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Word embedding, which represents individual words with semantically fixed-length vectors, has made it possible to successfully apply deep learning to natural language processing tasks such as semantic role-modeling, question answering, and machine translation. As math text consists of natural text, as well as math expressions that similarly exhibit linear correlation and contextual characteristics, word embedding techniques can also be applied to math documents. However, while mathematics is a precise and accurate science, it is usually expressed through imprecise and less accurate descriptions, contributing to the relative dearth of machine learning applications for information retrieval in this domain. Generally, mathematical documents communicate their knowledge with an ambiguous, context-dependent, and non-formal language. Given recent advances in word embedding, it is worthwhile to explore their use and effectiveness in math information retrieval tasks, such as math language processing and semantic knowledge extraction. In this paper, we explore math embedding by testing it on several different scenarios, namely, (1) math-term similarity, (2) analogy, (3) numerical concept-modeling based on the centroid of the keywords that characterize a concept, (4) math search using query expansions, and (5) semantic extraction, i.e., extracting descriptive phrases for math expressions. Due to the lack of benchmarks, our investigations were performed using the arXiv collection of STEM documents and carefully selected illustrations on the Digital Library of Mathematical Functions (DLMF: NIST digital library of mathematical functions. Release 1.0.20 of 2018-09-1, 2018). Our results show that math embedding holds much promise for similarity, analogy, and search tasks. However, we also observed the need for more robust math embedding approaches. Moreover, we explore and discuss fundamental issues that we believe thwart the progress in mathematical information retrieval in the direction of machine learning. [ABSTRACT FROM AUTHOR]

Choice overload describes a situation in which a person has difficulty in making decisions due to too many options. We examine choice overload when displaying related-article recommendations in digital libraries, and examine the effectiveness of recommendation algorithms in this domain. We first analyzed existing digital libraries, and found that only 30% of digital libraries show related-article recommendations to their users. Of these libraries, the majority (74%) displays 3–5 related articles; 28% of them display 6–10 related articles; and no digital library displayed more than ten related-article recommendations. We then conducted our experimental evaluation through GESIS' digital library Sowiport, with recommendations delivered by recommendations-as-a-service provider Mr. DLib. We use four metrics to analyze 41.3 million delivered recommendations: click-through rate (CTR), percentage of clicked recommendation sets (clicked set rate, CSR), average clicks per clicked recommendation set (ACCS), and time to first click (TTFC), which is the time between delivery of a set of recommendations to the first click. These metrics help us to analyze choice overload and can yield evidence for finding the ideal number of recommendations to display. We found that with increasing recommendation set size, i.e., the numbers of displayed recommendations, CTR decreases from 0.41% for one recommendation to 0.09% for 15 recommendations. Most recommendation sets only receive one click. ACCS increases with set size, but increases more slowly for six recommendations and more. When displaying 15 recommendations, the average clicks per set is at a maximum (1.15). Similarly, TTFC increases with larger recommendation set size, but increases more slowly for sets of more than five recommendations. While CTR and CSR do not indicate choice overload, ACCS and TTFC point toward 5–6 recommendations as being optimal for Sowiport. Content-based filtering yields the highest CTR with 0.118%, while stereotype recommendations yield the highest ACCS (1.28). Stereotype recommendations also yield the highest TTFC. This means that users take more time before clicking stereotype recommendations when compared to recommendations based on other algorithms. [ABSTRACT FROM AUTHOR]

Current mathematical search systems allow math expressions within a document to be queried using math expressions and keywords. To accept such queries, math search systems must index both math expressions and textual information in documents. Each indexed math expression is usually associated with all the words in its surrounding context within a given window size. However, we found that this context is often ineffective for explaining math expressions in scientific papers. The meaning of an expression is usually defined in the early part of a document, and the meaning of each symbol contained in the expression can be useful for explaining the entire expression. This explanation may not be captured within the context of a math expression, unless we set the context to have a very wide window size. However, widening the window size also increases the proportion of words that are unrelated to the expression. This paper proposes the use of dependency relationships between math expressions to enrich the textual information of each expression. We examine the influence of this enrichment in a math search system. The experimental results show that significantly better precision can be obtained using the enriched textual information rather than the math expressions' own textual information. This indicates that the enrichment of textual information for each math expression using dependency relationships enhances the math search system. [ABSTRACT FROM AUTHOR]

We built a researcher identifier management system called the Researcher Name Resolver (RNR) to assist with the name disambiguation of authors in digital libraries on the Web. RNR, which is designed to cover all researchers in Japan, is a Web-oriented service that can be openly connected with external scholastic systems. We expect it to be widely used for enriched scholarly communications. In this paper, we first outline the conceptual framework of RNR, which is jointly focused on researcher identifier management and Web resource linking. We based our researcher identifier scheme on the reuse of multiple sets of existing researcher identifiers belonging to the Japanese grant database KAKEN and the researcher directory ReaD & Researchmap. Researcher identifiers are associated by direct links to related resources on the Web through a combination of methods, including descriptive mapping, focused crawling on campus directories and researcher identification by matching names and affiliations. Second, we discuss our implementation of RNR based on this framework. Researcher identifiers construct uniform resource identifiers to show Web pages that describe researcher profiles and provide links to related external resources. We have adapted Web-friendly technologies-e.g., OpenSearch and the RDFs of Linked Data technology-in this implementation to provide Web-friendly services. Third, we discuss our application of RNR to a name disambiguation task for the search portal of the Japanese Institutional Repositories Online to determine how well the researcher identifier management system cooperates with external systems. Finally, we discuss lessons learned from the entire project as well as the future development directions we intend to take. [ABSTRACT FROM AUTHOR]