Information Retrieval

Nowadays, a considerable volume of news articles is produced daily by news agencies worldwide. Since there is an extensive volume of news on the web, finding exact answers to the users’ questions is not a straightforward task. Developing Question ...

Visual search has become more popular in recent years, allowing users to search by an image they are taking using their mobile device or uploading from their photo library. One domain in which visual search is especially valuable is electronic ...

In light of recent advances in adversarial learning, there has been strong and continuing interest in exploring how to perform adversarial learning-to-rank. The previous adversarial ranking methods [e.g., IRGAN by Wang et al. (IRGAN: a minimax ...

Generating natural language answers for question-answering (QA) tasks has recently surged in popularity with the rise of task-based personalized assistants. Most QA research is on extractive QA, methods that find answer spans in text passages. ...

The team discovery task is concerned with finding a group of experts from a collaboration network who would collectively cover a desirable set of skills. Most prior work for team discovery either adopt graph-based or neural mapping approaches. ...$\mathrm{}$$\mathrm{}$

Multi-modal retrieval has seen tremendous progress with the development of vision-language models. However, further improving these models require additional labelled data which is a huge manual effort. In this paper, we propose a framework MuMUR, ...

The plethora of available and stored temporal data necessitated the development of effective algorithms for information retrieval. The previous research on temporal information retrieval predominantly focused on the correctness of the retrieval ...$\mathrm{}{\mathrm{}}^{\mathrm{}}$

This paper presents an exploration of the usage of Heterogeneous Graph Attention Networks, or HGATs, for the task of Passage Retrieval. More precisely, we study how these models perform to alleviate the problem of passage contextualization, that ...

Recent machine learning advances demonstrate the effectiveness of zero-shot models trained on large amounts of data collected from the internet. Among these, CLIP (Contrastive Language-Image Pre-training) has been introduced as a multimodal model ...

Evaluation metrics such as precision, recall and normalized discounted cumulative gain have been widely applied in ad hoc retrieval experiments. They have facilitated the assessment of system performance in various topics over the past decade. ...

Search results from the search engine may be not enough to satisfy users’ search intent when the issued query is broad or ambiguous. In such cases, presenting to the user query facets, which include common query reformulations, may help ...

Previous work on privacy-aware ranking has addressed the minimization of information leakage when scoring top k documents, and has not studied on how to retrieve these top documents and their features for ranking. This paper proposes a privacy-...

Stemming algorithms are crucial tools for enhancing the information retrieval process in natural language processing. This paper presents a novel Arabic light stemming algorithm called Tashaphyne0.4, the idea behind this algorithm is to extract ...

Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for document retrieval tasks. Such models are fine-tuned based on the query-document/query-passage level relevance labels to capture the ranking signals. However, ...