OpenRaiser/Intern-Atlas huggingface dataset:
Detecting regulation violations for an indian regulatory body through multi label classification
Authors: Ujwal Narayan, Pulkit Parikh, Kamalakar Karlapalem, Natraj Raman
Venue: Conference WWW 2022
Year: 2022

The Securities and Exchange Board of India (SEBI) is the regulatory body for securities and commodities in India. SEBI creates, and enforces regulations that must be followed by all listed companies. To the best of our knowledge, this is the first work on identifying the regulation(s) that a SEBI-related case violates, which could be of substantial value to companies, lawyers, and other stakeholders in the regulatory process. We create a dataset for this task by automatically extracting violations from publicly available case-files. Using this data, we explore various multi-label text classification methods to determine the potentially multiple regulations violated by (the facts of) a case. Our experiments demonstrate the importance of employing contextual text representations to understand complex financial and legal concepts. We also highlight the challenges that must be addressed to develop a fully functional system in the real-world.
Personalized dynamic recommender system for investors
Authors: Takehiro Takayanagi, Chung-Chi Chen, Kiyoshi Izumi
Venue: Conference SIGIR 2023
Year: 2023

With the development of online platforms, people can share and obtain opinions quickly. It also makes individuals' preferences change dynamically and rapidly because they may change their minds when getting convincing opinions from other users. Unlike representative areas of recommendation research such as e-commerce platforms where items' features are fixed, in investment scenarios financial instruments' features such as stock price, also change dynamically over time. To capture these dynamic features and provide a better-personalized recommendation for amateur investors, this study proposes a Personalized Dynamic Recommender System for Investors, PDRSI. The proposed PDRSI considers two investor's personal features: dynamic preferences and historical interests, and two temporal environmental properties: recent discussions on the social media platform and the latest market information. The experimental results support the usefulness of the proposed PDRSI, and the ablation studies show the effect of each module. For reproduction, we follow Twitter's developer policy to share our dataset for future work.
Dynamicesg: A dataset for dynamically unearthing esg ratings from news articles
Authors: Yu-Min Tseng, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: Conference CIKM 2023
Year: 2023

This paper introduces the DynamicESG dataset, a unique resource for dynamically extracting ESG ratings from news articles. The ESG rating, a novel metric employed annually to gauge a company's sustainability, relies heavily on corporate disclosure and other external information, especially news narratives. Our dataset, comprising a wide spectrum of news over a twelve-year span, annotates articles in accordance with MSCI ESG ratings methodology and SASB standards, with relevance to ESG issues. DynamicESG provides a comprehensive means of investigating the relationship between public discourse, ESG-related events, and subsequent ESG rating adjustments. We detail our data collection, curation, annotation procedure, and inter-rater agreement, ensuring high data quality and usability. Importantly, our dataset includes a temporal dimension, enabling the analysis of longitudinal trends in ESG ratings and their correlation with news coverage. Moreover, the dataset incorporates an opportunity/risk tendency, thus permitting analysis from diverse perspectives to discern if the news is beneficial or detrimental to the company. We believe this dataset will serve as a valuable resource for researchers in fields such as corporate social responsibility, sustainable investing, machine learning, and natural language processing. Initial analysis using the dataset underscores its potential to facilitate new insights into the dynamics of ESG ratings and the influence of news media on these ratings.
An albert-based similarity measure for mathematical answer retrieval
Authors: Anja Reusch, Maik Thiele, Wolfgang Lehner
Venue: Conference SIGIR 2021
Year: 2021

Mathematical Language Processing (MLP) deals with the automated processing and analysis of mathematical documents and relies heavily on good representations of mathematical symbols and texts. The aim of this work is to explore the modeling capabilities of state-of-the-art unsupervised deep learning methods to create such representations. Therefore, we pre-trained different instances of an ALBERT model on Mathematics StackExchange data and fine-tuned it on the task of Mathematical Answer Retrieval. Our evaluation shows that ALBERT outperforms all previous systems and is on par with current state-of-the-art systems for math retrieval indicating strong capabilities of modeling mathematical posts. This implies that our approach can also be beneficial to various other tasks in MLP such as automatic proof checking or summarization of scientific texts.
Approximated doubly robust search relevance estimation
Authors: Lixin Zou, Changying Hao, Hengyi Cai, Shuaiqiang Wang, Suqi Cheng, Zhicong Cheng, Wenwen Ye, Simiu Gu, Dawei Yin
Venue: Conference CIKM 2022
Year: 2022

Extracting query-document relevance from the sparse, biased clickthrough log is among the most fundamental tasks in the web search system. Prior art mainly learns a relevance judgment model with semantic features of the query and document and ignores directly counterfactual relevance evaluation from the clicking log. Though the learned semantic matching models can provide relevance signals for tail queries as long as the semantic feature is available. However, such a paradigm lacks the capability to introspectively adjust the biased relevance estimation whenever it conflicts with massive implicit user feedback. The counterfactual evaluation methods, on the contrary, ensure unbiased relevance estimation with sufficient click information. However, they suffer from the sparse or even missing clicks caused by the long-tailed query distribution. In this paper, we propose to unify the counterfactual evaluating and learning approaches for unbiased relevance estimation on search queries with various popularities. Specifically, we theoretically develop a doubly robust estimator with low bias and variance, which intentionally combines the benefits of existing relevance evaluating and learning approaches. We further instantiate the proposed unbiased relevance estimation framework in Baidu search, with comprehensive practical solutions designed regarding the data pipeline for click behavior tracking and online relevance estimation with an approximated deep neural network. Finally, we present extensive empirical evaluations to verify the effectiveness of our proposed framework, finding that it is robust in practice and manages to improve online ranking performance substantially.
E2mocase: A dataset for emotional, event and moral observations in news articles on high-impact legal cases
Authors: Candida Maria Greco, Lorenzo Zangari, Davide Picca, Andrea Tagarelli
Venue: Conference CIKM 2025
Year: 2025

The way the media report on legal cases can significantly shape public opinion, often embedding subtle biases that influence societal views on justice, fairness, and morality. Analyzing these narratives requires a holistic approach that captures their emotional tone, moral framing, and the specific events they convey. In this work, we introduce E2MoCase, a novel dataset that enables integrated analysis of emotions, morality, and events within legal narratives and media coverage. We leverage NLP models to extract events and predict morality and emotions, providing a multidimensional perspective on how legal cases are portrayed in news articles. Our experimental evaluation showed that E2MoCase is beneficial for addressing emotion- and morality-based tasks, which is also confirmed by a human evaluation of the annotations.
Larch: Large language model-based automatic readme creation with heuristics
Authors: Yuta Koreeda, Terufumi Morishita, Osamu Imaichi, Yasuhiro Sogawa
Venue: Conference CIKM 2023
Year: 2023

Writing a readme is a crucial aspect of software development as it plays a vital role in managing and reusing program code. Though it is a pain point for many developers, automatically creating one remains a challenge even with the recent advancements in large language models (LLMs), because it requires generating an abstract description from thousands of lines of code. In this demo paper, we show that LLMs are capable of generating a coherent and factually correct readmes if we can identify a code fragment that is representative of the repository. Building upon this finding, we developed LARCH (LLM-based Automatic Readme Creation with Heuristics) which leverages representative code identification with heuristics and weak supervision. Through human and automated evaluations, we illustrate that LARCH can generate coherent and factually correct readmes in the majority of cases, outperforming a baseline that does not rely on representative code identification. We have made LARCH open-source and provided a cross-platform Visual Studio Code interface and command-line interface, accessible at https://github.com/hitachi-nlp/larch. A demo video showcasing LARCH's capabilities is available at https://youtu.be/ZUKkh5ED-O4.
Leveraging event schema to ask clarifying questions for conversational legal case retrieval
Authors: Bulou Liu, Yiran Hu, Qingyao Ai, Yiqun Liu, Yueyue Wu, Chenliang Li, Weixing Shen
Venue: Conference CIKM 2023
Year: 2023

Legal case retrieval is a special IR task aiming to retrieve supporting cases for a given query case. Existing works have shown that conversational search paradigm can improve users' search experience in legal case retrieval. One of the keys to a practical conversational search system is how to ask high-quality clarifying questions to initiate conversations with users and understand their search intents. Recently, Large Language Models, such as ChatGPT and GPT-4, have shown superior ability in both open-domain QA and conversations with human. Thus it is natural to believe that they could be applied to legal conversational search as well. However, our preliminary study has shown that generating clarifying questions in legal conversational search with SOTA LLMs (e.g., GPT-4) often suffers from several problems such as duplication and low-utility contents. To address these problems, we propose LeClari, which leverages legal event schema as external knowledge to instruct LLMs to generate effective clarifying questions for legal conversational search. LeClari is constructed with a prompt module and a novel legal event selection module. The former defines a prompt with legal events for clarifying question generation and the latter selects potential event types by modeling the relationships of legal event types, conversational context, and candidate cases. We also propose ranking-oriented rewards and employ the reward augmented maximum likelihood (RAML) method to optimize LeClari directly based on the final retrieval performance of the conversational legal search system. Empirical results over two widely adopted legal case retrieval datasets demonstrate the effectiveness of our approach as compared with the state-of-the-art baselines.
Strum: Extractive aspect-based contrastive summarization
Authors: Beliz Gunel, Sandeep Tata, Marc Najork
Venue: Conference WWW 2023
Year: 2023

Comparative decisions, such as picking between two cars or deciding between two hiking trails, require the users to visit multiple webpages and contrast the choices along relevant aspects. Given the impressive capabilities of pre-trained large language models [4, 11], we ask whether they can help automate such analysis. We refer to this task as extractive aspect-based contrastive summarization which involves constructing a structured summary that compares the choices along relevant aspects. In this paper, we propose a novel method called STRUM for this task that can generalize across domains without requiring any human-written summaries or fixed aspect list as supervision. Given a set of relevant input webpages, STRUM solves this problem using two pre-trained T5-based [11] large language models: first one fine-tuned for aspect and value extraction [14], and second one fine-tuned for natural language inference [13]. We showcase the abilities of our method across different domains, identify shortcomings, and discuss questions that we believe will be critical in this new line of research.
Multi-aspect expertise matching for review assignment
Authors: Maryam Karimzadehgan, ChengXiang Zhai, Geneva G. Belford
Venue: Conference CIKM 2008
Year: 2008

Review assignment is a common task that many people such as conference organizers, journal editors, and grant administrators would have to do routinely. As a computational problem, it involves matching a set of candidate reviewers with a paper or proposal to be reviewed. A common deficiency of all existing work on solving this problem is that they do not consider the multiple aspects of topics or expertise and all match the entire document to be reviewed with the overall expertise of a reviewer. As a result, if a document contains multiple subtopics, which often happens, existing methods would not attempt to assign reviewers to cover all the subtopics; instead, it is quite possible that all the assigned reviewers would cover the major subtopic quite well, but not covering any other subtopic. In this paper, we study how to model multiple aspects of expertise and assign reviewers so that they together can cover all subtopics in the document well. We propose three general strategies for solving this problem and propose new evaluation measures for this task. We also create a multi-aspect review assignment test set using ACM SIGIR publications. Experiment results on this data set show that the proposed methods are effective for assigning reviewers to cover all topical aspects of a document.
Superresolution texture maps for multiview reconstruction
Authors: Bastian Goldlücke, Daniel Cremers
Venue: Conference ICCV 2009
Year: 2009

We study the scenario of a multiview setting, where several calibrated views of a textured object with known surface geometry are available. The objective is to estimate a diffuse texture map as precisely as possible. A superresolution image formation model based on the camera properties leads to a total variation energy for the desired texture map, which can be recovered as the minimizer of the functional by solving the Euler-Lagrange equation on the surface. The PDE is transformed to planar texture space via an automatically created conformal atlas, where it can be solved using total variation deblurring. The proposed approach allows to recover a high-resolution, high-quality texture map even from lower-resolution photographs, which is of interest for a variety of image-based modeling applications.
3d reconstruction of background and objects moving on ground plane viewed from a moving camera
Authors: Chang Yuan, Gérard G. Medioni
Venue: Conference CVPR 2006
Year: 2006

We present a novel method to obtain a 3D Euclidean reconstruction of both the background and moving objects in a video sequence. We assume that, multiple objects are moving rigidly on a ground plane observed by a moving camera. The video sequence is first segmented into static background and motion blobs by a homography-based motion segmentation method. Then classical "Structure from Motion" (SfM) techniques are applied to obtain a Euclidean reconstruction of the static background. The motion blob corresponding to each moving object is treated as if there were a static object observed by a hypothetical moving camera, called a "virtual camera". This virtual camera shares the same intrinsic parameters with the real camera but moves differently due to object motion. The same SfM techniques are applied to estimate the 3D shape of each moving object and the pose of the virtual camera. We show that the unknown scale of moving objects can be approximately determined by the ground plane, which is a key contribution of this paper. Another key contribution is that we prove that the 3D motion of moving objects can be solved from the virtual camera motion with a linear constraint imposed on the object translation. In our approach, a planartranslation constraint is formulated: "the 3D instantaneous translation of moving objects must be parallel to the ground plane". Results on real-world video sequences demonstrate the effectiveness and robustness of our approach.
Connected contours: A new contour completion model that respects the closure effect
Authors: Yansheng Ming, Hongdong Li, Xuming He 0001
Venue: Conference CVPR 2012
Year: 2012

Contour Completion plays an important role in visual perception, where the goal is to group fragmented low-level edge elements into perceptually coherent and salient contours. This process is often considered as guided by some middle-level Gestalt principles. Most existing methods for contour completion have focused on utilizing rather local Gestalt laws such as good-continuity and proximity. In contrast, much fewer methods have addressed the global contour closure effect, despite that many psychological evidences have shown the usefulness of closure in perceptual grouping. This paper proposes a novel higher-order CRF model to address the contour closure effect, through local connectedness approximation. This leads to a simplified problem structure, where the higher-order inference can be formulated as an integer linear program (ILP) and solved by an efficient cutting-plane variant. Tested on the BSDS benchmark, our method achieves a comparable precision-recall performance, a superior contour grouping ability (measured by Rand index), and more visually pleasing results, compared with existing methods.
Ligature instabilities in the perceptual organization of shape
Authors: Jonas August, Steven W. Zucker, Kaleem Siddiqi
Venue: Conference CVPR 1999
Year: 1999

Although the classical Blum skeleton has long been considered unstable, many have attempted to alleviate this defect through pruning. Unfortunately, these methods have an arbitrary basis, and, more importantly, they do not prevent internal structural alterations due to slight changes in an object's boundary. The result is a relative lack of development of skeleton representations for indexing object databases, despite a long history. Here we revisit a subset of the skeleton-called ligature by Blum-to demonstrate how the topological sensitivity of the skeleton can be eliminated. We relate ligature to a natural growth principle to provide an account of the perceptual parts of shape.
A layered approach to stereo reconstruction
Authors: Simon Baker, Richard Szeliski, P. Anandan 0001
Venue: Conference CVPR 1998
Year: 1998

We propose a framework for extracting structure from stereo which represents the scene as a collection of approximately planar layers. Each layer consists of an explicit 3D plane equation, a colored image with per-pixel opacity (a sprite), and a per-pixel depth offset relative to the plane. Initial estimates of the layers are recovered using techniques taken from parametric motion estimation. These initial estimates are then refined using a re-synthesis algorithm which takes into account both occlusions and mixed pixels. Reasoning about such effects allows the recovery of depth and color information with high accuracy even in partially occluded regions. Another important benefit of our framework is that the output consists of a collection of approximately planar regions, a representation which is far more appropriate than a dense depth map for many applications such as rendering and video parsing.
Robust nonrigid icp using outlier-sparsity regularization
Authors: Hidekata Hontani, Takamiti Matsuno, Yoshihide Sawada
Venue: Conference CVPR 2012
Year: 2012

We show how to incorporate a statistical shape model into the nonrigid ICP framework, and propose a robust nonrigid ICP algorithm. In the nonrigid ICP framework, a template surface is represented by a set of points, and the shape of the template is parametrized by a transformation matrix per one template point. In the proposed method, the statistics of the matrices are estimated based on a set of training surfaces, and the statistical shape model is incorporated into the nonrigid ICP framework by modifying the representation of the stiffness of the template. The statistical shape model and a noise model make it possible to discriminate outliers from inliers in given targets. Our proposed method detects the outliers, which are not represented by the models appropriately, based on their sparseness. The detected outliers are automatically excluded from the target to be registered, and the template is deformed to fit the inliers only. As the result, the accuracy of the registration is improved. The performance of the proposed method is evaluated qualitatively and quantitatively using synthetic data and clinical CT images.
Meshless deformable models for lv motion analysis
Authors: Xiaoxu Wang, Dimitris N. Metaxas, Ting Chen 0001, Leon Axel
Venue: Conference CVPR 2008
Year: 2008

We propose a novel meshless deformable model for in vivo cardiac left ventricle (LV) 3D motion estimation. As a relatively new technology, taggedMRI (tMRI) provides a direct and noninvasive way to reveal local deformation of the myocardium, which creates a large amount of heart motion data which requiring quantitative analysis. In our study, we sample the heart motion sparsely at intersections of three sets of orthogonal tagging planes and then use a new meshless deformable model to recover the dense 3D motion of the myocardium temporally during the cardiac cycle. We compute external forces at tag intersections based on tracked local motion and redistribute the force to meshless particles throughout the myocardium. Internal constraint forces at particles are derived from local strain energy using a Moving Least Squares (MLS) method. The dense 3D motion field is then computed and updated using the Lagrange equation. The new model avoids the singularity problem of mesh-based models and is capable of tracking large deformation with high efficiency and accuracy. In particular, the model performs well even when the control points (tag intersections) are relatively sparse. We tested the performance of the meshless model on a numerical phantom, as well as in vivo heart data of healthy subjects and patients. The experimental results show that the meshless deformable model can fully recover the myocardium motion in 3D.
Translider: Transfer ensemble learning from exploitation to exploration
Authors: Kuo Zhong, Ying Wei 0001, Chun Yuan 0003, Haoli Bai, Junzhou Huang
Venue: Conference KDD 2020
Year: 2020

In transfer learning, what and where to transfer has been widely studied. Nevertheless, the learned transfer strategies are at high risk of over-fitting, especially when only a few annotated instances are available in the target domain. In this paper, we introduce the concept of transfer ensemble learning, a new direction to tackle the over-fitting of transfer strategies. Intuitively, models with different transfer strategies offer various perspectives on what and where to transfer. Therefore a core problem is to search these diversely transferred models for ensemble so as to achieve better generalization. Towards this end, we propose the Transferability Slider (TranSlider) for transfer ensemble learning. By decreasing the transferability, we obtain a spectrum of base models ranging from pure exploitation of the source model to unconstrained exploration for the target domain. Furthermore, the manner of decreasing transferability with parameter sharing guarantees fast optimization at no additional training cost. Finally, we conduct extensive experiments with various analyses, which demonstrate that TranSlider achieves the state-of-the-art on comprehensive benchmark datasets.