First international workshop on recent trends in news information retrieval (NewsIR’16)

The news industry has gone through seismic shifts in the past decade with digital content and social media completely redefining how people consume news. Readers check for accurate fresh news from multiple sources throughout the day using dedicated apps or social media on their smartphones and tablets. At the same time, news publishers rely more and more on social networks and citizen journalism as a frontline to breaking news. In this new era of fast-flowing instant news delivery and consumption, publishers and aggregators have to overcome a great number of challenges. These include the verification or assessment of a source’s reliability; the integration of news with other sources of information; real-time processing of both news content and social streams in multiple languages, in different formats and in high volumes; deduplication; entity detection and disambiguation; automatic summarization; and news recommendation. Although Information Retrieval (IR) applied to news has been a popular research area for decades, fresh approaches are needed due to the changing type and volume of media content available and the way people consume this content. The goal of this workshop is to stimulate discussion around new and powerful uses of IR applied to news sources and the intersection of multiple IR tasks to solve real user problems. To promote research efforts in this area, we released a new dataset consisting of one million news articles to the research community and introduced a data challenge track as part of the workshop

A Semantic Graph-Based Approach for Mining Common Topics From Multiple Asynchronous Text Streams

In the age of Web 2.0, a substantial amount of unstructured content are distributed through multiple text streams in an asynchronous fashion, which makes it increasingly difficult to glean and distill useful information. An effective way to explore the information in text streams is topic modelling, which can further facilitate other applications such as search, information browsing, and pattern mining. In this paper, we propose a semantic graph based topic modelling approach for structuring asynchronous text streams. Our model in- tegrates topic mining and time synchronization, two core modules for addressing the problem, into a unified model. Specifically, for handling the lexical gap issues, we use global semantic graphs of each timestamp for capturing the hid- den interaction among entities from all the text streams. For dealing with the sources asynchronism problem, local semantic graphs are employed to discover similar topics of different entities that can be potentially separated by time gaps. Our experiment on two real-world datasets shows that the proposed model significantly outperforms the existing ones

Weighting Passages Enhances Accuracy

We observe that in curated documents the distribution of the occurrences of salient terms, e.g., terms with a high Inverse Document Frequency, is not uniform, and such terms are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of the document. We study a multiplicity of partitioning schemes of document content into passages and compute the collection-dependent weights associated with them on the basis of the distribution of occurrences of salient terms in documents. Moreover, we tune BM25P hyperparameters and investigate their impact on ad hoc document retrieval through fully reproducible experiments conducted using four publicly available datasets. Our findings demonstrate that our BM25P weighting model markedly and consistently outperforms BM25 in terms of effectiveness by up to 17.44% in NDCG@5 and 85% in NDCG@1, and up to 21% in MRR

Agenda

Jornadas

State of the art document clustering algorithms based on semantic similarity

The constant success of the Internet made the number of text documents in electronic forms increases hugely. The techniques to group these documents into meaningful clusters are becoming critical missions. The traditional clustering method was based on statistical features, and the clustering was done using a syntactic notion rather than semantically. However, these techniques resulted in un-similar data gathered in the same group due to polysemy and synonymy problems. The important solution to this issue is to document clustering based on semantic similarity, in which the documents are grouped according to the meaning and not keywords. In this research, eighty papers that use semantic similarity in different fields have been reviewed; forty of them that are using semantic similarity based on document clustering in seven recent years have been selected for a deep study, published between the years 2014 to 2020. A comprehensive literature review for all the selected papers is stated. Detailed research and comparison regarding their clustering algorithms, utilized tools, and methods of evaluation are given. This helps in the implementation and evaluation of the clustering of documents. The exposed research is used in the same direction when preparing the proposed research. Finally, an intensive discussion comparing the works is presented, and the result of our research is shown in figures

Effectiveness of Data Enrichment on Categorization: Two Case Studies on Short Texts and User Movements

The widespread diffusion of mobile devices, e.g., smartphones and tablets, has made possible a huge increment in data generation by users. Nowadays, about a billion users daily interact on online social media, where they share information and discuss about a wide variety of topics, sometimes including the places they visit. Furthermore, the use of mobile devices makes available a large amount of data tracked by integrated sensors, which monitor several users’ activities, again including their position. The content produced by users are composed of few elements, such as only some words in a social post, or a simple GPS position, therefore a poor source of information to analyze. On this basis, a data enrichment process may provide additional knowledge by exploiting other related sources to extract additional data. The aim of this dissertation is to analyze the effectiveness of data enrichment for categorization, in particular on two domains, short texts and user movements. We de- scribe the concept behind our experimental design where users’ content are represented as abstract objects in a geometric space, with distances representing relatedness and similarity values, and contexts representing regions close to the each object where it is possibile to find other related objects, and therefore suitable as data enrichment source. Regarding short texts our research involves a novel approach on short text enrichment and categorization, and an extensive study on the properties of data used as enrich- ment. We analyze the temporal context and a set of properties which characterize data from an external source in order to properly select and extract additional knowledge related to textual content that users produce. We use Twitter as short texts source to build datasets for all experiments. Regarding user movements we address the problem of places categorization recognizing important locations that users visit frequently and intensively. We propose a novel approach on places categorization based on a feature space which models the users’ movement habits. We analyze both temporal and spa- tial context to find additional information to use as data enrichment and improve the importance recognition process. We use an in-house built dataset of GPS logs and the GeoLife public dataset for our experiments. Experimental evaluations on both our stud- ies highlight how the enrichment phase has a considerable impact on each process, and the results demonstrate its effectiveness. In particular, the short texts analysis shows how news articles are documents particularly suitable to be used as enrichment source, and their freshness is an important property to consider. User Movements analysis demonstrates how the context with additional data helps, even with user trajectories difficult to analyze. Finally, we provide an early stage study on user modeling. We exploit the data extracted with enrichment on the short texts to build a richer user profile. The enrichment phase, combined with a network-based approach, improves the profiling process providing higher scores in similarity computation where expectedCo-supervisore: Ivan ScagnettoopenDottorato di ricerca in Informaticaope

An enhanced binary bat and Markov clustering algorithms to improve event detection for heterogeneous news text documents

Event Detection (ED) works on identifying events from various types of data. Building an ED model for news text documents greatly helps decision-makers in various disciplines in improving their strategies. However, identifying and summarizing events from such data is a non-trivial task due to the large volume of published heterogeneous news text documents. Such documents create a high-dimensional feature space that influences the overall performance of the baseline methods in ED model. To address such a problem, this research presents an enhanced ED model that includes improved methods for the crucial phases of the ED model such as Feature Selection (FS), ED, and summarization. This work focuses on the FS problem by automatically detecting events through a novel wrapper FS method based on Adapted Binary Bat Algorithm (ABBA) and Adapted Markov Clustering Algorithm (AMCL), termed ABBA-AMCL. These adaptive techniques were developed to overcome the premature convergence in BBA and fast convergence rate in MCL. Furthermore, this study proposes four summarizing methods to generate informative summaries. The enhanced ED model was tested on 10 benchmark datasets and 2 Facebook news datasets. The effectiveness of ABBA-AMCL was compared to 8 FS methods based on meta-heuristic algorithms and 6 graph-based ED methods. The empirical and statistical results proved that ABBAAMCL surpassed other methods on most datasets. The key representative features demonstrated that ABBA-AMCL method successfully detects real-world events from Facebook news datasets with 0.96 Precision and 1 Recall for dataset 11, while for dataset 12, the Precision is 1 and Recall is 0.76. To conclude, the novel ABBA-AMCL presented in this research has successfully bridged the research gap and resolved the curse of high dimensionality feature space for heterogeneous news text documents. Hence, the enhanced ED model can organize news documents into distinct events and provide policymakers with valuable information for decision making