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2022 ◽  
Vol 40 (4) ◽  
pp. 1-42
Author(s):  
Kelsey Urgo ◽  
Jaime Arguello

Search systems are often used to support learning-oriented goals. This trend has given rise to the “search-as-learning” movement, which proposes that search systems should be designed to support learning. To this end, an important research question is: How does a searcher’s type of learning objective (LO) influence their trajectory (or pathway ) toward that objective? We report on a lab study (N = 36) in which participants gathered information to meet a specific type of LO. To characterize LOs and pathways , we leveraged Anderson and Krathwohl’s (A&K’s) taxonomy [ 3 ]. A&K’s taxonomy situates LOs at the intersection of two orthogonal dimensions: (1) cognitive process (CP) (remember, understand, apply, analyze, evaluate, and create) and (2) knowledge type (factual, conceptual, procedural, and metacognitive knowledge). Participants completed learning-oriented search tasks that varied along three CPs (apply, evaluate, and create) and three knowledge types (factual, conceptual, and procedural knowledge). A pathway is defined as a sequence of learning instances (e.g., subgoals) that were also each classified into cells from A&K’s taxonomy. Our study used a think-aloud protocol, and pathways were generated through a qualitative analysis of participants’ think-aloud comments and recorded screen activities. We investigate three research questions. First, in RQ1, we study the impact of the LO on pathway characteristics (e.g., pathway length). Second, in RQ2, we study the impact of the LO on the types of A&K cells traversed along the pathway. Third, in RQ3, we study common and uncommon transitions between A&K cells along pathways conditioned on the knowledge type of the objective. We discuss implications of our results for designing search systems to support learning.


2022 ◽  
Vol 54 (7) ◽  
pp. 1-38
Author(s):  
Lynda Tamine ◽  
Lorraine Goeuriot

The explosive growth and widespread accessibility of medical information on the Internet have led to a surge of research activity in a wide range of scientific communities including health informatics and information retrieval (IR). One of the common concerns of this research, across these disciplines, is how to design either clinical decision support systems or medical search engines capable of providing adequate support for both novices (e.g., patients and their next-of-kin) and experts (e.g., physicians, clinicians) tackling complex tasks (e.g., search for diagnosis, search for a treatment). However, despite the significant multi-disciplinary research advances, current medical search systems exhibit low levels of performance. This survey provides an overview of the state of the art in the disciplines of IR and health informatics, and bridging these disciplines shows how semantic search techniques can facilitate medical IR. First,we will give a broad picture of semantic search and medical IR and then highlight the major scientific challenges. Second, focusing on the semantic gap challenge, we will discuss representative state-of-the-art work related to feature-based as well as semantic-based representation and matching models that support medical search systems. In addition to seminal works, we will present recent works that rely on research advancements in deep learning. Third, we make a thorough cross-model analysis and provide some findings and lessons learned. Finally, we discuss some open issues and possible promising directions for future research trends.


Author(s):  
M. F. Zianchuk ◽  
I. V. Saltanova

The efficiency of using global Internet search systems versus specialized systems, including the State System of Scientific and Technical Information of the Republic of Belarus, for searching the scientific and technical information is compared in this article. Potential advantages and disadvantages of STI search systems are considered. The experience of individual states in creating and maintaining the functioning of state resources of scientific and technical information is analyzed.


2021 ◽  
Author(s):  
Matukhin Pavel ◽  
Provotorova Elena ◽  
Petrova Marina ◽  
Gracheva Olga ◽  
Rybakova Irina ◽  
...  

The article deals with the issues related to the scientometric indexing of the net resources of the group of language teachers and scientists. A detailed accounting of all types of publications allows us to obtain initial information about the level of their Internet engagement. The analysis was carried out including a wide range of genres of publications, the structure of publications, the format and language of publications, the corpus of academic subjects, the language aspects of the publications under study, the composition of the authors of publications, based on their position. The authors’ use of wide opportunities to present their developments on various Internet resources provides them with the opportunity to be detected by search systems based on artificial intelligence (AI) as well as Big Data techniques and to be most fully characterized by existing and prospective scientometric systems.


Mäetagused ◽  
2021 ◽  
Vol 81 ◽  
pp. 161-176
Author(s):  
Külli Prillop ◽  
◽  
Tiit Hennoste ◽  
Külli Habicht ◽  
Helle Metslang ◽  
...  

Within the project “Pragmatics above grammar: Subjectivity and intersubjectivity in Estonian registers and text types” (PRG341) we are studying the expression of subjectivity and intersubjectivity in different written and spoken registers of modern Estonian. We focus on adverbs that function as discourse markers (e.g. vist ‘maybe, probably’, ilmselt ‘apparently, obviously’, tegelikult ‘actually’), markers that develop from main clauses containing cognition verbs that take sentence complements (e.g. (ma) arvan ‘I think’, usun ‘I believe’, (mulle) tundub ‘it seems (to me), it appears (that)’) as well as modal and performative verbs (e.g. võib (juhtuda) ‘can (happen’, peaks (tulema) ‘should (come)’; kinnitan/väidan (olevat) ‘I affirm/claim’). The analysis combines quantitative corpus-linguistic and qualitative pragmatic approaches, thus belonging to the field of corpus pragmatics. Unlike previous studies of related topics, the project systematically compares the usage of markers in different registers (spoken, online communication, print texts) and text types. The pilot studies performed thus far have revealed several problems with the existing Estonian corpora, important in the study of pragmatics. Firstly, some text types are underrepresented or not represented at all, the text types cannot always be distinguished, and the particular text may not always correspond to the nominal text type (e.g. an academic text may contain quotes from texts of other types). All of this makes it difficult to do comparative statistical analysis of different text types. Secondly, the markers under examination are multifunctional and identifying their (inter)subjective function requires consideration of context broader than a single sentence. However, the public search systems for the existing corpora do not provide this context. For instance, the discourse marker function of cognition verbs is indicated primarily by the fact that the topic of the conversation or text follows through the subordinate clause, not the main clause. Since the available search systems do not provide context larger than a single sentence, the identification of the topic of the discourse, and therefore of the potential discourse-marker function of the verb, is made more difficult. To avoid these problems, the project working group is developing a new “Pragmatics” corpus, being created in the SketchEngine environment. The corpus is made up of 10 subcorpora representing different text types and registers. Each subcorpus contains roughly 500,000 words.


2021 ◽  
Vol 11 (23) ◽  
pp. 11365
Author(s):  
Edna Dias Canedo ◽  
Valério Aymoré Martins ◽  
Vanessa Coelho Ribeiro ◽  
Vinicius Eloy dos Reis ◽  
Lucas Alexandre Carvalho Chaves ◽  
...  

A jurisprudence search system is a solution that makes available to its users a set of decisions made by public bodies on the recurring understanding as a way of understanding the law. In the similarity of legal decisions, jurisprudence seeks subsidies that provide stability, uniformity, and some predictability in the analysis of a case decided. This paper presents a proposed solution architecture for the jurisprudence search system of the Brazilian Administrative Council for Economic Defense (CADE), with a view to building and expanding the knowledge generated regarding the economic defense of competition to support the agency’s final procedural business activities. We conducted a literature review and a survey to investigate the characteristics and functionalities of the jurisprudence search systems used by Brazilian public administration agencies. Our findings revealed that the prevailing technologies of Brazilian agencies in developing jurisdictional search systems are Java programming language and Apache Solr as the main indexing engine. Around 87% of the jurisprudence search systems use machine learning classification. On the other hand, the systems do not use too many artificial intelligence and morphological construction techniques. No agency participating in the survey claimed to use ontology to treat structured and unstructured data from different sources and formats.


2021 ◽  
Vol 39 (4) ◽  
pp. 1-22
Author(s):  
Aldo Lipani ◽  
Ben Carterette ◽  
Emine Yilmaz

As conversational agents like Siri and Alexa gain in popularity and use, conversation is becoming a more and more important mode of interaction for search. Conversational search shares some features with traditional search, but differs in some important respects: conversational search systems are less likely to return ranked lists of results (a SERP), more likely to involve iterated interactions, and more likely to feature longer, well-formed user queries in the form of natural language questions. Because of these differences, traditional methods for search evaluation (such as the Cranfield paradigm) do not translate easily to conversational search. In this work, we propose a framework for offline evaluation of conversational search, which includes a methodology for creating test collections with relevance judgments, an evaluation measure based on a user interaction model, and an approach to collecting user interaction data to train the model. The framework is based on the idea of “subtopics”, often used to model novelty and diversity in search and recommendation, and the user model is similar to the geometric browsing model introduced by RBP and used in ERR. As far as we know, this is the first work to combine these ideas into a comprehensive framework for offline evaluation of conversational search.


2021 ◽  
Vol 39 (4) ◽  
pp. 1-42
Author(s):  
Zeyang Liu ◽  
Ke Zhou ◽  
Max L. Wilson

Conversational search systems, such as Google assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues. Evaluating such systems is very challenging, given that any natural language responses could be generated, and users commonly interact for multiple semantically coherent rounds to accomplish a search task. Although prior studies proposed many evaluation metrics, the extent of how those measures effectively capture user preference remain to be investigated. In this article, we systematically meta-evaluate a variety of conversational search metrics. We specifically study three perspectives on those metrics: (1) reliability : the ability to detect “actual” performance differences as opposed to those observed by chance; (2) fidelity : the ability to agree with ultimate user preference; and (3) intuitiveness : the ability to capture any property deemed important: adequacy, informativeness, and fluency in the context of conversational search. By conducting experiments on two test collections, we find that the performance of different metrics vary significantly across different scenarios, whereas consistent with prior studies, existing metrics only achieve weak correlation with ultimate user preference and satisfaction. METEOR is, comparatively speaking, the best existing single-turn metric considering all three perspectives. We also demonstrate that adapted session-based evaluation metrics can be used to measure multi-turn conversational search, achieving moderate concordance with user satisfaction. To our knowledge, our work establishes the most comprehensive meta-evaluation for conversational search to date.


2021 ◽  
pp. 311-325
Author(s):  
Anas El-Ansari ◽  
Marouane Birjali ◽  
Mustapha Hankar ◽  
Abderrahim Beni-Hssane

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