Reliability and effectiveness of clickthrough data for automatic image annotation

Automatic image annotation using supervised learning is performed by concept classifiers trained on labelled example images. This work proposes the use of clickthrough data collected from search logs as a source for the automatic generation of concept training data, thus avoiding the expensive manual annotation effort. We investigate and evaluate this approach using a collection of 97,628 photographic images. The results indicate that the contribution of search log based training data is positive despite their inherent noise; in particular, the combination of manual and automatically generated training data outperforms the use of manual data alone. It is therefore possible to use clickthrough data to perform large-scale image annotation with little manual annotation effort or, depending on performance, using only the automatically generated training data. An extensive presentation of the experimental results and the accompanying data can be accessed at http://olympus.ee.auth.gr/~diou/civr2009/

Characterizing User Search Intent and Behavior for Click Analysis in Sponsored Search

Interpreting user actions to better understand their needs provides an important tool for improving information access services. In the context of organic Web search, considerable effort has been made to model user behavior and infer query intent, with the goal of improving the overall user experience. Much less work has been done in the area of sponsored search, i.e., with respect to the advertisement links (ads) displayed on search result pages by many commercial search engines. This thesis develops and evaluates new models and methods required to interpret user browsing and click behavior and understand query intent in this very different context. The concern of the initial part of the thesis is on extending the query categories for commercial search and on inferring query intent, with a focus on two major tasks: i) enriching queries with contextual information obtained from search result pages returned for these queries, and ii) developing relatively simple methods for the reliable labeling of training data via crowdsourcing. A central idea of this thesis work is to study the impact of contextual factors (including query intent, ad placement, and page structure) on user behavior. Later, this information is incorporated into probabilistic models to evaluate the quality of advertisement links within the context that they are displayed in their history of appearance. In order to account for these factors, a number of query and location biases are proposed and formulated into a group of browsing and click models. To explore user intent and behavior and to evaluate the performance of the proposed models and methods, logs of query and click information provided for research purposes are used. Overall, query intent is found to have substantial impact on predictions of user click behavior in sponsored search. Predictions are further improved by considering ads in the context of the other ads displayed on a result page. The parameters of the browsing and click models are learned using an expectation maximization technique applied to click signals recorded in the logs. The initial motivation of the user to browse the ad list and their browsing persistence are found to be related to query intent and browsing/click behavior. Accommodating these biases along with the location bias in user models appear as effective contextual signals, improving the performance of the existing models

Information retrieval approaches: A comparative study

The area of information retrieval (IR) has taken on increasing importance in recent years. This field is now of interest to large communities in several application domains (security, medicine, aeronautics, etc.). IR studies find relevant information from the semi-unstructured type of data. As the information resources generated after the search can be extensive in quantity and different in quality, it is essential to rank these results according to the degree of relevance. This paper focuses on text information retrieval (TIR) and emphasizes the importance of each IR approach. This study presents insightful aspects of TIR and provides a comparative study between some proposed approaches and models. Each model offers IR advantages and suffers from several limitations

Beyond actions : exploring the discovery of tactics from user logs

Search log analysis has become a common practice to gain insights into user search behaviour; it helps gain an understanding of user needs and preferences, as well as an insight into how well a system supports such needs. Currently, log analysis is typically focused on low-level user actions, i.e. logged events such as issued queries and clicked results, and often only a selection of such events are logged and analysed. However, types of logged events may differ widely from interface to interface, making comparison between systems difficult. Further, the interpretation of the meaning of and subsequent analysis of a selection of events may lead to conclusions out of context—e.g. the statistics of observed query reformulations may be influenced by the existence of a relevance feedback component. Alternatively, in lab studies user activities can be analysed at a higher level, such as search tactics and strategies, abstracted away from detailed interface implementation. Unfortunately, until now the required manual codings that map logged events to higher-level interpretations have prevented large-scale use of this type of analysis. In this paper, we propose a new method for analysing search logs by (semi-)automatically identifying user search tactics from logged events, allowing large-scale analysis that is comparable across search systems. In addition, as the resulting analysis is at a tactical level we reduce potential issues surrounding the need for interpretation of low-level user actions for log analysis. We validate the efficiency and effectiveness of the proposed tactic identification method using logs of two reference search systems of different natures: a product search system and a video search system. With the identified tactics, we perform a series of novel log analyses in terms of entropy rate of user search tactic sequences, demonstrating how this type of analysis allows comparisons of user search behaviours across systems of different nature and design. This analysis provides insights not achievable with traditional log analysis

Personalised video retrieval: application of implicit feedback and semantic user profiles

A challenging problem in the user profiling domain is to create profiles of users of retrieval systems. This problem even exacerbates in the multimedia domain. Due to the Semantic Gap, the difference between low-level data representation of videos and the higher concepts users associate with videos, it is not trivial to understand the content of multimedia documents and to find other documents that the users might be interested in. A promising approach to ease this problem is to set multimedia documents into their semantic contexts. The semantic context can lead to a better understanding of the personal interests. Knowing the context of a video is useful for recommending users videos that match their information need. By exploiting these contexts, videos can also be linked to other, contextually related videos. From a user profiling point of view, these links can be of high value to recommend semantically related videos, hence creating a semantic-based user profile. This thesis introduces a semantic user profiling approach for news video retrieval, which exploits a generic ontology to put news stories into its context. Major challenges which inhibit the creation of such semantic user profiles are the identification of user's long-term interests and the adaptation of retrieval results based on these personal interests. Most personalisation services rely on users explicitly specifying preferences, a common approach in the text retrieval domain. By giving explicit feedback, users are forced to update their need, which can be problematic when their information need is vague. Furthermore, users tend not to provide enough feedback on which to base an adaptive retrieval algorithm. Deviating from the method of explicitly asking the user to rate the relevance of retrieval results, the use of implicit feedback techniques helps by learning user interests unobtrusively. The main advantage is that users are relieved from providing feedback. A disadvantage is that information gathered using implicit techniques is less accurate than information based on explicit feedback. In this thesis, we focus on three main research questions. First of all, we study whether implicit relevance feedback, which is provided while interacting with a video retrieval system, can be employed to bridge the Semantic Gap. We therefore first identify implicit indicators of relevance by analysing representative video retrieval interfaces. Studying whether these indicators can be exploited as implicit feedback within short retrieval sessions, we recommend video documents based on implicit actions performed by a community of users. Secondly, implicit relevance feedback is studied as potential source to build user profiles and hence to identify users' long-term interests in specific topics. This includes studying the identification of different aspects of interests and storing these interests in dynamic user profiles. Finally, we study how this feedback can be exploited to adapt retrieval results or to recommend related videos that match the users' interests. We analyse our research questions by performing both simulation-based and user-centred evaluation studies. The results suggest that implicit relevance feedback can be employed in the video domain and that semantic-based user profiles have the potential to improve video exploration

Table-to-Text: Generating Descriptive Text for Scientific Tables from Randomized Controlled Trials

Unprecedented amounts of data have been generated in the biomedical domain, and the bottleneck for biomedical research has shifted from data generation to data management, interpretation, and communication. Therefore, it is highly desirable to develop systems to assist in text generation from biomedical data, which will greatly improve the dissemination of scientific findings. However, very few studies have investigated issues of data-to-text generation in the biomedical domain. Here I present a systematic study for generating descriptive text from tables in randomized clinical trials (RCT) articles, which includes: (1) an information model for representing RCT tables; (2) annotated corpora containing pairs of RCT table and descriptive text, and labeled structural and semantic information of RCT tables; (3) methods for recognizing structural and semantic information of RCT tables; (4) methods for generating text from RCT tables, evaluated by a user study on three aspects: relevance, grammatical quality, and matching. The proposed hybrid text generation method achieved a low bilingual evaluation understudy (BLEU) score of 5.69; but human review achieved scores of 9.3, 9.9 and 9.3 for relevance, grammatical quality and matching, respectively, which are comparable to review of original human-written text. To the best of our knowledge, this is the first study to generate text from scientific tables in the biomedical domain. The proposed information model, labeled corpora and developed methods for recognizing tables and generating descriptive text could also facilitate other biomedical and informatics research and applications

Optimisation Method for Training Deep Neural Networks in Classification of Non- functional Requirements

Non-functional requirements (NFRs) are regarded critical to a software system's success. The majority of NFR detection and classification solutions have relied on supervised machine learning models. It is hindered by the lack of labelled data for training and necessitate a significant amount of time spent on feature engineering. In this work we explore emerging deep learning techniques to reduce the burden of feature engineering. The goal of this study is to develop an autonomous system that can classify NFRs into multiple classes based on a labelled corpus. In the first section of the thesis, we standardise the NFRs ontology and annotations to produce a corpus based on five attributes: usability, reliability, efficiency, maintainability, and portability. In the second section, the design and implementation of four neural networks, including the artificial neural network, convolutional neural network, long short-term memory, and gated recurrent unit are examined to classify NFRs. These models, necessitate a large corpus. To overcome this limitation, we proposed a new paradigm for data augmentation. This method uses a sort and concatenates strategy to combine two phrases from the same class, resulting in a two-fold increase in data size while keeping the domain vocabulary intact. We compared our method to a baseline (no augmentation) and an existing approach Easy data augmentation (EDA) with pre-trained word embeddings. All training has been performed under two modifications to the data; augmentation on the entire data before train/validation split vs augmentation on train set only. Our findings show that as compared to EDA and baseline, NFRs classification model improved greatly, and CNN outperformed when trained using our suggested technique in the first setting. However, we saw a slight boost in the second experimental setup with just train set augmentation. As a result, we can determine that augmentation of the validation is required in order to achieve acceptable results with our proposed approach. We hope that our ideas will inspire new data augmentation techniques, whether they are generic or task specific. Furthermore, it would also be useful to implement this strategy in other languages

LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach

In recent years, machine learning has developed rapidly, enabling the development of applications with high levels of recognition accuracy relating to the use of speech and images. However, other types of data to which these models can be applied have not yet been explored as thoroughly. Labelling is an indispensable stage of data pre-processing that can be particularly challenging, especially when applied to single or multi-model real-time sensor data collection approaches. Currently, real-time sensor data labelling is an unwieldy process, with a limited range of tools available and poor performance characteristics, which can lead to the performance of the machine learning models being compromised. In this paper, we introduce new techniques for labelling at the point of collection coupled with a pilot study and a systematic performance comparison of two popular types of deep neural networks running on five custom built devices and a comparative mobile app (68.5-89% accuracy within-device GRU model, 92.8% highest LSTM model accuracy). These devices are designed to enable real-time labelling with various buttons, slide potentiometer and force sensors. This exploratory work illustrates several key features that inform the design of data collection tools that can help researchers select and apply appropriate labelling techniques to their work. We also identify common bottlenecks in each architecture and provide field tested guidelines to assist in building adaptive, high-performance edge solutions