Evaluating tag-based information access in image collections

The availability of social tags has greatly enhanced access to information. Tag clouds have emerged as a new "social" way to find and visualize information, providing both one-click access to information and a snapshot of the "aboutness" of a tagged collection. A range of research projects explored and compared different tag artifacts for information access ranging from regular tag clouds to tag hierarchies. At the same time, there is a lack of user studies that compare the effectiveness of different types of tag-based browsing interfaces from the users point of view. This paper contributes to the research on tag-based information access by presenting a controlled user study that compared three types of tag-based interfaces on two recognized types of search tasks - lookup and exploratory search. Our results demonstrate that tag-based browsing interfaces significantly outperform traditional search interfaces in both performance and user satisfaction. At the same time, the differences between the two types of tag-based browsing interfaces explored in our study are not as clear. Copyright 2012 ACM

Hyperlink-extended pseudo relevance feedback for improved microblog retrieval

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

Core Building Blocks: Next Gen Geo Spatial GPT Application

This paper proposes MapGPT which is a novel approach that integrates the capabilities of language models, specifically large language models (LLMs), with spatial data processing techniques. This paper introduces MapGPT, which aims to bridge the gap between natural language understanding and spatial data analysis by highlighting the relevant core building blocks. By combining the strengths of LLMs and geospatial analysis, MapGPT enables more accurate and contextually aware responses to location-based queries. The proposed methodology highlights building LLMs on spatial and textual data, utilizing tokenization and vector representations specific to spatial information. The paper also explores the challenges associated with generating spatial vector representations. Furthermore, the study discusses the potential of computational capabilities within MapGPT, allowing users to perform geospatial computations and obtain visualized outputs. Overall, this research paper presents the building blocks and methodology of MapGPT, highlighting its potential to enhance spatial data understanding and generation in natural language processing applications

Comparison of two methods on vector space model for trust in social commerce

The study of dealing with searching information in documents within web pages is Information retrieval (IR). The user needs to describe information with comments or reviews that consists of a number of words. Discovering weight of an inquiry term is helpful to decide the significance of a question. Estimation of term significance is a basic piece of most information retrieval approaches and it is commonly chosen through term frequency-inverse document frequency (TF-IDF). Also, improved TF-IDF method used to retrieve information in web documents. This paper presents comparison of TF-IDF method and improved TF-IDF method for information retrieval. Cosine similarity method calculated on both methods. The results of cosine similarity method on both methods compared on the desired threshold value. The relevance documents of TF-IDF method are more extracted than improved TF-IDF method

Modèles de langues pour la détection d'opinions dans les blogs

Cet article décrit une approche de recherche de documents pertinents vis-à-vis d’une requête et exprimant une opinion. Afin de détecter si un document est porteur d’opinion (i.e. comporte de l’information subjective), nous proposons de le comparer à des sources d’information qui comportent du contenu de type opinion. L’intuition derrière cela est la suivante : un document ayant une similarité forte avec des sources d’opinions, est vraisemblablement porteur d’opinion. Pour mesurer cette similarité, nous exploitons des modèles de langue. Nous modélisons le document et la source (référence) porteuse d’opinions par des modèles de langue, nous évaluons ensuite la similarité de ces modèles. Plusieurs expérimentations ont été réalisées sur des collections issues de TREC. Les résultats obtenus valident notre intuition

Combining granularity-based topic-dependent and topic-independent evidences for opinion detection

Fouille des opinion, une sous-discipline dans la recherche d'information (IR) et la linguistique computationnelle, fait référence aux techniques de calcul pour l'extraction, la classification, la compréhension et l'évaluation des opinions exprimées par diverses sources de nouvelles en ligne, social commentaires des médias, et tout autre contenu généré par l'utilisateur. Il est également connu par de nombreux autres termes comme trouver l'opinion, la détection d'opinion, l'analyse des sentiments, la classification sentiment, de détection de polarité, etc. Définition dans le contexte plus spécifique et plus simple, fouille des opinion est la tâche de récupération des opinions contre son besoin aussi exprimé par l'utilisateur sous la forme d'une requête. Il y a de nombreux problèmes et défis liés à l'activité fouille des opinion. Dans cette thèse, nous nous concentrons sur quelques problèmes d'analyse d'opinion. L'un des défis majeurs de fouille des opinion est de trouver des opinions concernant spécifiquement le sujet donné (requête). Un document peut contenir des informations sur de nombreux sujets à la fois et il est possible qu'elle contienne opiniâtre texte sur chacun des sujet ou sur seulement quelques-uns. Par conséquent, il devient très important de choisir les segments du document pertinentes à sujet avec leurs opinions correspondantes. Nous abordons ce problème sur deux niveaux de granularité, des phrases et des passages. Dans notre première approche de niveau de phrase, nous utilisons des relations sémantiques de WordNet pour trouver cette association entre sujet et opinion. Dans notre deuxième approche pour le niveau de passage, nous utilisons plus robuste modèle de RI i.e. la language modèle de se concentrer sur ce problème. L'idée de base derrière les deux contributions pour l'association d'opinion-sujet est que si un document contient plus segments textuels (phrases ou passages) opiniâtre et pertinentes à sujet, il est plus opiniâtre qu'un document avec moins segments textuels opiniâtre et pertinentes. La plupart des approches d'apprentissage-machine basée à fouille des opinion sont dépendants du domaine i.e. leurs performances varient d'un domaine à d'autre. D'autre part, une approche indépendant de domaine ou un sujet est plus généralisée et peut maintenir son efficacité dans différents domaines. Cependant, les approches indépendant de domaine souffrent de mauvaises performances en général. C'est un grand défi dans le domaine de fouille des opinion à développer une approche qui est plus efficace et généralisé. Nos contributions de cette thèse incluent le développement d'une approche qui utilise de simples fonctions heuristiques pour trouver des documents opiniâtre. Fouille des opinion basée entité devient très populaire parmi les chercheurs de la communauté IR. Il vise à identifier les entités pertinentes pour un sujet donné et d'en extraire les opinions qui leur sont associées à partir d'un ensemble de documents textuels. Toutefois, l'identification et la détermination de la pertinence des entités est déjà une tâche difficile. Nous proposons un système qui prend en compte à la fois l'information de l'article de nouvelles en cours ainsi que des articles antérieurs pertinents afin de détecter les entités les plus importantes dans les nouvelles actuelles. En plus de cela, nous présentons également notre cadre d'analyse d'opinion et tâches relieés. Ce cadre est basée sur les évidences contents et les évidences sociales de la blogosphère pour les tâches de trouver des opinions, de prévision et d'avis de classement multidimensionnel. Cette contribution d'prématurée pose les bases pour nos travaux futurs. L'évaluation de nos méthodes comprennent l'utilisation de TREC 2006 Blog collection et de TREC Novelty track 2004 collection. La plupart des évaluations ont été réalisées dans le cadre de TREC Blog track.Opinion mining is a sub-discipline within Information Retrieval (IR) and Computational Linguistics. It refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online sources like news articles, social media comments, and other user-generated content. It is also known by many other terms like opinion finding, opinion detection, sentiment analysis, sentiment classification, polarity detection, etc. Defining in more specific and simpler context, opinion mining is the task of retrieving opinions on an issue as expressed by the user in the form of a query. There are many problems and challenges associated with the field of opinion mining. In this thesis, we focus on some major problems of opinion mining

Analyzing the Impact of RDF Graph Structure on Dataset Search: A Case Study with ACORDAR

openNel mondo del Semantic Web, RDF si pone come elemento cardine per la modellazione precisa dei dati e dei loro legami. L'obiettivo centrale di questo lavoro è esplorare le dinamiche dei grafi RDF, mettendo in luce le principali problematiche e potenzialità nell'ambito della ricerca di dataset. Il caso studio di ACORDAR viene esaminato per illustrare l'effetto delle strutture a grafo sull'organizzazione dei dati. Vengono analizzate le tecniche di serializzazione in RDF, sottolineando la centralità di elementi quali gli URI e le capacità avanzate offerte da SPARQL. Si affronta il tema della riproducibilità di ACORDAR, mettendo in risalto l'importanza dei metadati nella fase di ricerca dei dataset. In conclusione, si delineano prospettive future per ottimizzare la ricerca di dataset, arricchendo l'analisi con informazioni tratte dalle strutture a grafo e avvalendosi delle tecnologie emergenti.RDF plays a central role in the era of the Semantic Web, enabling a structured representation of datasets and their relationships. The complex nature of RDF graph structures significantly influences the retrieval of datasets, offering a blend of both challenges and possibilities. Delving deeply into the ACORDAR case study, the work unveils how graph structures influence dataset retrieval and the organization of data. Furthermore, it introduces serialization methods within RDF, emphasizing the importance of URI and the capabilities of the SPARQL. Presenting the ACORDAR reproducibility, the research underscores the significance of metadata in dataset search. Exploring potential avenues for future research in dataset search, the investigation integrates graph structures and harnesses emerging technologies from the Semantic Web era