seyed mostafa fakhrahmad

Several factors are involved in the production and presentation of recommender systems.The aim of this study was to investigate the effect of the two challenges lack and sparsity of data on the effectiveness of the proposed results of the RICeST Journal Finder. The corpus includes more than 15,000 articles from technical and engineering publications in the period 2013 to 2017, which have been collected from their website. Textual elements of these articles were extracted, normalized and processed, and a research body database was created. Based on the number of collected articles, by using Cochran's formula, 400 basic articles that previously published in related to the topic of each journal were selected in a random-proportional method. Title and abstract of these articles as a query, in order to receive the system Journals suggested, to print the article in two stages of before and after improving the two challenges of lack and sparsity of data in the test corpus. The suggested results in each step were saved in Excel. Finally, the effectiveness of the system results in each stage was determined by Leave-one-out Cross-Validation method and based on the accuracy criterion in k.The relative abundance of categories showed that, in the current situation, the target journal was suggested in only 26% of searches in the first 3 ranks. After enriching, normalizing and processing the data and thus improving the lack of data challenge, although 30% of the results were still rated above 10; But the accuracy of the results in the first 3 ranks increased by 15%. Also, after thematically categorizing the data with the aim of improving the sparsity challenge, 30% increase in the accuracy of the system results in the first 3 ranks compared to the previous step was achieved. The results of this study showed that enriching the database, improving the processing process and thematic classification of data in RICeST journal finder can reduce the two challengs lack and sparsity of data and increase the effectiveness of the proposed results of this systems.

This study aims to compare the researchers' views in two fields about the importance of criteria for submitting an article to the journal and the degree of thematic relevance of the proposed results of the RICeST Journal Finder.

The research is a survey in terms of applied purpose and the data collection method and was done in two steps; First, due to the lack of a standard questionnaire, while studying the literature, important and common criteria for the researchers of the two fields were extracted and a researcher-made questionnaire consisting of 13 criteria was prepared. The face and content validity of the questionnaire was done by 10 experts in information science and epistemology. Then, in order to identify the importance of the criteria as well as the thematic relevance of the system's proposed results, subject matter experts (reviewers) in two fields from all over Iran were used.

Criteria of Peer review, thematic relevance of the article with the thematic scope of the journal, and having an impact factor from the perspective of researchers in both groups, were the most important and the age of the journal was the least. Measuring the thematic relevance of the results suggested by the system using the opinions of experts showed that in more than 85% of the queries, the proposed publication for the article is completely relevant. There is no statistically significant difference between the opinions of experts in these two areas. It is necessary to explain that the evaluation of the thematic relevance of the results was done after improving the existing challenges in the RICeST journal finder.

There are many Journals in various scientific fields, so authors are facing challenges to find the most appropriate journal to publish their research findings. The results showed that the importance of the criteria of selecting the journal, from the viewpoint of national researchers, is consistent with the findings of international studies. However, considering the mental variables of researchers in different conditions, it is not possible to consider a single factor category to choose a journal to publish a manuscript; However, the possibility of refining the results, based on the priority criteria of the researchers has been introduced the use of Journal finder systems as an auxiliary tool that can be found more quickly and easily to a list of related publications. Among the other factors that can be examined objectively and are important, regardless of the author's priorities and limitations, is the thematic connection of the manuscript with the journal to which the manuscript is to be sent and published, which is the basis of the performance of the Journal finder systems. In general, according to the obtained results, the authors can use the RICeST journal finder at the national level to obtain relevant journals to publish the manuscript.

In this research, a new algorithm for facets extraction has been developed and introduced, which provides the experimental possibility of identifying facets based on a literary warrant. In the field of automatic facet extraction, two main ideas were considered by reviewing the researches. The first idea is that the facet appears in the context. Therefore, to identify the facet in a corpus, its context must be examined. The second idea is that the facet is the focal point in a lexical tree that is neither very general nor very specific. Based on these two ideas, first, the corpus in the medicine area and the obstetrics and gynaecology domain was prepared. The research team selected three corpora from the literary warrant and used the abstract and title of the collection of articles in the top 20 journals of the field to create a contextual corpus. This collection contained 167071 documents. 2000 articles were randomly selected to create the origin corpus. The third body is the lexical corpus. The proper words of the corpus were extracted using a web-based service. The output contained 514 words. Duplicate words were removed and finally, 480 important words were identified. Then, the words were expanded in the contextual corpus with the help of the guide set- Mesh and then-candidate dissertations were extracted based on the two conditions of frequency-based Shifting and rank-based Shifting. Finally, using the three rules of specificity, substitution, and generality, the identified facets were modified and named. Finally, 26 facets were identified in the domain of gynaecology and obstetrics. Comparing the proposed algorithm with other algorithms, it was found that the combination of statistical approach and tree pruning can have better results than purely statistical approach or tree pruning. Also, the comparison of the output facets of the algorithm with the traditional facets in this obstetrics and gynaecology domain showed that the output of the algorithm is smaller and more useful for browsing information retrieval tools. Also, in this study was specified that specialized domain facets are different from general facets and can be redefined independently, but the results cannot be generalized to all medical domains and other research needs to be done in other fields.

Keyword and phrase extraction is a prerequisite of many natural language processing tasks. However, a review on the related Persian and English literature showed that a few studies have already been done on how to extract keywords and phrases from Persian texts. Thus, Aiming to shed light on the research status of Keyword and phrase extraction from Persian texts, the present study reviews the Persian and English publications which have assessed their research ideas over Persian texts. We also focus on each of the studies to challenge their methodologies, implementations and evaluation methods and measures.To our knowledge, a total number of 14 Persian and 6 English papers exist which have worked on the extraction of Persian keywords and phrases. Investigating on the papers revealed that they were mostly based on statistical and linguistic information. A majority of the papers suffered from the lack of either appropriate methodologies or lucid explanation of their research ideas. They generally used non-standard datasets and vague or problematic metrics to evaluate the experimental systems. Generally speaking, except for 3 papers that appropriately reported their proposed methods, the other papers lacked reproducibility and generalizability. Hence, their results cannot be confidently used as a benchmark in evaluating future works, and their proposed ideas cannot be employed in developing applications for extraction of key words and phrases from Persian texts.

Although the concept of facet analysis has a long background in the classification and information retrieval systems, the use of facet analysis approach in information retrieval systems has been associated with drawbacks. One of these drawbacks is lack of proper attention to the user as the main stakeholder of the system. In this study, a method is presented for appropriate facet extraction in the modern information retrieval systems.

In order to perceive the need of users and achieve the facets of gynecology and obstetrics, the Contractual Content Analysis method with a qualitative approach was employed. First, the information needs of the user group were identified after having interviews with 14 specialists in the fields of gynecology and obstetrics. Then, the information needs were classified with the help of specialists in the subject area and a facet was attributed to each stage. An expert group consisting of eight subject-area specialists and eight specialists in knowledge and information Science evaluated the efficiency of the extracted facets; this way, the agreement was evaluated based on the total agreement formula.

Based on the codes extracted from the interviews related to determining the information needs of stakeholders in the domains of gynecology and obstetrics, 23 facets were identified, 9 of which were identified as proper facets including Age groups, Organ, Therapeutics, diagnosis, Disease, symptoms or Finding, risk factor, Complication, Prognosis by the experts through receiving a coefficient of agreement above 80%.

Facet extraction of information retrieval systems based on the user-oriented approach converts the facets from general to specialized states. In this case, the facets are different for the users of each specialized domain in the user interface; thus, the specialized user interfaces would be formed

 Author's abstract contains those contributions that the author himself considers important. Meanwhile, they may be less important among scientific community. This supplementary information can be obtained by analyzing citing articles. Citation contexts citing a cited article are actually summaries of that article produced by the scientific community. This type of summary is called citation summary which can provide a deeper insight into the impact of that article on scientific community. Selecting useful citation sentences to be inserted in a system summary is one of the major challenges of citation-based automatic text summarization. Hence, the semantic approach of analyzing citation contexts reveals citation functions; it can be used to refine citation contexts and to insert important content in the final summary. So, approaches like metadiscourse analysis that provide more information would result in producing useful summaries. Therefore, this paper aims at analyzing the application of Hyland metadiscourse model for citation-based automatic summarization of scientific texts. Moreover, based on Hyland Metadiscourse Model, an annotation scheme was proposed for citation contexts which could be used in corpus-based citation summarization systems.  

 This is a library research that answers research questions through studying and analyzing resources related to Hyland Metadiscourse Model, Scientific Text Summarization, Citation Context Analysis and Citation Function Classification. The scheme was evolved during two stages of analysis. First, an initial scheme was created based on studying existing schemes. Then, its metadiscourse version was suggested through analyzing Hyland Metadiscourse Model. Expert evaluation was performed for validating the proposed annotation scheme. Three experts in Information Science and two in Linguistics confirmed the scheme.

>Hyland interactional metadiscourse is suitable for analyzing citation contexts because it is used to represent the author's perspective on propositional information and also the reader. Moreover, interactional metadiscourse analysis applies appropriate language tools for the critique genre. Therefore, a scheme was proposed based on boosters, attitude markers, hedges, engagement markers and self-mentions which are the main components of Hyland interactional metadiscourse. The proposed scheme includes 70 classes.

 Hyland interactive metadiscourse can be used to construct proper corpora for automatic citation-based text summarization. Also, some other phases of automatic summarization such as classifier development, citation context refinement, and sentence selection could be performed based on this type of metadiscourse. Annotating corpora is usually performed using an annotation scheme. Thus, the proposed annotation scheme would be beneficial. However, it is a conceptual scheme proposed on existing theories. So, it is necessary to ask annotators to write down any new labels while annotating. Moreover, they should make some notes about the reasons of creating new ones. In the next stage, if desirable agreement is reached those labels could be added to the scheme.

Co-citation forms a relational document network. Co-citation-based measures are found to be effective in retrieving relevant documents. However, they are far from ideal and need further enhancements. Co-opinion concept was proposed and tested in previous research and found to be effective in retrieving relevant documents. The present study endeavors to explore the correlation between opinion (dis)similarity measures and the traditional co-citation-based ones including Citation Proximity Index (CPI), co-citedness and co-citation context similarity. The results show significant, though weak to medium, correlations between the variables. The correlations are direct for co-opinion measure, while being inverse for the opinion distance. Accordingly, the two groups of measures are revealed to represent some similar aspects of the document relation. Moreover, the weakness of the correlations implies that there are different dimensions represented by the two groups

Facet analysis approach (FAA) has exhibited a continuous growth trend since the early 20th century. The present paper is aimed to systematically review the studies and documents of the faceted organization plans as well as thematic and temporal classification of these works. This review led to the identification of the growth and development trends of applying this approach in the information organization and retrieval tools, followed by providing some suggestions for researchers in future works. Accordingly, the steps to be taken in the present work were as follows: The first step included a comprehensive search in relevant references as well as a primary review of the documents, followed by classification and refinement of the documents in the second step. The third step addressed temporal and thematic classification of the documents, analysis of the literature, and identification of the existing gaps. In the final step, some suggestions were provided for covering these gaps. The outcomes of the previous works included the development of faceted rankings, faceted glossaries and headings, and faceted information retrieval systems, the extensive use of which continued until 1990s. Subsequently, with the development of computer systems and web, the facets took another role in the retrieval of the data available in the database. During this period, a set of models, faceted metadata, faceted user interfaces, and faceted ontologies was made, which was followed by the development of several software in this field. The facet analysis approach has been developing since the early 20th century to 1990 based on the logical system (a priori) of science classification. However, since then, due to the development of the computer capabilities and growth of the users' needs, the logical perspective was replaced by the computational and user-oriented (a posteriori) perspective. Creating the structure of facets in semantic web environments and formulation of new standards, utilizing more effective methods to perceive user behaviors, and taking historical development and changes of sciences into account are the gaps that still require further studies. Covering these gaps promises sustainable effectiveness of the facet analysis process in future.

This article presents an empirical evaluation to investigate the distributional semantic power of abstract, body and full-text, as different text levels, in predicting the semantic similarity using a collection of open access articles from PubMed. The semantic similarity is measured based on two criteria namely, linear MeSH terms intersection and hierarchical MeSH terms distance. As such, a random sample of 200 queries and 20000 documents are selected from a test collection built on CITREC open source code. Sim Pack Java Library is used to calculate the textual and semantic similarities. The nDCG value corresponding to two of the semantic similarity criteria is calculated at three precision points. Finally, the nDCG values are compared by using the Friedman test to determine the power of each text level in predicting the semantic similarity. The results showed the effectiveness of the text in representing the semantic similarity in such a way that texts with maximum textual similarity are also shown to be 77% and 67% semantically similar in terms of linear and hierarchical criteria, respectively. Furthermore, the text length is found to be more effective in representing the hierarchical semantic compared to the linear one. Based on the findings, it is concluded that when the subjects are homogenous in the tree of knowledge, abstracts provide effective semantic capabilities, while in heterogeneous milieus, full-texts processing or knowledge bases is needed to acquire IR effectiveness.

A bilingual corpus is considered as a very important knowledge source and an inevitable requirement for many natural language processing (NLP) applications in which two languages are involved. For some languages such as Persian, lack of such resources is much more significant. Several applications, including statistical and example-based machine translation needs bilingual corpora, in which large amounts of texts from two different languages have been aligned at the sentence or phrase levels. In order to meet this requirement, this paper aims to propose an accurate and hybrid sentence alignment method for construction of an English-Persian parallel corpus. As the first step, the proposed method uses statistical length based analysis for filtering of candidates. Punctuation marks are used as a directing feature to reduce the complexity and increase the accuracy. Finally, the proposed method makes use of some lexical knowledge in order to produce the final output. . In the phase of lexical analysis, a bilingual dictionary as well as a Persian semantic net (denoted as FarsNet) is used to calculate the extended semantic similarity. Experiments showed the positive effect of expansion on synonym words by extended semantic similarity on the accuracy of the sentence alignment process. In the proposed matching scheme, a semantic load based approach (which considers the verb as the pivot and the main part of a sentence) was also used in order for increasing the accuracy. The results obtained from the experiments were promising and the generated parallel corpus can be used as an effective knowledge source by researchers who work on Persian language.

Machine translation is considered as a branch of machine intelligence with about fifty years background. Ambiguity of language is the most problematic issue in machine translation systems, which may lead to unclear or wrong translation. One of the problems involved in natural language processing is the semantic and structural ambiguity of the words. The objective of this paper to focused on the word sense disambiguation. In here, the existing algorithms for word sense disambiguation are evaluated and a method which is proposed based on the concept, structure and meaning of the words. The experimental results are promising and indicate that this proposed approach significantly outperform its counterparts in terms of disambiguation accuracy.