the Okapi feedback algorithm for document expansion and Okapi BM25 model for retrieval. Experimental results show that combining document expansion with the document reduction method give the best overall retrieval results.

For the multilingual ad-hoc document retrieval track (TEL@CLEF) at at the Cross-Language Retrieval Forum (CLEF) Trinity College Dublin and Dublin City University participated in collaboration. Our retrieval experiments focus on i) investigating document expansion using an entry vocabulary module, ii) translating queries with Google translate and a statistical MT system, and iii) investigating language modeling as a retrieval method. The major results are that the document expansion approach did not increase MAP; topic translation using the statistical MT system resulted in about 70 % of the mean average precision (MAP) achieved when using Google translate for topic translation, and language modeling performs equally or better in comparison with BM25. The bilingual retrieval French and German to English experiments obtained 89 % and 90 % of the best MAP for monolingual English. Categories and Subject Descriptors

Successful information retrieval requires effective matching between the user’s search request and the contents of relevant documents. Often the request entered by a user may not use the same topic relevant terms as the authors ’ of the documents. One potential approach to address problems of query-document term mismatch is document expansion to include additional topically relevant indexing terms in a document which may encourage its retrieval when relevant to queries which do not match its original contents well. We propose and evaluate a new document expansion method using external resources. While results of previous research have been inconclusive in determining the impact of document expansion on retrieval effectiveness, our method is shown to work effectively for text-based image retrieval of

Etter Solutions Research Group participated in the TRECVID conference for the first time in 2007. We submitted five runs in the area of fully automatic search.