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Diversity Aware Relevance Learning for Argument Search 43rd

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Diversity Aware Relevance Learning for Argument Search

43rd European Conference on Information Retrieval, ECIR 2021

作者： Fromm, Michael Berrendorf, Max Obermeier, Sandra Seidl, Thomas Faerman, Evgeniy Database Systems and Data Mining LMU Munich Munich Germany

ISBN: (纸本)9783030722395

In this work, we focus on retrieving relevant arguments for a query claim covering diverse aspects. State-of-the-art methods rely on explicit mappings between claims and premises and thus cannot utilize extensive available collections of premises without laborious and costly manual annotation. Their diversity approach relies on removing duplicates via clustering, which does not directly ensure that the selected premises cover all aspects. This work introduces a new multi-step approach for the argument retrieval problem. Rather than relying on ground-truth assignments, our approach employs a machine learning model to capture semantic relationships between arguments. Beyond that, it aims to cover diverse facets of the query instead of explicitly identifying duplicates. Our empirical evaluation demonstrates that our approach leads to a significant improvement in the argument retrieval task, even though it requires fewer data than prior methods. Our code is available at https://***/fromm-m/ecir2021-am-search. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

Relational and Fine-Grained Argument mining: The LMU Munich project ReMLAV within the DFG Priority Program RATIO "Robust Argumentation Machines"

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Datenbank-Spektrum 2020年第2期20卷 99-105页

作者： Trautmann, Dietrich Fromm, Michael Tresp, Volker Seidl, Thomas Schütze, Hinrich Center for Information and Language Processing LMU Munich Munich Germany Database Systems and Data Mining LMU Munich Munich Germany

In our project ReMLAV, funded within the DFG Priority Program RATIO (http://***/), we focus on relational and fine-grained argument mining. In this article, we first introduce the problems we address and then summarize related work. The main part of the article describes our research on argument mining, both coarse-grained and fine-grained methods, and on same-side stance classification, a relational approach to the problem of stance classification. We conclude with an outlook. © 2020, The Author(s).

关键词： Machine learning

Towards a Holistic View on Argument Quality Prediction

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arXiv 2022年

作者： Fromm, Michael Berrendorf, Max Reiml, Johanna Mayerhofer, Isabelle Bhargava, Siddharth Faerman, Evgeniy Seidl, Thomas Database Systems and Data Mining LMU Munich Germany

Argumentation is one of society’s foundational pillars, and, sparked by advances in NLP and the vast availability of text data, automated mining of arguments receives increasing attention. A decisive property of arguments is their strength or quality. While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal. In this work, we close this gap by approaching argument quality estimation from multiple different angles: Grounded on rich results from thorough empirical evaluations, we assess the generalization capabilities of argument quality estimation across diverse domains, the interplay with related argument mining tasks, and the impact of emotions on perceived argument strength. We find that generalization depends on a sufficient representation of different domains in the training part. In zero-shot transfer and multi-task experiments, we reveal that argument quality is among the more challenging tasks but can improve others. Finally, we show that emotions play a minor role in argument quality than is often assumed. We publish our code at https://***/r/kdd-holistic-view-aq-C0D8. Copyright © 2022, The Authors. All rights reserved.

关键词： Behavioral research

Diversity aware relevance learning for argument search

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arXiv 2020年

作者： Fromm, Michael Berrendorf, Max Obermeier, Sandra Seidl, Thomas Faerman, Evgeniy Database Systems and Data Mining LMU Munich Germany

In this work, we focus on retrieving relevant arguments for a query claim covering diverse aspects. State-of-the-art methods rely on explicit mappings between claims and premises and thus cannot utilize extensive available collections of premises without laborious and costly manual annotation. Their diversity approach relies on removing duplicates via clustering, which does not directly ensure that the selected premises cover all aspects. This work introduces a new multistep approach for the argument retrieval problem. Rather than relying on ground-truth assignments, our approach employs a machine learning model to capture semantic relationships between arguments. Beyond that, it aims to cover diverse facets of the query instead of explicitly identifying duplicates. Our empirical evaluation demonstrates that our approach leads to a significant improvement in the argument retrieval task, even though it requires fewer data than prior methods. Our code is available at https://***/fromm-m/ecir2021-am-search. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

TACAM: Topic And Context Aware Argument mining

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arXiv 2019年

作者： Fromm, Michael Faerman, Evgeniy Seidl, Thomas Database Systems and Data Mining LMU Munich Germany

In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them. The main challenge in the argument recognition task, which is also known as argument mining, is that often sentences containing arguments are structurally similar to purely informative sentences without any stance about the topic. In fact, they only differ semantically. Most approaches use topic or search term information only for the first search step and therefore assume that arguments can be classified independently of a topic. We argue that topic information is crucial for argument mining, since the topic defines the semantic context of an argument. Precisely, we propose different models for the classification of arguments, which take information about a topic of an argument into account. Moreover, to enrich the context of a topic and to let models understand the context of the potential argument better, we integrate information from different external sources such as Knowledge Graphs or pre-trained NLP models. Our evaluation shows that considering topic information, especially in connection with external information, provides a significant performance boost for the argument mining task. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

k-Nearest Neighbor based Clustering with Shape Alternation Adaptivity

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k-Nearest Neighbor based Clustering with Shape Alternation A...

International Joint Conference on Neural Networks (IJCNN)

作者： Yifeng Lu Yao Zhang Florian Richter Thomas Seidl Database Systems and Data Mining Group LMU Munich Germany

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Existing clustering algorithms aim at identifying clusters from a single dataset. However, many applications generate a series of datasets. For example, scientists need to repeat an experiment many times to ensure reproducibility; sensors collect information day after day. In such scenarios, we need to identify clusters separately from a large number of datasets, which can contain an unknown number of clusters with various densities and ***-based clustering algorithms are commonly used in identifying arbitrary shaped clusters when the cluster number is unknown. Most density-based clustering algorithms are "DBSCAN-alike", where clusters are formed by connecting consecutive high dense regions. Therefore, points are grouped as one cluster as long as they are densely connected. When the distribution shape of points is changed across different datasets, parameter tuning on each dataset is necessary to obtain proper results, which is *** this work, we developed a new kNN density-based clustering algorithm, which does not adopt the DBSCAN paradigm. Instead, we identify clusters by maximizing the intra-cluster similarities, which are estimated using: 1) the probability that two points belong to the same cluster; 2) the probability that a point is a cluster center. The kNN concept and minimum spanning tree are used to compute both probabilities. Our approach is capable of extracting clusters in arbitrary shapes using the single parameter k, and can handle a series of datasets with less parameter tuning effort. Experiments on both synthetic and real-world datasets show that our approach outperforms other recent kNN clustering algorithms.

关键词： Clustering algorithms Shape Tuning Toy manufacturing industry Estimation Task analysis Euclidean distance

Argument mining driven analysis of peer-reviews

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arXiv 2020年

作者： Fromm, Michael Faerman, Evgeniy Berrendorf, Max Bhargava, Siddharth Qi, Ruoxia Zhang, Yao Dennert, Lukas Selle, Sophia Mao, Yang Seidl, Thomas Database Systems and Data Mining LMU Munich Germany LMU Munich Germany

Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work. At the same time, it is a time-consuming process and increasing interest in emerging fields often results in a high review workload, especially for senior researchers in this area. How to cope with this problem is an open question and it is vividly discussed across all major conferences. In this work, we propose an Argument mining based approach for the assistance of editors, meta-reviewers, and reviewers. We demonstrate that the decision process in the field of scientific publications is driven by arguments and automatic argument identification is helpful in various use-cases. One of our findings is that arguments used in the peer-review process differ from arguments in other domains making the transfer of pre-trained models difficult. Therefore, we provide the community with a new peer-review dataset from different computer science conferences with annotated arguments. In our extensive empirical evaluation, we show that Argument mining can be used to efficiently extract the most relevant parts from reviews, which are paramount for the publication decision. The process remains interpretable since the extracted arguments can be highlighted in a review without detaching them from their context. © 2020, CC BY.

关键词：

Keynote: data mining on Process data

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Keynote: Data Mining on Process Data

International Conference on Process mining (ICPM)

作者： Thomas Seidl Computer science and head of the Database Systems and Data Mining group at LMU Munich

ISBN: (数字)9781728198323

ISBN: (纸本)9781728198330

data mining and Process mining - is one just a variant of the other, or do worlds separate the two areas from each other? The notions sound so similar but the contents sometimes look differently, so respective researchers may get confused in their mutual perception, be it authors or reviewers. The talk recalls commonalities like model-based supervised and unsupervised learning approaches, and it also sheds light to peculiarities in process data and process mining tasks as seen from a data mining perspective. When considering trace data from event log files as time series, as sequences, or as activity sets, quite different data mining techniques apply and may be extended and improved. A particular example is rare pattern mining, which fills a gap between frequent patterns and outlier detection. The task aims at identifying patterns that occur with low frequency but above single outliers. Structural deficiences may cause malfunctions or other undesired behavior which get discarded as outliers in event logs, since they are observed infrequently only. Rare pattern mining may identify these situations, and recent approaches include clustering or ordering non-conformant *** talk concludes with some remarks on how to sell process mining papers to the data mining community, and vice versa, in order to improve mutual acceptance, and to increase synergies in the fields.

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Conifer seedling detection in UAV-Imagery with RGB-depth information

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arXiv 2021年

作者： Jooste, Jason Fromm, Michael Schubert, Matthias LMU Munich Germany Database Systems and Data Mining LMU Munich Germany

Monitoring of reforestation is currently being considerably streamlined through the use of drones and image recognition algorithms, which have already proven to be effective on colour imagery. In addition to colour imagery, elevation data is often also available. The primary aim of this work was to improve the performance of the faster-RCNN object detection algorithm by integrating this height information, which showed itself to notably improve performance. Interestingly, the structure of the network played a key role, with direct addition of the height information as a fourth image channel showing no improvement, while integration after the backbone network and before the region proposal network led to marked improvements. This effect persisted with very long training regimes. Increasing the resolution of this height information also showed little effect. © 2021, CC BY.

关键词： Reforestation