164 research outputs found

    On the Impact of Entity Linking in Microblog Real-Time Filtering

    Full text link
    Microblogging is a model of content sharing in which the temporal locality of posts with respect to important events, either of foreseeable or unforeseeable nature, makes applica- tions of real-time filtering of great practical interest. We propose the use of Entity Linking (EL) in order to improve the retrieval effectiveness, by enriching the representation of microblog posts and filtering queries. EL is the process of recognizing in an unstructured text the mention of relevant entities described in a knowledge base. EL of short pieces of text is a difficult task, but it is also a scenario in which the information EL adds to the text can have a substantial impact on the retrieval process. We implement a start-of-the-art filtering method, based on the best systems from the TREC Microblog track realtime adhoc retrieval and filtering tasks , and extend it with a Wikipedia-based EL method. Results show that the use of EL significantly improves over non-EL based versions of the filtering methods.Comment: 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 - 17, 201

    Knowledge-based Query Expansion in Real-Time Microblog Search

    Full text link
    Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we propose a new language modeling approach for microblog retrieval by inferring various types of context information. In particular, we expand the query using knowledge terms derived from Freebase so that the expanded one can better reflect users' search intent. Besides, in order to further satisfy users' real-time information need, we incorporate temporal evidences into the expansion method, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on two official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.Comment: 9 pages, 9 figure

    Modeling Temporal Evidence from External Collections

    Full text link
    Newsworthy events are broadcast through multiple mediums and prompt the crowds to produce comments on social media. In this paper, we propose to leverage on this behavioral dynamics to estimate the most relevant time periods for an event (i.e., query). Recent advances have shown how to improve the estimation of the temporal relevance of such topics. In this approach, we build on two major novelties. First, we mine temporal evidences from hundreds of external sources into topic-based external collections to improve the robustness of the detection of relevant time periods. Second, we propose a formal retrieval model that generalizes the use of the temporal dimension across different aspects of the retrieval process. In particular, we show that temporal evidence of external collections can be used to (i) infer a topic's temporal relevance, (ii) select the query expansion terms, and (iii) re-rank the final results for improved precision. Experiments with TREC Microblog collections show that the proposed time-aware retrieval model makes an effective and extensive use of the temporal dimension to improve search results over the most recent temporal models. Interestingly, we observe a strong correlation between precision and the temporal distribution of retrieved and relevant documents.Comment: To appear in WSDM 201

    Sentiment analysis and real-time microblog search

    Get PDF
    This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

    Temporal Information Models for Real-Time Microblog Search

    Get PDF
    Real-time search in Twitter and other social media services is often biased towards the most recent results due to the “in the moment” nature of topic trends and their ephemeral relevance to users and media in general. However, “in the moment”, it is often difficult to look at all emerging topics and single-out the important ones from the rest of the social media chatter. This thesis proposes to leverage on external sources to estimate the duration and burstiness of live Twitter topics. It extends preliminary research where itwas shown that temporal re-ranking using external sources could indeed improve the accuracy of results. To further explore this topic we pursued three significant novel approaches: (1) multi-source information analysis that explores behavioral dynamics of users, such as Wikipedia live edits and page view streams, to detect topic trends and estimate the topic interest over time; (2) efficient methods for federated query expansion towards the improvement of query meaning; and (3) exploiting multiple sources towards the detection of temporal query intent. It differs from past approaches in the sense that it will work over real-time queries, leveraging on live user-generated content. This approach contrasts with previous methods that require an offline preprocessing step

    Hyperlink-extended pseudo relevance feedback for improved microblog retrieval

    Get PDF
    Microblog retrieval has received much attention in recent years due to the wide spread of social microblogging platforms such as Twitter. The main motive behind microblog retrieval is to serve users searching a big collection of microblogs a list of relevant documents (microblogs) matching their search needs. What makes microblog retrieval different from normal web retrieval is the short length of the user queries and the documents that you search in, which leads to a big vocabulary mismatch problem. Many research studies investigated different approaches for microblog retrieval. Query expansion is one of the approaches that showed stable performance for improving microblog retrieval effectiveness. Query expansion is used mainly to overcome the vocabulary mismatch problem between user queries and short relevant documents. In our work, we investigate existing query expansion method (Pseudo Relevance Feedback - PRF) comprehensively, and propose an extension using the information from hyperlinks attached to the top relevant documents. Our experimental results on TREC microblog data showed that Pseudo Relevance Feedback (PRF) alone could outperform many retrieval approaches if configured properly. We showed that combining the expansion terms with the original query by a weight, not to dilute the effect of the original query, could lead to superior results. The weighted combine of the expansion terms is different than what is commonly used in the literature by appending the expansion terms to the original query without weighting. We experimented using different weighting schemes, and empirically found that assigning a small weight for the expansion terms 0.2, and 0.8 for the original query performs the best for the three evaluation sets 2011, 2012, and 2013. We applied the previous weighting scheme to the most reported PRF configuration used in the literature and measured the retrieval performance. The P@30 performance achieved using our weighting scheme was 0.485, 0.4136, and 0.4811 compared to 0.4585, 0.3548, and 0.3861 without applying weighting for the three evaluation sets 2011, 2012 and 2013 respectively. The MAP performance achieved using our weighting scheme was 0.4386, 0.2845, and 0.3262 compared to 0.3592, 0.2074, and 0.2256 without applying weighting for the three evaluation sets 2011, 2012 and 2013 respectively. Results also showed that utilizing hyperlinked documents attached to the top relevant tweets in query expansion improves the results over traditional PRF. By utilizing hyperlinked documents in the query expansion our best runs achieved 0.5000, 0.4339, and 0.5546 P@30 compared to 0.4864, 0.4203, and 0.5322 when applying traditional PRF, and 0.4587, 0.3044, and 0.3584 MAP when applying traditional PRF compared to 0.4405, 0.2850, and 0.3492 when utilizing the hyperlinked document contents (using web page titles, and meta-descriptions) for the three evaluation sets 2011, 2012 and 2013 respectively. We explored different types of information extracted from the hyperlinked documents; we show that using the document titles and meta-descriptions helps in improving the retrieval performance the most. On the other hand, using the meta- keywords degraded the retrieval performance. For the test set released in 2013, using our hyperlinked-extended approach achieved the best improvement over the PRF baseline, 0.5546 P@30 compared to 0.5322 and 0.3584 MAP compared to 0.3492. For the test sets released in 2011 and 2012 we got less improvements over PRF, 0.5000, 0.4339 P@30 compared to 0.4864, 0.4203, and 0.4587, 0.3044 MAP compared to 0.4405, 0.2850. We showed that this behavior was due to the age of the collection, where a lot of hyperlinked documents were taken down or moved and we couldn\u27t get their information. Our best results achieved using hyperlink-extended PRF achieved statistically significant improvements over the traditional PRF for the test sets released in 2011, and 2013 using paired t-test with p-value \u3c 0.05. Moreover, our proposed approach outperformed the best results reported at TREC microblog track for the years 2011, and 2013, which applied more sophisticated algorithms. Our proposed approach achieved 0.5000, 0.5546 P@30 compared to 0.4551, 0.5528 achieved by the best runs in TREC, and 0.4587, 0.3584 MAP compared to 0.3350, 0.3524 for the evaluation sets of 2011 and 2013 respectively. The main contributions of our work can be listed as follows: 1. Providing a comprehensive study for the usage of traditional PRF with microblog retrieval using various configurations. 2. Introducing a hyperlink-based PRF approach for microblog retrieval by utilizing hyperlinks embedded in initially retrieved tweets, which showed a significant improvement to retrieval effectiveness

    Temporal Feedback for Tweet Search with Non-Parametric Density Estimation

    Get PDF
    This paper investigates the temporal cluster hypothesis: in search tasks where time plays an important role, do relevant documents tend to cluster together in time? We explore this question in the context of tweet search and temporal feedback: starting with an initial set of results from a baseline retrieval model, we estimate the temporal density of relevant documents, which is then used for result reranking. Our contributions lie in a method to characterize this temporal density function using kernel density estimation, with and without human relevance judgments, and an approach to integrating this information into a standard retrieval model. Experiments on TREC datasets confirm that our temporal feedback formulation improves search effectiveness, thus providing support for our hypothesis. Our approach outperforms both a standard baseline and previous temporal retrieval models. Temporal feedback improves over standard lexical feedback (with and without human judgments), illustrating that temporal relevance signals exist independently of document content
    corecore