Incorporating intra-query term dependencies in an Aspect Query Language Model

Query language modeling based on relevance feedback has been widely applied to improve the effectiveness of information retrieval. However, intra-query term dependencies (i.e., the dependencies between different query terms and term combinations) have not yet been sufficiently addressed in the existing approaches. This paper aims to investigate this issue within a comprehensive framework, namely the Aspect Query Language Model (AM). We propose to extend the AM with a Hidden Markov Model (HMM) structure, to incorporate the intra-query term dependencies and learn the structure of a novel Aspect Hidden Markov Model (AHMM) for query language modeling. In the proposed AHMM, the combinations of query terms are viewed as latent variables representing query aspects. They further form an Ergodic HMM, where the dependencies between latent variables (nodes) are modelled as the transitional probabilities. The segmented chunks from the feedback documents are considered as observables of the HMM. Then the AHMM structure is optimized by the HMM, which can estimate the prior of the latent variables and the probability distribution of the observed chunks. Our extensive experiments on three large scale TREC collections have shown that our method not only significantly outperforms a number of strong baselines in terms of both effectiveness and robustness, but also achieves better results than the AM and another state-of-the-art approach, namely the Latent Concept Expansion (LCE) model

Social Search: retrieving information in Online Social Platforms -- A Survey

Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviors (as they can be inferred from data available on social platforms) to optimize their information needs further

Towards Multi-modal Explainable Video Understanding

This thesis presents a novel approach to video understanding by emulating human perceptual processes and creating an explainable and coherent storytelling representation of video content. Central to this approach is the development of a Visual-Linguistic (VL) feature for an interpretable video representation and the creation of a Transformer-in-Transformer (TinT) decoder for modeling intra- and inter-event coherence in a video. Drawing inspiration from the way humans comprehend scenes by breaking them down into visual and non-visual components, the proposed VL feature models a scene through three distinct modalities. These include: (i) a global visual environment, providing a broad contextual understanding of the scene; (ii) local visual main agents, focusing on key elements or entities in the video; and (iii) linguistic scene elements, incorporating semantically relevant language-based information for a comprehensive understanding of the scene. By integrating these multimodal features, the VL representation offers a rich, diverse, and interpretable view of video content, effectively bridging the gap between visual perception and linguistic description. To ensure the temporal coherence and narrative structure of the video content, we introduce an autoregressive Transformer-in-Transformer (TinT) decoder. The TinT design consists of a nested architecture where the inner transformer models the intra-event coherency, capturing the semantic connections within individual events, while the outer transformer models the inter-event coherency, identifying the relationships and transitions between different events. This dual-layer transformer structure facilitates the generation of accurate and meaningful video descriptions that reflect the chronological and causal links in the video content. Another crucial aspect of this work is the introduction of a novel VL contrastive loss function. This function plays an essential role in ensuring that the learned embedding features are semantically consistent with the video captions. By aligning the embeddings with the ground truth captions, the VL contrastive loss function enhances the model\u27s performance and contributes to the quality of the generated descriptions. The efficacy of our proposed methods is validated through comprehensive experiments on popular video understanding benchmarks. The results demonstrate superior performance in terms of both the accuracy and diversity of the generated captions, highlighting the potential of our approach in advancing the field of video understanding. In conclusion, this thesis provides a promising pathway toward building explainable video understanding models. By emulating human perception processes, leveraging multimodal features, and incorporating a nested transformer design, we contribute a new perspective to the field, paving the way for more advanced and intuitive video understanding systems in the future

Towards Multi-modal Explainable Video Understanding

This thesis presents a novel approach to video understanding by emulating human perceptual processes and creating an explainable and coherent storytelling representation of video content. Central to this approach is the development of a Visual-Linguistic (VL) feature for an interpretable video representation and the creation of a Transformer-in-Transformer (TinT) decoder for modeling intra- and inter-event coherence in a video. Drawing inspiration from the way humans comprehend scenes by breaking them down into visual and non-visual components, the proposed VL feature models a scene through three distinct modalities. These include: (i) a global visual environment, providing a broad contextual understanding of the scene; (ii) local visual main agents, focusing on key elements or entities in the video; and (iii) linguistic scene elements, incorporating semantically relevant language-based information for a comprehensive understanding of the scene. By integrating these multimodal features, the VL representation offers a rich, diverse, and interpretable view of video content, effectively bridging the gap between visual perception and linguistic description. To ensure the temporal coherence and narrative structure of the video content, we introduce an autoregressive Transformer-in-Transformer (TinT) decoder. The TinT design consists of a nested architecture where the inner transformer models the intra-event coherency, capturing the semantic connections within individual events, while the outer transformer models the inter-event coherency, identifying the relationships and transitions between different events. This dual-layer transformer structure facilitates the generation of accurate and meaningful video descriptions that reflect the chronological and causal links in the video content. Another crucial aspect of this work is the introduction of a novel VL contrastive loss function. This function plays an essential role in ensuring that the learned embedding features are semantically consistent with the video captions. By aligning the embeddings with the ground truth captions, the VL contrastive loss function enhances the model\u27s performance and contributes to the quality of the generated descriptions. The efficacy of our proposed methods is validated through comprehensive experiments on popular video understanding benchmarks. The results demonstrate superior performance in terms of both the accuracy and diversity of the generated captions, highlighting the potential of our approach in advancing the field of video understanding. In conclusion, this thesis provides a promising pathway toward building explainable video understanding models. By emulating human perception processes, leveraging multimodal features, and incorporating a nested transformer design, we contribute a new perspective to the field, paving the way for more advanced and intuitive video understanding systems in the future

Quantum-Inspired Interactive Networks for Conversational Sentiment Analysis.

Conversational sentiment analysis is an emerging, yet challenging Artificial Intelligence (AI) subtask. It aims to discover the affective state of each participant in a conversation. There exists a wealth of interaction information that affects the sentiment of speakers. However, the existing sentiment analysis approaches are insufficient in dealing with this task due to ignoring the interactions and dependency relationships between utterances. In this paper, we aim to address this issue by modeling intrautterance and inter-utterance interaction dynamics. We propose an approach called quantum-inspired interactive networks (QIN), which leverages the mathematical formalism of quantum theory (QT) and the long short term memory (LSTM) network, to learn such interaction dynamics. Specifically, a density matrix based convolutional neural network (DM-CNN) is proposed to capture the interactions within each utterance (i.e., the correlations between words), and a strong-weak influence model inspired by quantum measurement theory is developed to learn the interactions between adjacent utterances (i.e., how one speaker influences another). Extensive experiments are conducted on the MELD and IEMOCAP datasets. The experimental results demonstrate the effectiveness of the QIN model