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Sweeping through the Topic Space: Bad luck? Roll again! 12

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Sweeping through the Topic Space: Bad luck? Roll again!

Conference of The European Chapter of The Association for Computational Linguistics

作者： Martin Riedl Chris Biemann Ubiquitous Knowledge Processing Lab Computer Science Department Technische Universitaet Darmstadt Hochschulstrasse 10 D-64289 Darmstadt Germany

ISBN: (纸本)9781622764907

Topic Models (TM) such as Latent Dirich-let Allocation (LDA) are increasingly used in Natural Language processing applications. At this, the model parameters and the influence of randomized sampling and inference are rarely examined — usually, the recommendations from the original papers are adopted. In this paper, we examine the parameter space of LDA topic models with respect to the application of Text Segmentation (TS), specifically targeting error rates and their variance across different runs. We find that the recommended settings result in error rates far from optimal for our application. We show substantial variance in the results for different runs of model estimation and inference, and give recommendations for increasing the robustness and stability of topic models. Running the inference step several times and selecting the last topic ID assigned per token, shows considerable improvements. Similar improvements are achieved with the mode method: We store all assigned topic IDs during each inference iteration step and select the most frequent topic ID assigned to each word. These recommendations do not only apply to TS, but are generic enough to transfer to other applications.

关键词： Error analysis modal method SWEEPS parameter space methods Luck token model parameters laser Doppler anemometry Anterior interventricular branch of left coronary artery Left-sided displacement of abomasum Variance SCROLL IDAHO OPERATIONS OFFICE TBXAS1 gene CACNA1C gene

TopicTiling: A Text Segmentation Algorithm based on LDA 12

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TopicTiling: A Text Segmentation Algorithm based on LDA

Student Research Workshop

作者： Martin Riedl Chris Biemann Ubiquitous Knowledge Processing Lab Computer Science Department Technische Universitaet Darmstadt Hochschulstrasse 10 D-64289 Darmstadt Germany

ISBN: (纸本)9781622765942

This work presents a Text Segmentation algorithm called TopicTiling. This algorithm is based on the well-known TextTiling algorithm, and segments documents using the Latent Dirichlet Allocation (LDA) topic model. We show that using the mode topic ID assigned during the inference method of LDA, used to annotate unseen documents, improves performance by stabilizing the obtained topics. We show significant improvements over state of the art segmentation algorithms on two standard datasets. As an additional benefit, TopicTiling performs the segmentation in linear time and thus is computationally less expensive than other LDA-based segmentation methods.

关键词： Left-sided displacement of abomasum laser Doppler anemometry Anterior interventricular branch of left coronary artery text segmentation algorithms state of the art

Ranking Creative Language Characteristics in Small Data Scenarios 13

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Ranking Creative Language Characteristics in Small Data Scen...

13th International Conference on Computational Creativity, ICCC 2022

作者： Siekiera, Julia Köppel, Marius Simpson, Edwin Stowe, Kevin Gurevych, Iryna Kramer, Stefan Dept. of Computer Science Johannes Gutenberg-Universität Mainz Germany Institute for Nuclear Physics Johannes Gutenberg-Universität Mainz Germany Dept. of Computer Science University of Bristol United Kingdom Ubiquitous Knowledge Processing Lab. Technische Universität Darmstadt Germany

ISBN: (纸本)9789895416042

The ability to rank creative natural language provides an important general tool for downstream language understanding and generation. However, current deep ranking models require substantial amounts of labeled data that are difficult and expensive to obtain for new domains, languages and creative characteristics. A recent neural approach, DirectRanker, reduces the amount of training data needed but has not previously been used to rank creative text. We therefore adapt DirectRanker to provide a new deep model for ranking creative language with small numbers of training instances, and compare it with a Bayesian approach, Gaussian process preference learning (GPPL), which was previously shown to work well with sparse data. Our experiments with short creative language texts show the effectiveness of DirectRanker even with small training datasets. Combining DirectRanker with GPPL outperforms the previous state of the art on humor and metaphor novelty tasks, increasing Spearman’s ρ by 25% and 29% on average. Furthermore, we provide a possible application to validate jokes in the process of creativity generation. © 2022 Proceedings of the 13th International Conference on Computational Creativity, ICCC 2022. All rights reserved.

关键词： Bayesian networks

Revisiting joint modeling of cross-document entity and event coreference resolution

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arXiv 2019年

作者： Barhom, Shany Shwartz, Vered Eirew, Alon Bugert, Michael Reimers, Nils Dagan, Ido Computer Science Department Bar-Ilan University Intel AI Lab Israel Ubiquitous Knowledge Processing Lab Technische Universitat Darmstadt Germany

Recognizing coreferring events and entities across multiple texts is crucial for many NLP applications. Despite the task's importance, research focus was given mostly to withindocument entity coreference, with rather little attention to the other variants. We propose a neural architecture for cross-document coreference resolution. Inspired by Lee et al. (2012), we jointly model entity and event coreference. We represent an event (entity) mention using its lexical span, surrounding context, and relation to entity (event) mentions via predicate-arguments structures. Our model outperforms the previous state-of-the-art event coreference model on ECB+, while providing the first entity coreference results on this corpus. Our analysis confirms that all our representation elements, including the mention span itself, its context, and the relation to other mentions contribute to the model's success. Copyright © 2019, The Authors. All rights reserved.

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Constrained C-Test Generation via Mixed-Integer Programming

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arXiv 2024年

作者： Lee, Ji-Ung Pfetsch, Marc E. Gurevych, Iryna Ubiquitous Knowledge Processing Lab Department of Computer Science Germany Research Group Optimization Department of Mathematics Technical University of Darmstadt Germany Hessian AI

This work proposes a novel method to generate C-Tests;a deviated form of cloze tests (a gap filling exercise) where only the last part of a word is turned into a gap. In contrast to previous works that only consider varying the gap size or gap placement to achieve locally optimal solutions, we propose a mixed-integer programming (MIP) approach. This allows us to consider gap size and placement simultaneously, achieving globally optimal solutions, and to directly integrate state-of-the-art models for gap difficulty prediction into the optimization problem. A user study with 40 participants across four C-Test generation strategies (including GPT-4) shows that our approach (MIP) significantly outperforms two of the baseline strategies (based on gap placement and GPT-4);and performs on-par with the third (based on gap size). Our analysis shows that GPT-4 still struggles to fulfill explicit constraints during generation and that MIP produces C-Tests that correlate best with the perceived difficulty. We publish our code, model, and collected data consisting of 32 English C-Tests with 20 gaps each (totaling 3,200 individual gap responses) under an open source license.1 © 2024, CC BY.

关键词： Integer programming

On the linearity of semantic change: Investigating meaning variation via dynamic graph models

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arXiv 2017年

作者： Eger, Steffen Mehler, Alexander Ubiquitous Knowledge Processing Lab Department of Computer Science Technische Universität Darmstadt Germany Text Technology Lab Department of Computer Science Goethe-Universität Frankfurt am Main Germany

We consider two graph models of semantic change. The first is a time-series model that relates embedding vectors from one time period to embedding vectors of previous time periods. In the second, we construct one graph for each word: nodes in this graph correspond to time points and edge weights to the similarity of the word's meaning across two time points. We apply our two models to corpora across three different languages. We find that semantic change is linear in two senses. Firstly, today's embedding vectors (= meaning) of words can be derived as linear combinations of embedding vectors of their neighbors in previous time periods. Secondly, self-similarity of words decays linearly in time. We consider both findings as new laws/hypotheses of semantic change. Copyright © 2017, The Authors. All rights reserved.

关键词： Embeddings

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2013年 8105 LNAI卷 VII-VIII页

作者： Gurevych, Iryna Biemann, Chris Zesch, Torsten Ubiquitous Knowledge Processing Lab. Department of Computer Science Technische Universität Darmstadt Darmstadt Germany Frankfurt am Main Germany FG Language Technology Department of Computer Science Technische Universität Darmstadt Darmstadt Germany

Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future

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arXiv 2022年

作者： Klie, Jan-Christoph Webber, Bonnie Gurevych, Iryna Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany School of Informatics University of Edinburgh United Kingdom UKP Lab TU Darmstadt Germany

Annotated data is an essential ingredient in natural language processing for training and evaluating machine learning models. It is therefore very desirable for the annotations to be of high quality. Recent work, however, has shown that several popular datasets contain a surprising amount of annotation errors or inconsistencies. To alleviate this issue, many methods for annotation error detection have been devised over the years. While researchers show that their approaches work well on their newly introduced datasets, they rarely compare their methods to previous work or on the same datasets. This raises strong concerns on methods’ general performance and makes it difficult to asses their strengths and weaknesses. We therefore reimplement 18 methods for detecting potential annotation errors and evaluate them on 9 English datasets for text classification as well as token and span labeling. In addition, we define a uniform evaluation setup including a new formalization of the annotation error detection task, evaluation protocol and general best practices. To facilitate future research and reproducibility, we release our datasets and implementations in an easy-to-use and open source software package.1 Copyright © 2022, The Authors. All rights reserved.

关键词： Open source software

Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets

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arXiv 2022年

作者： Schiller, Benjamin Daxenberger, Johannes Waldis, Andreas Gurevych, Iryna Summetix GmbH Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany Information Systems Research Lab Lucerne University of Applied Sciences and Arts Switzerland

Topic-Dependent Argument Mining (TDAM), that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large TDAM datasets are rare and recognition of argument components requires expert knowledge. The task becomes even more difficult if it also involves stance detection of retrieved arguments. In this work, we investigate the effect of TDAM dataset composition in few- and zero-shot settings. Our findings show that, while fine-tuning is mandatory to achieve acceptable model performance, using carefully composed training samples and reducing the training sample size by up to almost 90% can still yield 95% of the maximum performance. This gain is consistent across three TDAM tasks on three different datasets. We also publish a new dataset1 and code2 for future benchmarking. Copyright © 2022, The Authors. All rights reserved.

关键词： Adversarial machine learning