LLM-powered search agents are increasingly being used for multi-step information seeking tasks, yet the IR community lacks empirical understanding of how agentic search sessions unfold and how retrieved evidence is reflected in later queries. This paper presents a large-scale log analysis of agentic search based on 14.44M search requests (3.97M sessions) collected from DeepResearchGym, i.e., an open-source search API accessed by external agentic clients. We sessionize the logs, assign session-level intents and step-wise query-reformulation labels using LLM-based annotation, and propose Context-driven Term Adoption Rate (CTAR) to quantify whether newly introduced query terms are lexically traceable to previously retrieved evidence. Our analyses reveal distinctive behavioral patterns. First, over 90\% of multi-turn sessions contain at most ten steps, and 89\% of inter-step intervals fall under one minute. Second, behavior varies by intent. Fact-seeking sessions exhibit high repetition that increases over time, while sessions requiring reasoning sustain broader exploration. Third, query reformulations are often traceable to retrieved evidence across steps. On average, 54\% of newly introduced query terms appear in the accumulated evidence context, with additional traceability to earlier steps beyond the most recent retrieval. These findings provide candidate signals for repetition-aware stopping, intent-adaptive retrieval budgeting, and explicit cross-step context tracking. We released the anonymized logs, making them available at a public HuggingFace~\chref{https://huggingface.co/datasets/cx-cmu/deepresearchgym-agentic-search-logs}{repository}.

Vision-Language Models (VLMs) have recently experienced significant advancements. However, challenges persist in the accurate recognition of fine details within high resolution images, which limits performance in multiple tasks. This work introduces Pheye, a novel architecture that efficiently processes high-resolution images while training fewer parameters than similarly sized VLMs. Notably, Pheye achieves a high efficiency while maintaining strong performance, particularly in tasks that demand fine-grained image understanding and/or the handling of scene-text.

Neural retrieval models excel in Web search, but their training requires substantial amounts of labeled query-document pairs, which are costly to obtain. With the widespread availability of Web document collections like ClueWeb22, synthetic queries generated by large language models offer a scalable alternative. Still, synthetic training queries often vary in quality, which leads to suboptimal downstream retrieval performance. Existing methods typically filter out noisy query-document pairs based on signals from an external re-ranker. In contrast, we propose a framework that leverages Direct Preference Optimization (DPO) to integrate ranking signals into the query generation process, aiming to directly optimize the model towards generating high-quality queries that maximize downstream retrieval effectiveness. Experiments show higher ranker-assessed relevance between query-document pairs after DPO, leading to stronger downstream performance on the MS~MARCO benchmark when compared to baseline models trained with synthetic data.

Although the International Classification of Diseases (ICD) has been adopted worldwide, manually assigning ICD codes to clinical text is time-consuming, error-prone, and expensive, motivating the development of automated approaches.This paper describes a novel approach for automated ICD coding, combining several ideas from previous related work.We specifically employ a strong Transformer-based model as a text encoder and, to handle lengthy clinical narratives, we explored either (a) adapting the base encoder model into a Longformer, or (b) dividing the text into chunks and processing each chunk independently.The representations produced by the encoder are combined with a label embedding mechanism that explores diverse ICD code synonyms.Experiments with different splits of the MIMIC-III dataset show that the proposed approach outperforms the current state-of-the-art models in ICD coding, with the label embeddings significantly contributing to the good performance.Our approach also leads to properly calibrated classification results, which can effectively inform downstream tasks such as quantification.

Methods based on Contrastive Language-Image Pre-training (CLIP) are nowadays extensively used in support of vision-and-language tasks involving remote sensing data, such as cross-modal retrieval. The adaptation of CLIP to this specific domain has relied on model fine-tuning with the standard contrastive objective, using existing human-labeled image-caption datasets, or using synthetic data corresponding to image-caption pairs derived from other annotations over remote sensing images (e.g., object classes). The use of different pre-training mechanisms has received less attention, and only a few exceptions have considered multilingual inputs. This work proposes a novel vision-and-language model for the remote sensing domain, exploring the fine-tuning of a multilingual CLIP model and testing the use of a self-supervised method based on aligning local and global representations from individual input images, together with the standard CLIP objective. Model training relied on assembling pre-existing datasets of remote sensing images paired with English captions, followed by the use of automated machine translation into nine additional languages. We show that translated data is indeed helpful, e.g. improving performance also on English. Our resulting model, which we named Remote Sensing Multilingual CLIP (RS-M-CLIP), obtains state-of-the-art results in a variety of vision-and-language tasks, including cross-modal and multilingual image-text retrieval, or zero-shot image classification.

Tip-of-the-Tongue (ToT) retrieval is challenging for search engines because the queries are usually natural-language, verbose, and contain uncertain and inaccurate information. This paper studies the generalization capabilities of existing retrieval methods with ToT queries in multiple domains. We curate a multi-domain dataset and evaluate the effectiveness of recall-oriented first-stage retrieval methods across the different domains, considering in-domain, out-of-domain, and multi-domain training settings. We further explore the use of a Large Language Model (LLM), i.e. GPT-4, for zero-shot re-ranking in various ToT domains, relying solely on the item titles. Results show that multi-domain training enhances recall, and that LLMs are strong zero-shot re-rankers, especially for popular items, outperforming direct GPT-4 prompting without first-stage retrieval. Datasets and code can be found on GitHub https://github.com/LuisPB7/TipTongue

Sentiment analysis is a data-driven task, and the resources currently available mostly cover only a couple of text genres in specific contexts. Notably, sentiment analysis advancements have primarily centered on high-resource languages, whereas numerous languages and their speakers are overlooked. This paper introduces SentPT, a novel polarity classifier designed for sentiment analysis of Portuguese-language texts spanning various genres. The aim is to address the gap in multi-genre sentiment analysis by offering a customized solution. To this end, we curate a comprehensive dataset covering different contexts, such as news, literary texts, opinions, comments, social media, and more, followed by preprocessing for consistency. Our proposed classifier adopts a Transfer Learning approach , fine-tuning a BERT model, and is evaluated against a diverse set of texts, including product reviews, literary works, news articles, and game comments. The evaluation employs traditional metrics like precision, recall, and F1-score, with SentPT demonstrating the best overall performance. Our classifier proves effective for formal and informal texts, outperforming existing systems.

Recent advances in image captioning have focused on scaling the data and model size, substantially increasing the cost of pretraining and finetuning. As an alternative to large models, we present Smallcap, which generates a caption conditioned on an input image and related captions retrieved from a datastore. Our model is lightweight and fast to train, as the only learned parameters are in newly introduced cross-attention layers between a pre-trained CLIP encoder and GPT-2 decoder. Smallcap can transfer to new domains without additional finetuning and can exploit large-scale data in a training-free fashion since the contents of the datastore can be readily replaced. Our experiments show that Smallcap, trained only on COCO, has competitive performance on this benchmark, and also transfers to other domains without retraining, solely through retrieval from target-domain data. Further improvement is achieved through the training-free exploitation of diverse human-labeled and web data, which proves to be effective for a range of domains, including the nocaps benchmark, designed to test generalization to unseen visual concepts.11Code: https://github.com/RitaRamo/smallcap.

Inspired by retrieval-augmented language generation and pretrained Vision and Language (V&L) encoders, we present a new approach to image captioning that generates sentences given the input image and a set of captions retrieved from a datastore, as opposed to the image alone.The encoder in our model jointly processes the image and retrieved captions using a pretrained V&L BERT, while the decoder attends to the multimodal encoder representations, benefiting from the extra textual evidence from the retrieved captions.Experimental results on the COCO dataset show that image captioning can be effectively formulated from this new perspective.Our model, named EXTRA, benefits from using captions retrieved from the training dataset, and it can also benefit from using an external dataset without the need for retraining.Ablation studies show that retrieving a sufficient number of captions (e.g., k=5) can improve captioning quality.Our work contributes towards using pretrained V&L encoders for generative tasks, instead of standard classification tasks.

Road object detection in low-visibility conditions, such as nighttime, fog, and rain, is difficult for standard machine learning models, which often struggle because of limited training data. The collection of comprehensive datasets that encompass all possible scenarios encountered during deployment is often impractical in terms of time and cost. To overcome this limitation, this work proposes the use of specific augmentations tailored to address the challenges associated with low-visibility conditions. The model employed in this research was trained on sub-sets that complemented the missing low-visibility circumstances. Augmentations based on depth and Fourier domain techniques were applied to simulate such conditions during training and enhance the model's performance when faced with such a scenario. Experimental results demonstrated that appropriately applied augmentations can improve the model's performance. Specifically, in rainy weather, the best-performing model trained on augmented data achieved a 3.4% improvement over a model trained on non-augmented data. In other cases, however, the proposed augmentations did not increase significantly the per-formance of the classifier.

Task-oriented dialogue systems need to generate appropriate responses to help fulfill users' requests.This paper explores different strategies, namely prompting, retrieval, and finetuning, for task-oriented dialogue generation.Through a systematic evaluation, we aim to provide valuable insights and guidelines for researchers and practitioners working on developing efficient and effective dialogue systems for real-world applications.Evaluation is performed on the MultiWOZ and Taskmaster-2 datasets, and we test various versions of FLAN-T5, GPT-3.5, and GPT-4 models.Costs associated with running these models are analyzed, and dialogue evaluation is briefly discussed.Our findings suggest that when testing data differs from the training data, fine-tuning may decrease performance, favoring a combination of a more general language model and a prompting mechanism based on retrieved examples.

Abstract The study of argumentation is transversal to several research domains, from philosophy to linguistics, from the law to computer science and artificial intelligence. In discourse analysis, several distinct models have been proposed to harness argumentation, each with a different focus or aim. To analyze the use of argumentation in natural language, several corpora annotation efforts have been carried out, with a more or less explicit grounding on one of such theoretical argumentation models. In fact, given the recent growing interest in argument mining applications, argument-annotated corpora are crucial to train machine learning models in a supervised way. However, the proliferation of such corpora has led to a wide disparity in the granularity of the argument annotations employed. In this paper, we review the most relevant theoretical argumentation models, after which we survey argument annotation projects closely following those theoretical models. We also highlight the main simplifications that are often introduced in practice. Furthermore, we glimpse other annotation efforts that are not so theoretically grounded but instead follow a shallower approach. It turns out that most argument annotation projects make their own assumptions and simplifications, both in terms of the textual genre they focus on and in terms of adapting the adopted theoretical argumentation model for their own agenda. Issues of compatibility among argument-annotated corpora are discussed by looking at the problem from a syntactical, semantic, and practical perspective. Finally, we discuss current and prospective applications of models that take advantage of argument-annotated corpora.

Socio-demographic information is usually only accessible at relatively coarse spatial resolutions. However, its availability at thinner granularities is of substantial interest for several stakeholders, since it enhances the formulation of informed hypotheses on the distribution of population indicators. Spatial disaggregation methods aim to compute these fine-grained estimates, often using regression algorithms that employ ancillary data to re-distribute the aggregated information. However, since disaggregation tasks are ill-posed, and given that examples of disaggregated data at the target geospatial resolution are seldom available, model training is particularly challenging. We propose to address this problem through a self-supervision framework that iteratively refines initial estimates from seminal disaggregation heuristics. Specifically, we propose to co-train two different models, using the results from one model to train/refine the other. By doing so, we are able to explore complementary views from the data. We assessed the use of co-training with a fast regressor based on random forests that takes individual raster cells as input, together with a more expressive model, based on a fully-convolutional neural network, that takes raster patches as input. We also compared co-training against the use of self-training with a single model. In experiments involving the disaggregation of a socio-demographic variable collected for Continental Portugal, the results show that our co-training approach outperforms alternative disaggregation approaches, including methods based on self-training or co-training with two similar fully-convolutional models. Co-training is effective at exploring the characteristics of both regression algorithms, leading to a consistent improvement in different types of error metrics.

Interest in argument mining has resulted in an increasing number of argument annotated corpora. However, most focus on English texts with explicit argumentative discourse markers, such as persuasive essays or legal documents. Conversely, we report on the first extensive and consolidated Portuguese argument annotation project focused on opinion articles. We briefly describe the annotation guidelines based on a multi-layered process and analyze the manual annotations produced, highlighting the main challenges of this textual genre. We then conduct a comprehensive inter-annotator agreement analysis, including argumentative discourse units, their classes and relations, and resulting graphs. This analysis reveals that each of these aspects tackles very different kinds of challenges. We observe differences in annotator profiles, motivating our aim of producing a non-aggregated corpus containing the insights of every annotator. We note that the interpretation and identification of token-level arguments is challenging; nevertheless, tasks that focus on higher-level components of the argument structure can obtain considerable agreement. We lay down perspectives on corpus usage, exploiting its multi-faceted nature.

Natural Language Processing (NLP) can offer important tools for unlocking relevant information from clinical narratives. Although Transformer-based models can achieve remarkable results in several different NLP tasks, these models have been less used in clinical NLP, and particularly in low resource languages, of which Portuguese is one example. It is still not entirely clear whether pre-trained Transformer models are useful for clinical tasks, without further architecture engineering or particular training strategies. In this work, we propose a BERT model to assign ICD-10 codes for causes of death, by analyzing free-text descriptions in death certificates, together with the associated autopsy reports and clinical bulletins, from the Portuguese Ministry of Health. We used a novel pre-training procedure that incorporates in-domain knowledge, and also a fine-tuning method to address the class imbalance issue. Experimental results show that, in this particular clinical task that requires the processing of relatively short documents, Transformer-based models can achieve very strong results, significantly outperforming tailored approaches based on recurrent neural networks.

People often ask questions about places, and this is reflected on the frequency of geo-spatial queries made to information retrieval and question answering systems. Recent developments associated to these two types of systems rely on deep neural networks, specifically on methods for passage retrieval based on Transformer models, trained on large datasets like MS-MARCO. Despite significant progress in approaches for retrieving (or re-ranking) passages from a document collection according to their relevance to an input query, few studies have specifically looked at geo-spatial queries (i.e., where-questions directly concerning locations, and also questions covering other informational needs relating to places, their types, and affordances). In this work, we explore neural retrieval models in the context of geo-spatial queries, using a subset of MS-MARCO with questions and passages containing place-names. After characterizing the subset of MS-MARCO, we analyzed a re-ranking strategy based on geographic distance, which we argue to be useful for selecting hard negative examples for model training. Then, we fine-tuned neural ranking models, following bi-encoder or cross-encoder strategies, using the MS-MARCO subset together with a geographically-aware negative sampling procedure. Experimental results show that the fine-tuned models can indeed achieve a superior performance. We also describe a simple knowledge distillation procedure to further improve the computationally more efficient bi-encoder models, using the results of the cross-encoder.

Remote sensing image captioning involves generating a concise textual description for an input aerial image. Most previous methods are based on neural encoder-decoder models trained to generate a sequence of discrete outputs with the standard cross-entropy token-level loss. This paper explores an alternative method based on continuous outputs, generating sequences of embedding vectors instead of directly predicting discrete word tokens. We argue that continuous outputs can facilitate the optimization of semantic similarity, as opposed to exact word-by-word matches. It also facilitates the use of loss functions that compare different views of the data. This includes comparing representations for individual tokens and for the entire captions, and also comparing captions against intermediate image representations. We experimentally compared discrete versus continuous output methods over the RSICD dataset, extensively used in the area. Results show that continuous outputs can indeed lead to better results, and our approach performs competitively with the state-of-the-art model in the area.

Deep neural networks have achieved state-of-the-art results in various vision and/or language tasks. Despite the use of large training datasets, most models are trained by iterating over single input-output pairs, discarding the remaining examples for the current prediction. In this work, we actively exploit the training data, using the information from nearest training examples to aid the prediction both during training and testing. Specifically, our approach uses the target of the most similar training example to initialize the memory state of an LSTM model, or to guide attention mechanisms. We apply this approach to image captioning and sentiment analysis, respectively through image and text retrieval. Results confirm the effectiveness of the proposed approach for the two tasks, on the widely used Flickr8 and IMDB datasets. Our code is publicly available11htttp://github.com/RitaRamo/retrieval-augmentation-nn.

We present a novel deep learning approach for spatio-temporal forecasting with remote sensing data, extending a previous model named Spatio-Temporal Convolutional Sequence to Sequence Network (STConvS2S) in several directions. Experiments using datasets from previous studies show that the proposed approaches outperform the original STConvS2S and other baseline models on tasks related to predicting future time-steps. In tests related to predicting a missing time-step, some of the proposed extensions also lead to improvements over the original STConvS2S architecture, although simpler models seem to be beneficial in this scenario.

Abstract The semantic segmentation of remotely sensed aerial imagery is nowadays an extensively explored task, concerned with determining, for each pixel in an input image, the most likely class label from a finite set of possible labels. Most previous work in the area has addressed the analysis of high‐resolution modern images, although the semantic segmentation of historical grayscale aerial photos can also have important applications. Examples include supporting the development of historical road maps, or the development of dasymetric disaggregation approaches leveraging historical building footprints. Following recent work in the area related to the use of fully‐convolutional neural networks for semantic segmentation, and specifically envisioning the segmentation of grayscale aerial imagery, we evaluated the performance of an adapted version of the W‐Net architecture, which has achieved very good results on other types of image segmentation tasks. Our W‐Net model is trained to simultaneously segment images and reconstruct, or predict, the colour of the input images from intermediate representations. Through experiments with distinct data sets frequently used in previous studies, we show that the proposed W‐Net architecture is quite effective in colouring and segmenting the input images. The proposed approach outperforms a baseline corresponding to the U‐Net model for the segmentation of both coloured and grayscale imagery, and it also outperforms some of the other recently proposed approaches when considering coloured imagery.

Scholars in the humanities have a long tradition in the investigation of spatial theory and methods, as well as on the analysis of space and place within its multiple disciplines. Although technolo...

As cyberbullying becomes more and more frequent in social networks, automatically detecting it and pro-actively acting upon it becomes of the utmost importance. In this work, we study how a recent technique with proven success in similar tasks, Fuzzy Fingerprints, performs when detecting textual cyberbullying in social networks. Despite being commonly treated as binary classification task, we argue that this is in fact a retrieval problem where the only relevant performance is that of retrieving cyberbullying interactions. Experiments show that the Fuzzy Fingerprints slightly outperforms baseline classifiers when tested in a close to real life scenario, where cyberbullying instances are rarer than those without cyberbullying.

Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, enhancing the user's perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected. Modeling phenomena at different LoDs is needed, as there is no exclusive LoD at which data can be analyzed.Current practices work mainly on a single LoD, driven by the analysts perception, ignoring the fact that the identification of the suitable LoDs is a key issue for pointing relevant patterns.This paper presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs patterns emerge or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process.The use of several synthetic and real datasets allowed the evaluation of VAST, which was able to suggest LoDs with different interesting spatiotemporal patterns and the type of expected patterns.

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics.

Following the increasing adoption of online social networks, the users of those services can now easily collect and transmit various types of data instantaneously from real-world locations (e.g., geo-referenced pictures, videos, messages, etc.). This paper argues that one can take advantage of the information that is spread throughout social networks like Twitter, in order to provide a realistic solution for situational awareness that can be useful for decision makers during particular events. Through the generation of English captions assigned to geo-referenced photographs extracted from tweets, and by using these captions to infer important features of those photographs, social monitoring approaches based on the images can produce prompt contextual information that helps in disaster monitoring and response. We specifically describe the use of the Show and Tell model for this purpose. We managed to obtain 100% of precision, 94.42% of accuracy, and 93.93% of recall, in the task of classifying geo-referenced Twitter images depicting the accumulation of snow, useful for applications related to snow removal.

This paper describes Triplex-ST, a novel information extraction system for collecting spatio-temporal information from textual resources. Triplex-ST is based on a distantly supervised approach, which leverages rich linguistic annotations together with information in existing knowledge bases. In particular, we leverage triples associated with temporal and/or spatial contexts, e.g., as available from the YAGO knowledge base, so as to infer templates that capture new facts from previously unseen sentences.

Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed relationships while limiting the semantic drift.We research bootstrapping for relationship extraction using word embeddings to find similar relationships.Experimental results show that relying on word embeddings achieves a better performance on the task of extracting four types of relationships from a collection of newswire documents when compared with a baseline using TF-IDF to find similar relationships.

Abstract Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.

The LocWeb 2014 workshop continues a successful workshop series at the intersection of geospatial search, information management, and Web architecture with a focus towards location-aware information access. The workshop reflects a multitude of fields that demand and utilize location features, featuring presentations that look at the topic of location on the Web from an interdisciplinary perspective, including new approaches dealing with or utilizing geospatial information.

In this paper, we describe a tool to visualize and explore communities of politicians arising from mentions in news articles, combining techniques from network analysis and visual analytics. With this tool one can explore the communities, their organization according to parties, with whom a politician was mentioned with in a given year, or key political events, like the emergence of a new prime-minister. User tests involving our tool, and a collection of Portuguese news articles for a period of 10 years, have shown the interest on this kind of tool, and its applicability to analyze group evolution.

This article presents an automated method for defining the boundaries of imprecise geographic regions, based on publicly available data. The method uses one-class support vector machines (SVMs) for interpolating from a set of point locations, which are assumed to lie in the region whose boundaries are to be defined, and leverages also on a combination of multiple Gaussian kernels, within the formalism of SVMs, to improve accuracy. The points that are used for model training correspond to geospatial coordinates associated with Flickr photos that are tagged with the name of the vague region to be defined. Besides considering latitude and longitude coordinates from Flickr photos, as done in a previous related work, each point location is also associated with a set of descriptive features, obtained from textual annotations and from publicly available raster datasets encoding population counts, terrain elevation, and/or land coverage information. The overall approach is evaluated by means of statistical classification measures, using regions whose boundaries are well defined (i.e., the official boundaries for several European countries). Besides this formal evaluation, we also illustrate our results for several vague regions. Results show that our method performs better than a previous state-of-the-art approach (i.e., we measured an improvement of 5.5% in terms of the F1 metric), which was based solely on interpolating from the geospatial coordinates of known points.

With the prevalence of mobile computing systems and location based services, the research interest on spatiotemporal data has significantly increased, as evidenced by the collection of huge amounts of movement data. Consequently, this type of data raises several issues, namely in the research area of geographic information visualization. Despite the existence of several visual analysis techniques for the exploration of movement data, it is still unclear how usable and useful these techniques are, how can they be improved, and for which situations are these techniques most suitable. In this paper, we present current open challenges on the visual analysis of movement data, and the Ph.D work in progress aiming to address these problems. Our work will explore several factors that may affect the users' performance, and, based on those factors we will propose a taxonomy and an evaluation framework covering different tasks and techniques.

The analysis of human location histories is currently getting an increasing attention, due to the widespread usage of geopositioning technologies such as the GPS, and also of online location-based services that allow users to share this information. Tasks such as the prediction of human movement can be addressed through the usage of these data, in turn offering support for more advanced applications, such as adaptive mobile services with proactive context-based functions. This paper presents an hybrid method for predicting human mobility on the basis of Hidden Markov Models (HMMs). The proposed approach clusters location histories according to their characteristics, and latter trains an HMM for each cluster. The usage of HMMs allows us to account with location characteristics as unobservable parameters, and also to account with the effects of each individual's previous actions. We report on a series of experiments with a real-world location history dataset from the GeoLife project, showing that a prediction accuracy of 13.85% can be achieved when considering regions of roughly 1280 squared meters.

The association of illustrative photos to textual contents is a challenging cross-media retrieval problem with many practical applications. We have, for instance, that the association of photos to specific parts of travelogues, i.e. textual descriptions for travel experiences, may lead to a better usage of these documents. Despite the huge number of high quality photos in websites like Flickr, these photos are currently not being properly explored in cross-media retrieval tasks.

Geo-temporal information is pervasive over textual documents, since most of them contain references to particular locations, calendar dates, clock times or duration periods. An important text analytics problem is therefore related to resolving the place names and the temporal expressions referenced in the texts, i.e. linking the character strings in the documents that correspond to either locations or temporal instances, to the specific geospatial coordinates or the time intervals that they refer to. However, geo-temporal reference resolution presents several non-trivial problems to the area of text mining, due to the inherent ambiguity and contextual assumptions of natural language discourse.

This paper presents an application of PageRank for assigning documents with a corresponding geographical scope. We describe the technique in detail, together with its theoretical formulation. Experimental results are promising, comparing favorably with previous proposals.

Automated methods for resource annotation are a clear necessity, as the success of the semantic Web depends on the availability of Web resources with meta data conforming to known standards and ontologies. This paper describes the WebCAT framework for automatically generating RDF descriptions of Web pages. We present a general view of the system and the algorithms involved, giving an emphasis to typical issues in processing Web data.