Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain.We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles.We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.
Schaefer2014 proposed the Normalized Relevance Distance (NRD) @cite_12 . This metric measures the semantic relatedness of terms. NRD is based on the co-occurrence of terms in documents, and extends NGD by using relevance weights of terms. The quality of these methods depends on the size of the corpus used.
@cite_12
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We propose the Normalized Relevance Distance (NRD): a robust metric for computing semantic relatedness between terms. NRD makes use of a controlled reference corpus for a statistical analysis. The analysis is based on the relevance scores and joint occurrence of terms in documents. On the basis of established reference datasets, we demonstrate that NRD does not require sophisticated data tuning and is less dependent on the choice of the reference corpus than comparable approaches. Patent prior art search is a type of search in the patent domain where documents are searched for that describe the work previously carried out related to a patent application. The goal of this search is to check whether the idea in the patent application is novel. Vocabulary mismatch is one of the main problems of patent retrieval which results in low retrievability of similar documents for a given patent application. In this paper we show how the term distribution of the cited documents in an initially retrieved ranked list can be used to address the vocabulary mismatch. We propose a method for query modeling estimation which utilizes the citation links in a pseudo relevance feedback set. We first build a topic dependent citation graph, starting from the initially retrieved set of feedback documents and utilizing citation links of feedback documents to expand the set. We identify the important documents in the topic dependent citation graph using a citation analysis measure. We then use the term distribution of the documents in the citation graph to estimate a query model by identifying the distinguishing terms and their respective weights. We then use these terms to expand our original query. We use CLEF-IP 2011 collection to evaluate the effectiveness of our query modeling approach for prior art search. We also study the influence of different parameters on the performance of the proposed method. The experimental results demonstrate that the proposed approach significantly improves the recall over a state-of-the-art baseline which uses the link-based structure of the citation graph but not the term distribution of the cited documents. Question answering (QA) is the task of retrieving an answer in response to a question by analyzing documents. Although most of the efforts in developing QA systems are devoted to dealing with electronic text, we consider it is also necessary to develop systems for document images. In this paper, we propose a method of document image retrieval for such QA systems. Since the task is not to retrieve all relevant documents but to find the answer somewhere in documents, retrieval should be precision oriented. The main contribution of this paper is to propose a method of improving precision of document image retrieval by taking into account the co-occurrence of successive terms in a question. The indexing scheme is based on two-dimensional distributions of terms and the weight of co-occurrence is measured by calculating the density distributions of terms. The proposed method was tested by using 1253 pages of documents about the major league baseball with 20 questions and found that it is superior to the baseline method proposed by the authors. In this thesis, we study the relation between Latent Semantic Indexing (LSI) and the co-occurrence of terms in collections. LSI is a method for automatic indexing and retrieval, which is based on the vector space model and which represents the documents and computes the relevance scores in a reduced, topic-related space. For our study, we view LSI as a document expansion method, i.e. for a pair of terms, the occurrence of one of them in a document increases or decreases the importance of the other term for the document, depending on the respective entry in the expansion matrix. We study the relation between the expansion matrix and the co-occurrence information of the pairs of terms in collections. We find out that the entries of the expansion matrix are influenced by the order of co-occurrence of the pairs of terms. We then show that the retrieval performance of LSI for the optimal choice of parameters can be obtained when the expansion matrix used is a simple linear combination of the first and the second order co-occurrences. Hiermit erklare ich an Eides Statt, diese Diplomarbeit selbststandig angefertigt, nur die angegebenen Quellen benutzt und sie noch keinem anderen Prufungsamt vorgelegt zu haben. Saarbrucken, den 31. Mai 2005