Proceedings of the 5th Spanish Conference on Information Retrieval最新文献

{"title":"Improving access to scientific literature: a semantic IR perspective","authors":"D. Buscaldi","doi":"10.1145/3230599.3230601","DOIUrl":"https://doi.org/10.1145/3230599.3230601","url":null,"abstract":"Nowadays, the flow of data and publications in almost every field of research is continuously growing. Some estimates place the growth rate in the number of scientific publications between 2.2% and 14% per year, depending on the type and the domain of the publication [6]. This data deluge presents a bottleneck for scientific progress and a challenge for existing search engines. The problems to be solved are some old ones: the ambiguity of a concept, especially among different research fields (for instance, \"lattice\" in computer science vs. physics), and the synonymy (or quasi-synonymy) of concepts that are expressed in different ways: for instance, \"opinion mining\" and \"sentiment analysis\". These issues may affect various tasks: a researcher building a state of the art for a specific topic, an editor finding reviewers for a given paper, or a government official studying a project proposal, among others. The need to go beyond the mere document retrieval in the context of scientific literature is corroborated by the proliferation of related projects and works, and the organization of new shared tasks, in particular the ScienceIE task at SemEval-2017, focused on the identification of keyphrases representing topics, methods, data and tools [1], and task-7 at Semeval-2018 about semantic relation extraction and classification in scientific papers [3]. Some recent works address the problem with the help of structured lists of known keywords, such as Rexplore [7], which integrates statistical analysis with semantic technologies, or by analyzing the citation network among various papers, such as in CiteSpace [2]. In most cases, the relevance, or impact, of a paper is assessed by the number of citations it receives. However, Oren Etzioni1 observed that \"Academics may cite papers for non-essential reasons - out of courtesy, for completeness or to promote their own publications. These superfluous citations can impede literature searches and exaggerate a paper's importance\" and therefore it is necessary to use Artificial Intelligence to discover the meaning and the importance of a specific citation. Recently, at LIPN we started working on the access to scientific information from a semantic information retrieval perspective, therefore leveraging the use of ontologies and similar semantic resources for this task. The first step has been to build a typology of semantic relations [4] that are often used in state of the art sections of scientific paper. Some of these relations link methods and the problems they solve, others link a resource and a system that used it. This typology can evolve or be integrated into more complex ontologies. The next step was to verify whether it is possible to detect these relations automatically. We focused on unsupervised methods that exploit the information coming from keywords and patterns around the entities that are connected by the relations, and tested the possibility to improve these results using semantic embeddings [5]. W","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123912046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying Subsequence Matching to Collaborative Filtering: Extended Abstract","authors":"Alejandro Bellogín, Pablo Sánchez","doi":"10.1145/3230599.3230605","DOIUrl":"https://doi.org/10.1145/3230599.3230605","url":null,"abstract":"Neighbourhood-based approaches, although they are one of the most popular strategies in the recommender systems area, continue using classic similarities that leave aside the sequential information of the users interactions. In this extended abstract, we summarise the main contributions of our previous work where we proposed to use the Longest Common Subsequence algorithm as a similarity measure between users, by adapting it to the recommender systems context and proposing a mechanism to transform users interactions into sequences. Furthermore, we also introduced some modifications on the original LCS algorithm to allow non-exact matchings between users and to bound the similarities obtained in the [0,1] interval. Our reported results showed that our LCS-based similarity was able to outperform different state-of-the-art recommenders in two datasets in both ranking and novelty and diversity metrics.","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recommending Contacts in Social Networks Using Information Retrieval Models","authors":"Javier Sanz-Cruzado, Sofía M. Pepa, P. Castells","doi":"10.1145/3230599.3230619","DOIUrl":"https://doi.org/10.1145/3230599.3230619","url":null,"abstract":"The fast expansion of online social networks has given rise to new challenges and opportunities for information retrieval and, as a particular area, recommender systems. A particularly compelling problem in this context is recommending contacts, that is, automatically predicting people that a given user may wish or benefit from connecting to in the network. This task has interesting particularities compared to more traditional recommendation domains, a salient one being that recommended items belong to the same space as the users they are recommended to. In this paper, we explore the connection between the contact recommendation and the information retrieval (IR) tasks. Specifically, we research the adaptation of IR models for recommending contacts in social networks. We report experiments over data downloaded from Twitter where we observe that IR models are competitive compared to state-of-the art contact recommendation methods.","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115716584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Query Expansion as a Matrix Factorization Problem: Extended Abstract","authors":"Daniel Valcarce, Javier Parapar, Álvaro Barreiro","doi":"10.1145/3230599.3230603","DOIUrl":"https://doi.org/10.1145/3230599.3230603","url":null,"abstract":"Pseudo-relevance feedback (PRF) provides an automatic method for query expansion in Information Retrieval. These techniques find relevant expansion terms using the top retrieved documents with the original query. In this paper, we present an approach based on linear methods called LiMe that formulates the PRF task as a matrix factorization problem. LiMe learns an inter-term similarity matrix from the pseudo-relevant set and the query that uses for computing expansion terms. The experiments on five datasets show that LiMe outperforms state-of-the-art baselines in most cases.","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125183146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in the creation of Spanish web sites and their active service","authors":"Joofre Honores Tapia, Diego Fernández Iglesias, Fidel Cacheda Seijo","doi":"10.1145/3230599.3230613","DOIUrl":"https://doi.org/10.1145/3230599.3230613","url":null,"abstract":"In this research we present statistical information about the domainses that have been created since January 2007 until February 2018, with the objective of providing a clear vision of the different errors they present and how they have changed throughout the years. Moreover, we also provide relevant data about the response times from the web servers when replying to a service request for a web domain, the web page size or the number of input and output links.","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121884591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coarse-grained Semantic Characterization of Large Knowledge Resources","authors":"Rafael Berlanga Llavori, Antonio Jimeno-Yepes, María Pérez, Indira Lanza-Cruz","doi":"10.1145/3230599.3230616","DOIUrl":"https://doi.org/10.1145/3230599.3230616","url":null,"abstract":"This work presents an experimental study about the automatic assignment of semantic groups to concepts of large knowledge resources (KR) such as DBpedia1 or BabelNet2. Our proposal combines a simple lexico-statistical method for hypernym extraction combined with document and word embeddings extracted from Wikipedia. Results are encouraging and open new directions for improving other tasks related to large KR management like debugging and semantic annotation.","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128128996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-effective construction of Information Retrieval test collections","authors":"D. Losada, Javier Parapar, Álvaro Barreiro","doi":"10.1145/3230599.3230612","DOIUrl":"https://doi.org/10.1145/3230599.3230612","url":null,"abstract":"In this paper we describe our recent research on effective construction of Information Retrieval collections. Relevance assessments are a core component of test collections, but they are expensive to produce. For each test query, only a sample of documents in the corpus can be assessed for relevance. We discuss here a class of document adjudication methods that iteratively choose documents based on reinforcement learning. Given a pool of candidate documents supplied by multiple retrieval systems, the production of relevance assessments is modeled as a multi-armed bandit problem. These bandit-based algorithms identify relevant documents with minimal effort. One instance of these models has been adopted by NIST to build the test collection of the TREC 2017 common core track.","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129912294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Term Association Measures for Memory-based Recommender Systems","authors":"Eva Suárez-García, Alfonso Landin, Daniel Valcarce, Álvaro Barreiro","doi":"10.1145/3230599.3230606","DOIUrl":"https://doi.org/10.1145/3230599.3230606","url":null,"abstract":"The adaptation of Information Retrieval techniques for the item recommendation task has become a fertile research area. Previous works have established the correspondence between these two fields that allowed to adapt several retrieval techniques successfully. One line of study aims to model the item recommendation problem as a profile expansion task following the methods for query expansion in pseudo-relevance feedback. To solve the query expansion task in ad-hoc retrieval, several term association measures have been proposed in the past. In this paper, we adapt several of these measures to the top-N recommendation problem, specifically to the collaborative filtering scenario. Moreover, we perform experiments to study their effectiveness regarding accuracy, diversity and novelty. Our results show that some of the proposed measures can improve these aspects over well-known and commonly used recommendation similarity metrics (cosine similarity and Pearson's correlation coefficient).","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133475008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards the Development of a Tool to Keep Track of Interesting Information in a Sea of Digital Documents: Short Paper (Work in Progress)","authors":"S. Ilarri, Guillermo Azón","doi":"10.1145/3230599.3230610","DOIUrl":"https://doi.org/10.1145/3230599.3230610","url":null,"abstract":"Managing the current data deluge is a great challenge for users. Emails are constantly arriving, notifications of tweets and RSS feeds keep popping out, newspapers and blogs of different types publish potentially-relevant news every day, etc. If a user wants to keep track of certain topics in an efficient way, a careful filtering is needed in order to keep the number of items to review manageable, as otherwise the user may finally give up or just perform some random or casual reading. Automated tools can help the user to perform this initial selection, and thus to minimize the feeling of being overwhelmed that the user may experience. In this short paper, we present our ongoing work for the development of DodoAid, a recommender of digital objects that attempts to alleviate the current user's overload when he/she wants to follow information about certain topics. Beyond the application of information retrieval and text mining techniques, it can also apply techniques from the field of recommender systems to suggest items that not only fit topics of interest for the user but are also expected to be valuable according to the individual user's preferences, which can be learnt automatically in an implicit way.","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116803143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Click-through prediction when searching local businesses","authors":"Fidel Cacheda, Nicola Barbieri","doi":"10.1145/3230599.3230609","DOIUrl":"https://doi.org/10.1145/3230599.3230609","url":null,"abstract":"Local search engines allow users to issue queries with a geographical connotation, named local searches, against a business database. Local search differs from traditional search in that, in order to capture adequately the user behaviour, the relevance estimation must integrate geographical signals, such as distance. In this work we investigate the problem of estimating the click-through in local searches using standard search methods along with a set of geographical features and business related. Our approach is validated using the logs of a local search engine. The evaluation shows how the non-linear combination of features of business, geo-local and textual allow a significant improvement over state-of-the-art alternatives based on text relevance, distance and business reputation.","PeriodicalId":448209,"journal":{"name":"Proceedings of the 5th Spanish Conference on Information Retrieval","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122247019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}