538 research outputs found

    Temporal search in document streams

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    In this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Examples of temporal document collections are web archives, news archives, blogs, personal emails and enterprise documents. Unfortunately, traditional IR approaches based on termmatching only can give unsatisfactory results when searching temporal document collections. The reason for this is twofold: the contents of documents are strongly time-dependent, i.e., documents are about events happened at particular time periods, and a query representing an information need can be time-dependent as well, i.e., a temporal query. On the other hand, time-only-based methods fall short when it comes to reasoning about events in social media. During the last few years users create chronologically ordered documents about topics that draw their attention in an ever increasing pace. However, with the vast adoption of social media, new types of marketing campaigns have been developed in order to promote content, i.e. brands, products, celebrities, etc

    Documents and Time

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    This essay offers a philosophical account of time and documents. It first presents a number of theories of time and discusses how time has been applied in research on documents to date. These applications have been limited by their conceptualization of time as a physical entity. In order to extend our understanding of documental time, this paper draws from Heidegger\u27s experiential theory of time and the theory of document transaction in order to introduce a theory of documental time. In documental time, the past and future of the person and the past and future of the object cohere in a shared present. The special case of numinous document experiences—and numinous time—is also explored

    Deep Learning for Period Classification of Historical Texts

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    In this study, we address the interesting task of classifying historical texts by their assumed period of writing. This task is useful in digital humanity studies where many texts have unidentified publication dates. For years, the typical approach for temporal text classification was supervised using machine-learning algorithms. These algorithms require careful feature engineering and considerable domain expertise to design a feature extractor to transform the raw text into a feature vector from which the classifier could learn to classify any unseen valid input. Recently, deep learning has produced extremely promising results for various tasks in natural language processing (NLP). The primary advantage of deep learning is that human engineers did not design the feature layers, but the features were extrapolated from data with a general-purpose learning procedure. We investigated deep learning models for period classification of historical texts. We compared three common models: paragraph vectors, convolutional neural networks (CNN), and recurrent neural networks (RNN). We demonstrate that the CNN and RNN models outperformed the paragraph vector model and supervised machine-learning algorithms. In addition, we constructed word embeddings for each time period and analyzed semantic changes of word meanings over time

    Cadre d'évaluation de systÚmes de recherche d'information géographique Apport de la combinaison des dimensions spatiale, temporelle et thématique

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    National audienceCommon search engines process users' queries, i.e., information needs, by extracting terms from documents. Such approaches are limited regarding particular contexts, such as specialized collections (e.g., cultural heritage collections) or specific retrieval criteria (e.g., multidimensional criteria). In this paper, we consider Geographic Information Retrieval Systems (GIRS) exploiting the spatial, temporal, and topical dimensions. Our contribution is twofold as we propose a GIRS evaluation framework for testing the following assumption: combining spatial and temporal dimensions along with the topical dimension improves GIRS effectiveness

    Temporal dynamics in information retrieval

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    The passage of time is unrelenting. Time is an omnipresent feature of our existence, serving as a context to frame change driven by events and phenomena in our personal lives and social constructs. Accordingly, various elements of time are woven throughout information itself, and information behaviours such as creation, seeking and utilisation. Time plays a central role in many aspects of information retrieval (IR). It can not only distinguish the interpretation of information, but also profoundly influence the intentions and expectations of users' information seeking activity. Many time-based patterns and trends - namely temporal dynamics - are evident in streams of information behaviour by individuals and crowds. A temporal dynamic refers to a periodic regularity, or, a one-off or irregular past, present or future of a particular element (e.g., word, topic or query popularity) - driven by predictable and unpredictable time-based events and phenomena. Several challenges and opportunities related to temporal dynamics are apparent throughout IR. This thesis explores temporal dynamics from the perspective of query popularity and meaning, and word use and relationships over time. More specifically, the thesis posits that temporal dynamics provide tacit meaning and structure of information and information seeking. As such, temporal dynamics are a ‘two-way street’ since they must be supported, but also conversely, can be exploited to improve time-aware IR effectiveness. Real-time temporal dynamics in information seeking must be supported for consistent user satisfaction over time. Uncertainty about what the user expects is a perennial problem for IR systems, further confounded by changes over time. To alleviate this issue, IR systems can: (i) assist the user to submit an effective query (e.g., error-free and descriptive), and (ii) better anticipate what the user is most likely to want in relevance ranking. I first explore methods to help users formulate queries through time-aware query auto-completion, which can suggest both recent and always popular queries. I propose and evaluate novel approaches for time-sensitive query auto-completion, and demonstrate state-of-the-art performance of up to 9.2% improvement above the hard baseline. Notably, I find results are reflected across diverse search scenarios in different languages, confirming the pervasive and language agnostic nature of temporal dynamics. Furthermore, I explore the impact of temporal dynamics on the motives behind users' information seeking, and thus how relevance itself is subject to temporal dynamics. I find that temporal dynamics have a dramatic impact on what users expect over time for a considerable proportion of queries. In particular, I find the most likely meaning of ambiguous queries is affected over short and long-term periods (e.g., hours to months) by several periodic and one-off event temporal dynamics. Additionally, I find that for event-driven multi-faceted queries, relevance can often be inferred by modelling the temporal dynamics of changes in related information. In addition to real-time temporal dynamics, previously observed temporal dynamics offer a complementary opportunity as a tacit dimension which can be exploited to inform more effective IR systems. IR approaches are typically based on methods which characterise the nature of information through the statistical distributions of words and phrases. In this thesis I look to model and exploit the temporal dimension of the collection, characterised by temporal dynamics, in these established IR approaches. I explore how the temporal dynamic similarity of word and phrase use in a collection can be exploited to infer temporal semantic relationships between the terms. I propose an approach to uncover a query topic's "chronotype" terms -- that is, its most distinctive and temporally interdependent terms, based on a mix of temporal and non-temporal evidence. I find exploiting chronotype terms in temporal query expansion leads to significantly improved retrieval performance in several time-based collections. Temporal dynamics provide both a challenge and an opportunity for IR systems. Overall, the findings presented in this thesis demonstrate that temporal dynamics can be used to derive tacit structure and meaning of information and information behaviour, which is then valuable for improving IR. Hence, time-aware IR systems which take temporal dynamics into account can better satisfy users consistently by anticipating changing user expectations, and maximising retrieval effectiveness over time

    Toward Enhanced Metadata Quality of Large-Scale Digital Libraries: Estimating Volume Time Range

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    In large-scale digital libraries, it is not uncommon that some bibliographic fields in metadata records are incomplete or missing. Adding to the incomplete or missing metadata can greatly facilitate users' search and access to digital library resources. Temporal information, such as publication date, is a key descriptor of digital resources. In this study, we investigate text mining methods to automatically resolve missing publication dates for the HathiTrust corpora, a large collection of documents digitized by optical character recognition (OCR). In comparison with previous approaches using only unigrams as features, our experiment results show that methods incorporating higher order n-gram features, e.g., bigrams and trigrams, can more effectively classify a document into discrete temporal intervals or "chronons". Our approach can be generalized to classify volumes within other digital libraries.ye
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