var titles = new Array(8);
var authors = new Array(8);
var abstracts = new Array(8);

   titles[0] = "Development of an Automatic Trend Exploration System using the MuST Data Collection";
  authors[0] = "Masaki Murata, Koji Ichii, Qing Ma, Tamotsu Shirado, Toshiyuki Kanamaru, Sachiyo Tsukawaki and Hitoshi Isahara";
abstracts[0] = "The automatic extraction of trend information from text documents such as newspaper articles would be useful for exploring and examining trends. To enable this, we used data sets provided by a workshop on multimodal summarization for trend information (the MuST Workshop) to construct an automatic trend exploration system. This system first extracts units, temporals, and item expressions from newspaper articles, then it extracts sets of expressions as trend information, and finally it arranges the sets and displays them in graphs. For example, when documents concerning the politics are given, the system extracts \"%\" and \"Cabinet approval rating\" as a unit and an item expression including temporal expressions. It next extracts values related to \"%\". Finally, it makes a graph where temporal expressions are used for the horizontal axis and the value of percentage is shown on the vertical axis. This graph indicates the trend of Cabinet approval rating and is useful for investigating Cabinet approval rating. Graphs are obviously easy to recognize and useful for understanding information described in documents. In experiments, when we judged the extraction of a correct graph as the top output to be correct, the system accuracy was 0.2500 in evaluation A and 0.3334 in evaluation B. (In evaluation A, a graph where 75% or more of the points were correct was judged to be correct; in evaluation B, a graph where 50% or more of the points were correct was judged to be correct.) When we judged the extraction of a correct graph in the top five outputs to be correct, accuracy rose to 0.4167 in evaluation A and 0.6250 in evaluation B. Our system is convenient and effective because it can output a graph that includes trend information at these levels of accuracy when given only a set of documents as input.";

   titles[1] = "Comparing Information Extraction Pattern Models";
  authors[1] = "Mark Stevenson and Mark A. Greenwood"
abstracts[1] = "Several recently reported techniques for the automatic acquisition of Information Extraction (IE) systems have used dependency trees as the basis of their extraction pattern representation. These approaches have used a variety of pattern models (schemes for representing IE patterns based on particular parts of the dependency analysis). An appropriate model should be expressive enough to represent the information which is to be extracted from text without being overly complicated. Four previously reported pattern models are evaluated using existing IE evaluation corpora and three dependency parsers. It was found that one model, linked chains, could represent around 95% of the information of interest without generating an unwieldy number of possible patterns.";

   titles[2] = "Automatic Extraction of Definitions from German Court Decisions";
  authors[2] = "Stephan Walter and Manfred Pinkal"
abstracts[3] = "This paper deals with the use of computational linguistic analysis techniques for information access and ontology learning within the legal domain. We present a rule-based approach for extracting and analysing definitions from parsed text and evaluate it on a corpus of about 6000 German court decisions. The results are applied to improve the quality of a text based ontology learning method on this corpus.";

   titles[3] = "Improving Semi-supervised Acquisition of Relation Extraction Patterns";
  authors[3] = "Mark A. Greenwood and Mark Stevenson";
abstracts[3] = "This paper presents a novel approach to the semi-supervised learning of Information Extraction patterns. The method makes use of more complex patterns than previous approaches and determines their similarity using a measure inspired by recent work using kernel methods (Culotta and Sorensen, 2004). Experiments show that the proposed similarity measure outperforms a previously reported measure based on cosine similarity when used to perform binary relation extraction.";

   titles[4] = "Automatic Knowledge Representation using a Graph-based Algorithm for Language-Independent Lexical Chaining";
  authors[4] = "Gaël Dias, Cláudia Santos and Guillaume Cleuziou";
abstracts[4] = "Lexical Chains are powerful representations of documents. In particular, they have successfully been used in the field of Automatic Text Summarization. However, until now, Lexical Chaining algorithms have only been proposed for English. In this paper, we propose a greedy Language-Independent algorithm that automatically extracts Lexical Chains from texts. For that purpose, we build a hierarchical lexico-semantic knowledge base from a collection of texts by using the Pole-Based Overlapping Clustering Algorithm. As a consequence, our methodology can be applied to any language and proposes a solution to languagedependent Lexical Chainers.";

   titles[5] = "Data Selection in Semi-supervised Learning for Name Tagging";
  authors[5] = "Heng Ji and Ralph Grishman";
abstracts[5] = "We present two semi-supervised learning techniques to improve a state-of-the-art multi-lingual name tagger. For English and Chinese, the overall system obtains 1.7% - 2.1% improvement in F-measure, representing a 13.5% - 17.4% relative reduction in the spurious, missing, and incorrect tags. We also conclude that simply relying upon large corpora is not in itself sufficient: we must pay attention to unlabeled data selection too. We describe effective measures to automatically select documents and sentences.";

   titles[6] = "LoLo: A System based on Terminology for Multilingual Extraction";
  authors[6] = "Yousif Almas and Khurshid Ahmad";
abstracts[6] = "An unsupervised learning method, based on corpus linguistics and special language terminology, is described that can extract time-varying information from text streams. The method is shown to be \"language-independent\" in that its use leads to sets of regular-expressions that can be used to extract the information in typologically distinct languages like English and Arabic. The method uses the information related to the distribution of Ngrams, for automatically extracting \"meaning bearing\" patterns of usage in a training corpus. The analysis of an English news wire corpus (1,720,142 tokens) and Arabic news wire corpus (1,720,154 tokens) show encouraging results.";

   titles[7] = "Learning Domain-Specific Information Extraction Patterns from the Web";
  authors[7] = "Siddharth Patwardhan and Ellen Riloff";
abstracts[7] = "Many information extraction (IE) systems rely on manually annotated training data to learn patterns or rules for extracting information about events. Manually annotating data is expensive, however, and a new data set must be annotated for each domain. So most IE training sets are relatively small. Consequently, IE patterns learned from annotated training sets often have limited coverage. In this paper, we explore the idea of using the Web to automatically identify domain-specific IE patterns that were not seen in the training data. We use IE patterns learned from the MUC-4 training set as anchors to identify domain-specific web pages and then learn new IE patterns from them. We compute the semantic affinity of each new pattern to automatically infer the type of information that it will extract. Experiments on the MUC-4 test set show that these new IE patterns improved recall with only a small precision loss.";

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		document.write(", <a class='link' href='./papers/IEBD060"+(i+1)+".pdf'><img border='0' src='pdf.png'> Paper</a>");
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