Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Topic": models, code, and papers

Pattern-Based Classification: A Unifying Perspective

Nov 26, 2011
Björn Bringmann, Siegfried Nijssen, Albrecht Zimmermann

The use of patterns in predictive models is a topic that has received a lot of attention in recent years. Pattern mining can help to obtain models for structured domains, such as graphs and sequences, and has been proposed as a means to obtain more accurate and more interpretable models. Despite the large amount of publications devoted to this topic, we believe however that an overview of what has been accomplished in this area is missing. This paper presents our perspective on this evolving area. We identify the principles of pattern mining that are important when mining patterns for models and provide an overview of pattern-based classification methods. We categorize these methods along the following dimensions: (1) whether they post-process a pre-computed set of patterns or iteratively execute pattern mining algorithms; (2) whether they select patterns model-independently or whether the pattern selection is guided by a model. We summarize the results that have been obtained for each of these methods.

  Access Paper or Ask Questions

Effects of Language Modeling on Speech-driven Question Answering

Jul 10, 2004
Tomoyosi Akiba, Atsushi Fujii, Katunobu Itou

We integrate automatic speech recognition (ASR) and question answering (QA) to realize a speech-driven QA system, and evaluate its performance. We adapt an N-gram language model to natural language questions, so that the input of our system can be recognized with a high accuracy. We target WH-questions which consist of the topic part and fixed phrase used to ask about something. We first produce a general N-gram model intended to recognize the topic and emphasize the counts of the N-grams that correspond to the fixed phrases. Given a transcription by the ASR engine, the QA engine extracts the answer candidates from target documents. We propose a passage retrieval method robust against recognition errors in the transcription. We use the QA test collection produced in NTCIR, which is a TREC-style evaluation workshop, and show the effectiveness of our method by means of experiments.

* Proceedings of the 8th International Conference on Spoken Language Processing (ICSLP 2004), pp.1053-1056, Oct. 2004 
* 4 pages, Proceedings of the 8th International Conference on Spoken Language Processing (to appear) 

  Access Paper or Ask Questions

Splitting numerical integration for matrix completion

Feb 14, 2022
Qianqian Song

Low rank matrix approximation is a popular topic in machine learning. In this paper, we propose a new algorithm for this topic by minimizing the least-squares estimation over the Riemannian manifold of fixed-rank matrices. The algorithm is an adaptation of classical gradient descent within the framework of optimization on manifolds. In particular, we reformulate an unconstrained optimization problem on a low-rank manifold into a differential dynamic system. We develop a splitting numerical integration method by applying a splitting integration scheme to the dynamic system. We conduct the convergence analysis of our splitting numerical integration algorithm. It can be guaranteed that the error between the recovered matrix and true result is monotonically decreasing in the Frobenius norm. Moreover, our splitting numerical integration can be adapted into matrix completion scenarios. Experimental results show that our approach has good scalability for large-scale problems with satisfactory accuracy

  Access Paper or Ask Questions

DocSCAN: Unsupervised Text Classification via Learning from Neighbors

May 11, 2021
Dominik Stammbach, Elliott Ash

We introduce DocSCAN, a completely unsupervised text classification approach using Semantic Clustering by Adopting Nearest-Neighbors (SCAN). For each document, we obtain semantically informative vectors from a large pre-trained language model. Similar documents have proximate vectors, so neighbors in the representation space tend to share topic labels. Our learnable clustering approach uses pairs of neighboring datapoints as a weak learning signal. The proposed approach learns to assign classes to the whole dataset without provided ground-truth labels. On five topic classification benchmarks, we improve on various unsupervised baselines by a large margin. In datasets with relatively few and balanced outcome classes, DocSCAN approaches the performance of supervised classification. The method fails for other types of classification, such as sentiment analysis, pointing to important conceptual and practical differences between classifying images and texts.

  Access Paper or Ask Questions

Interactive Storytelling over Document Collections

Feb 21, 2016
Dipayan Maiti, Mohammad Raihanul Islam, Scotland Leman, Naren Ramakrishnan

Storytelling algorithms aim to 'connect the dots' between disparate documents by linking starting and ending documents through a series of intermediate documents. Existing storytelling algorithms are based on notions of coherence and connectivity, and thus the primary way by which users can steer the story construction is via design of suitable similarity functions. We present an alternative approach to storytelling wherein the user can interactively and iteratively provide 'must use' constraints to preferentially support the construction of some stories over others. The three innovations in our approach are distance measures based on (inferred) topic distributions, the use of constraints to define sets of linear inequalities over paths, and the introduction of slack and surplus variables to condition the topic distribution to preferentially emphasize desired terms over others. We describe experimental results to illustrate the effectiveness of our interactive storytelling approach over multiple text datasets.

* This paper has been submitted to a conference for review 

  Access Paper or Ask Questions

Reproducible Evaluation of Pan-Tilt-Zoom Tracking

May 18, 2015
Gengjie Chen, Pierre-Luc St-Charles, Wassim Bouachir, Thomas Joeisseint, Guillaume-Alexandre Bilodeau, Robert Bergevin

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. However, it is very difficult to assess the progress that has been made on this topic because there is no standard evaluation methodology. The difficulty in evaluating PTZ tracking algorithms arises from their dynamic nature. In contrast to other forms of tracking, PTZ tracking involves both locating the target in the image and controlling the motors of the camera to aim it so that the target stays in its field of view. This type of tracking can only be performed online. In this paper, we propose a new evaluation framework based on a virtual PTZ camera. With this framework, tracking scenarios do not change for each experiment and we are able to replicate online PTZ camera control and behavior including camera positioning delays, tracker processing delays, and numerical zoom. We tested our evaluation framework with the Camshift tracker to show its viability and to establish baseline results.

* This is an extended version of the 2015 ICIP paper "Reproducible Evaluation of Pan-Tilt-Zoom Tracking" 

  Access Paper or Ask Questions

Research on Domain Information Mining and Theme Evolution of Scientific Papers

Apr 18, 2022
Changwei Zheng, Zhe Xue, Meiyu Liang, Feifei Kou, Zeli Guan

In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Cross-disciplinary research results have gradually become an emerging frontier research direction. There is a certain dependence between a large number of research results. It is difficult to effectively analyze today's scientific research results when looking at a single research field in isolation. How to effectively use the huge number of scientific papers to help researchers becomes a challenge. This paper introduces the research status at home and abroad in terms of domain information mining and topic evolution law of scientific and technological papers from three aspects: the semantic feature representation learning of scientific and technological papers, the field information mining of scientific and technological papers, and the mining and prediction of research topic evolution rules of scientific and technological papers.

* arXiv admin note: text overlap with arXiv:2203.16256 

  Access Paper or Ask Questions

BEyond observation: an approach for ObjectNav

Jun 21, 2021
Daniel V. Ruiz, Eduardo Todt

With the rise of automation, unmanned vehicles became a hot topic both as commercial products and as a scientific research topic. It composes a multi-disciplinary field of robotics that encompasses embedded systems, control theory, path planning, Simultaneous Localization and Mapping (SLAM), scene reconstruction, and pattern recognition. In this work, we present our exploratory research of how sensor data fusion and state-of-the-art machine learning algorithms can perform the Embodied Artificial Intelligence (E-AI) task called Visual Semantic Navigation. This task, a.k.a Object-Goal Navigation (ObjectNav) consists of autonomous navigation using egocentric visual observations to reach an object belonging to the target semantic class without prior knowledge of the environment. Our method reached fourth place on the Habitat Challenge 2021 ObjectNav on the Minival phase and the Test-Standard Phase.

* Presented at the 2th Embodied AI Workshop at CVPR 2021 

  Access Paper or Ask Questions

Prediction, Selection, and Generation: Exploration of Knowledge-Driven Conversation System

May 05, 2021
Cheng Luo, Dayiheng Liu, Chanjuan Li, Li Lu, Jiancheng Lv

In open-domain conversational systems, it is important but challenging to leverage background knowledge. We can use the incorporation of knowledge to make the generation of dialogue controllable, and can generate more diverse sentences that contain real knowledge. In this paper, we combine the knowledge bases and pre-training model to propose a knowledge-driven conversation system. The system includes modules such as dialogue topic prediction, knowledge matching and dialogue generation. Based on this system, we study the performance factors that maybe affect the generation of knowledge-driven dialogue: topic coarse recall algorithm, number of knowledge choices, generation model choices, etc., and finally made the system reach state-of-the-art. These experimental results will provide some guiding significance for the future research of this task. As far as we know, this is the first work to study and analyze the effects of the related factors.

  Access Paper or Ask Questions