Comparative evaluation of query expansion methods for enhanced search on microblog data: DCU ADAPT @ SMERP 2017 workshop data challenge

The rapid growth in the availability of social media content posted during emergency situations is creating significant interest in research into how this information can be exploited to assist emergency relief operations and to help with emergency preparedness and in early warning systems. We describe the DCU ADAPT Centre participation in the microblog search data challenge at the SMERP 2017 workshop. This task aimed to promote development of information retrieval (IR) methods for practical challenges that need to be addressed during an emergency event, along with comparative evaluation of the methodologies developed for this task. The task is based on a large dataset of microblogs posted during the earthquake in Italy in August 2016, together with a set of query topics provided by the task organisers. For our participation in this task we explored use of three different IR techniques: standard IR query expansion based on an external resource, query expansion based on WordNet and use of query expansio

Utilisation of metadata fields and query expansion in cross-lingual search of user-generated Internet video

Recent years have seen signicant eorts in the area of Cross Language Information Retrieval (CLIR) for text retrieval. This work initially focused on formally published content, but more recently research has begun to concentrate on CLIR for informal social media content. However, despite the current expansion in online multimedia archives, there has been little work on CLIR for this content. While there has been some limited work on Cross-Language Video Retrieval (CLVR) for professional videos, such as documentaries or TV news broadcasts, there has to date, been no signicant investigation of CLVR for the rapidly growing archives of informal user generated (UGC) content. Key differences between such UGC and professionally produced content are the nature and structure of the textual UGC metadata associated with it, as well as the form and quality of the content itself. In this setting, retrieval eectiveness may not only suer from translation errors common to all CLIR tasks, but also recognition errors associated with the automatic speech recognition (ASR) systems used to transcribe the spoken content of the video and with the informality and inconsistency of the associated user-created metadata for each video. This work proposes and evaluates techniques to improve CLIR effectiveness of such noisy UGC content. Our experimental investigation shows that dierent sources of evidence, e.g. the content from dierent elds of the structured metadata, significantly affect CLIR effectiveness. Results from our experiments also show that each metadata eld has a varying robustness to query expansion (QE) and hence can have a negative impact on the CLIR eectiveness. Our work proposes a novel adaptive QE technique that predicts the most reliable source for expansion and shows how this technique can be effective for improving CLIR effectiveness for UGC content

A systematic comparison of spatial search strategies for open government datasets

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesDatasets produced or collected by governments are being made publicly available for re-use. Open government data portals help realize such reuse by providing list of datasets and links to access those datasets. This ensures that users can search, inspect and use the data easily. With the rapidly increasing size of datasets in open government data portals, just like it is the case with the web, nding relevant datasets with a query of few keywords is a challenge. Furthermore, those data portals not only consist of textual information but also georeferenced data that needs to be searched properly. Currently, most popular open government data portals like the data.gov.uk and data.gov.ie lack the support for simultaneous thematic and spatial search. Moreover, the use of query expansion hasn't also been studied in open government datasets. In this study we have assessed di erent spatial search strategies and query expansions' performance and impact on user relevance judgment. To evaluate those strategies we harvested machine readable spatial datasets and their metadata from three English based open government data portals, performed metadata enhancement, developed a prototype and performed theoretical and user evaluation. According to the results from the evaluations keyword based search strategy returned limited number of results but the highest relevance rating. In the other hand aggregated spatial and thematic search improved the number of results of the baseline keyword based strategy with a 1 second increase in response time and but decreased relevance rating. Moreover, strategies based on WordNet Synonyms query expansion exhibited the highest relevance rated rst seven results than all other strategies except the keyword based baseline strategy in three out of the four query terms. Regarding the use of Hausdor distance and area of overlap, since documents were returned as results only if they overlap with the query, the number of results returned were the same in both spatial similarities. But strategies using Hausdor distance were of higher relevance rating and average mean than area of overlap based strategies in three of the four queries. In conclusion, while the spatial search strategies assessed in this study can be used to improve the existing keyword based OGDs search approaches, we recommend OGD developers to also consider using WordNet Synonyms based query expansion and hausdor distance as a way of improving relevant spatial data discovery in open government datasets using few keywords and tolerable response time

Contribution à l’amélioration de la recherche d’information par utilisation des méthodes sémantiques: application à la langue arabe

Un système de recherche d’information est un ensemble de programmes et de modules qui sert à interfacer avec l’utilisateur, pour prendre et interpréter une requête, faire la recherche dans l’index et retourner un classement des documents sélectionnés à cet utilisateur. Cependant le plus grand challenge de ce système est qu’il doit faire face au grand volume d’informations multi modales et multilingues disponibles via les bases documentaires ou le web pour trouver celles qui correspondent au mieux aux besoins des utilisateurs. A travers ce travail, nous avons présenté deux contributions. Dans la première nous avons proposé une nouvelle approche pour la reformulation des requêtes dans le contexte de la recherche d’information en arabe. Le principe est donc de représenter la requête par un arbre sémantique pondéré pour mieux identifier le besoin d'information de l'utilisateur, dont les nœuds représentent les concepts (synsets) reliés par des relations sémantiques. La construction de cet arbre est réalisée par la méthode de la Pseudo-Réinjection de la Pertinence combinée à la ressource sémantique du WordNet Arabe. Les résultats expérimentaux montrent une bonne amélioration dans les performances du système de recherche d’information. Dans la deuxième contribution, nous avons aussi proposé une nouvelle approche pour la construction d’une collection de test de recherche d’information arabe. L'approche repose sur la combinaison de la méthode de la stratégie de Pooling utilisant les moteurs de recherches et l’algorithme Naïve-Bayes de classification par l’apprentissage automatique. Pour l’expérimentation nous avons créé une nouvelle collection de test composée d’une base documentaire de 632 documents et de 165 requêtes avec leurs jugements de pertinence sous plusieurs topics. L’expérimentation a également montré l’efficacité du classificateur Bayésien pour la récupération de pertinences des documents, encore plus, il a réalisé des bonnes performances après l’enrichissement sémantique de la base documentaire par le modèle word2vec

An Intelligent Multi-Agent System Approach to Automating Safety Features for On-Line Real Time Communications: Agent Mediated Information Exchange

Child safety online is a growing problem, governmental attempts to highlight and combat this issue have not been as successful as it was hoped, and still there are highly publicised cases of children, young people and vulnerable adults coming to harm as a result of unsafe online practices. This thesis presents the research, design and development of a prototype system called SafeChat, which will provide a safer environment for children interacting in online environments. In order to combat such a complex problem, it is necessary to integrate various artificial intelligent technologies and autonomous systems. The SafeChat prototype system discussed within this research has been implemented in Java Agent Development Environment (JADE) and utilises Protégé Ontology development, reasoning and natural language processing techniques. To evaluate our system performance, comprehensive testing to measure its effectiveness in detecting potential risk to the user (e.g. child) is in constant development. Initial results of system testing are encouraging and demonstrate its effectiveness in identifying different levels of threat during online conversation. The potential impact of this work is immense, when used as a plug-in to popular communications software, such as Facebook Messenger, Skype and WhatsApp. SafeChat provides a safer environment for children to communicate, identifying potential and actual threats, whilst maintaining the privacy of their discourse. The SafeChat system could be easily adapted to provide autonomous solutions in other areas of online threat, such as cyberbullying and radicalisation