Michael Strube


2020

pdf bib
A Large Harvested Corpus of Location Metonymy
Kevin Alex Mathews | Michael Strube
Proceedings of the 12th Language Resources and Evaluation Conference

Metonymy is a figure of speech in which an entity is referred to by another related entity. The existing datasets of metonymy are either too small in size or lack sufficient coverage. We propose a new, labelled, high-quality corpus of location metonymy called WiMCor, which is large in size and has high coverage. The corpus is harvested semi-automatically from English Wikipedia. We use different labels of varying granularity to annotate the corpus. The corpus can directly be used for training and evaluating automatic metonymy resolution systems. We construct benchmarks for metonymy resolution, and evaluate baseline methods using the new corpus.

pdf bib
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification
Federico López | Michael Strube
Findings of the Association for Computational Linguistics: EMNLP 2020

Label inventories for fine-grained entity typing have grown in size and complexity. Nonetheless, they exhibit a hierarchical structure. Hyperbolic spaces offer a mathematically appealing approach for learning hierarchical representations of symbolic data. However, it is not clear how to integrate hyperbolic components into downstream tasks. This is the first work that proposes a fully hyperbolic model for multi-class multi-label classification, which performs all operations in hyperbolic space. We evaluate the proposed model on two challenging datasets and compare to different baselines that operate under Euclidean assumptions. Our hyperbolic model infers the latent hierarchy from the class distribution, captures implicit hyponymic relations in the inventory, and shows performance on par with state-of-the-art methods on fine-grained classification with remarkable reduction of the parameter size. A thorough analysis sheds light on the impact of each component in the final prediction and showcases its ease of integration with Euclidean layers.

pdf bib
Proceedings of the First Workshop on Computational Approaches to Discourse
Chloé Braud | Christian Hardmeier | Junyi Jessy Li | Annie Louis | Michael Strube
Proceedings of the First Workshop on Computational Approaches to Discourse

pdf bib
Evaluation of Coreference Resolution Systems Under Adversarial Attacks
Haixia Chai | Wei Zhao | Steffen Eger | Michael Strube
Proceedings of the First Workshop on Computational Approaches to Discourse

A substantial overlap of coreferent mentions in the CoNLL dataset magnifies the recent progress on coreference resolution. This is because the CoNLL benchmark fails to evaluate the ability of coreference resolvers that requires linking novel mentions unseen at train time. In this work, we create a new dataset based on CoNLL, which largely decreases mention overlaps in the entire dataset and exposes the limitations of published resolvers on two aspects—lexical inference ability and understanding of low-level orthographic noise. Our findings show (1) the requirements for embeddings, used in resolvers, and for coreference resolutions are, by design, in conflict and (2) adversarial approaches are sometimes not legitimate to mitigate the obstacles, as they may falsely introduce mention overlaps in adversarial training and test sets, thus giving an inflated impression for the improvements.

pdf bib
Incremental Neural Lexical Coherence Modeling
Sungho Jeon | Michael Strube
Proceedings of the 28th International Conference on Computational Linguistics

Pretrained language models, neural models pretrained on massive amounts of data, have established the state of the art in a range of NLP tasks. They are based on a modern machine-learning technique, the Transformer which relates all items simultaneously to capture semantic relations in sequences. However, it differs from what humans do. Humans read sentences one-by-one, incrementally. Can neural models benefit by interpreting texts incrementally as humans do? We investigate this question in coherence modeling. We propose a coherence model which interprets sentences incrementally to capture lexical relations between them. We compare the state of the art in each task, simple neural models relying on a pretrained language model, and our model in two downstream tasks. Our findings suggest that interpreting texts incrementally as humans could be useful to design more advanced models.

pdf bib
Reconstructing Manual Information Extraction with DB-to-Document Backprojection: Experiments in the Life Science Domain
Mark-Christoph Müller | Sucheta Ghosh | Maja Rey | Ulrike Wittig | Wolfgang Müller | Michael Strube
Proceedings of the First Workshop on Scholarly Document Processing

We introduce a novel scientific document processing task for making previously inaccessible information in printed paper documents available to automatic processing. We describe our data set of scanned documents and data records from the biological database SABIO-RK, provide a definition of the task, and report findings from preliminary experiments. Rigorous evaluation proved challenging due to lack of gold-standard data and a difficult notion of correctness. Qualitative inspection of results, however, showed the feasibility and usefulness of the task

pdf bib
Centering-based Neural Coherence Modeling with Hierarchical Discourse Segments
Sungho Jeon | Michael Strube
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Previous neural coherence models have focused on identifying semantic relations between adjacent sentences. However, they do not have the means to exploit structural information. In this work, we propose a coherence model which takes discourse structural information into account without relying on human annotations. We approximate a linguistic theory of coherence, Centering theory, which we use to track the changes of focus between discourse segments. Our model first identifies the focus of each sentence, recognized with regards to the context, and constructs the structural relationship for discourse segments by tracking the changes of the focus. The model then incorporates this structural information into a structure-aware transformer. We evaluate our model on two tasks, automated essay scoring and assessing writing quality. Our results demonstrate that our model, built on top of a pretrained language model, achieves state-of-the-art performance on both tasks. We next statistically examine the identified trees of texts assigned to different quality scores. Finally, we investigate what our model learns in terms of theoretical claims.

2019

pdf bib
Adapting Deep Learning Methods for Mental Health Prediction on Social Media
Ivan Sekulic | Michael Strube
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

Mental health poses a significant challenge for an individual’s well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users’ mental status through deep learning-based models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model’s word-level attention weights.

pdf bib
On the Importance of Subword Information for Morphological Tasks in Truly Low-Resource Languages
Yi Zhu | Benjamin Heinzerling | Ivan Vulić | Michael Strube | Roi Reichart | Anna Korhonen
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Recent work has validated the importance of subword information for word representation learning. Since subwords increase parameter sharing ability in neural models, their value should be even more pronounced in low-data regimes. In this work, we therefore provide a comprehensive analysis focused on the usefulness of subwords for word representation learning in truly low-resource scenarios and for three representative morphological tasks: fine-grained entity typing, morphological tagging, and named entity recognition. We conduct a systematic study that spans several dimensions of comparison: 1) type of data scarcity which can stem from the lack of task-specific training data, or even from the lack of unannotated data required to train word embeddings, or both; 2) language type by working with a sample of 16 typologically diverse languages including some truly low-resource ones (e.g. Rusyn, Buryat, and Zulu); 3) the choice of the subword-informed word representation method. Our main results show that subword-informed models are universally useful across all language types, with large gains over subword-agnostic embeddings. They also suggest that the effective use of subwords largely depends on the language (type) and the task at hand, as well as on the amount of available data for training the embeddings and task-based models, where having sufficient in-task data is a more critical requirement.

pdf bib
Sequence Tagging with Contextual and Non-Contextual Subword Representations: A Multilingual Evaluation
Benjamin Heinzerling | Michael Strube
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Pretrained contextual and non-contextual subword embeddings have become available in over 250 languages, allowing massively multilingual NLP. However, while there is no dearth of pretrained embeddings, the distinct lack of systematic evaluations makes it difficult for practitioners to choose between them. In this work, we conduct an extensive evaluation comparing non-contextual subword embeddings, namely FastText and BPEmb, and a contextual representation method, namely BERT, on multilingual named entity recognition and part-of-speech tagging. We find that overall, a combination of BERT, BPEmb, and character representations works best across languages and tasks. A more detailed analysis reveals different strengths and weaknesses: Multilingual BERT performs well in medium- to high-resource languages, but is outperformed by non-contextual subword embeddings in a low-resource setting.

pdf bib
Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection
Nafise Sadat Moosavi | Leo Born | Massimo Poesio | Michael Strube
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

The common practice in coreference resolution is to identify and evaluate the maximum span of mentions. The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepositional phrase attachment. To address this problem, minimum spans are manually annotated in smaller corpora. However, this additional annotation is costly and therefore, this solution does not scale to large corpora. In this paper, we propose the MINA algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora. We show that the extracted minimum spans by MINA are consistent with those that are manually annotated by experts. Our experiments show that using minimum spans is in particular important in cross-dataset coreference evaluation, in which detected mention boundaries are noisier due to domain shift. We have integrated MINA into https://github.com/ns-moosavi/coval for reporting standard coreference scores based on both maximum and automatically detected minimum spans.

pdf bib
Fine-Grained Entity Typing in Hyperbolic Space
Federico López | Benjamin Heinzerling | Michael Strube
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)

How can we represent hierarchical information present in large type inventories for entity typing? We study the suitability of hyperbolic embeddings to capture hierarchical relations between mentions in context and their target types in a shared vector space. We evaluate on two datasets and propose two different techniques to extract hierarchical information from the type inventory: from an expert-generated ontology and by automatically mining the dataset. The hyperbolic model shows improvements in some but not all cases over its Euclidean counterpart. Our analysis suggests that the adequacy of this geometry depends on the granularity of the type inventory and the representation of its distribution.

pdf bib
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials
Anoop Sarkar | Michael Strube
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials

2018

pdf bib
BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages
Benjamin Heinzerling | Michael Strube
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Using Linguistic Features to Improve the Generalization Capability of Neural Coreference Resolvers
Nafise Sadat Moosavi | Michael Strube
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Coreference resolution is an intermediate step for text understanding. It is used in tasks and domains for which we do not necessarily have coreference annotated corpora. Therefore, generalization is of special importance for coreference resolution. However, while recent coreference resolvers have notable improvements on the CoNLL dataset, they struggle to generalize properly to new domains or datasets. In this paper, we investigate the role of linguistic features in building more generalizable coreference resolvers. We show that generalization improves only slightly by merely using a set of additional linguistic features. However, employing features and subsets of their values that are informative for coreference resolution, considerably improves generalization. Thanks to better generalization, our system achieves state-of-the-art results in out-of-domain evaluations, e.g., on WikiCoref, our system, which is trained on CoNLL, achieves on-par performance with a system designed for this dataset.

pdf bib
A Neural Local Coherence Model for Text Quality Assessment
Mohsen Mesgar | Michael Strube
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a local coherence model that captures the flow of what semantically connects adjacent sentences in a text. We represent the semantics of a sentence by a vector and capture its state at each word of the sentence. We model what relates two adjacent sentences based on the two most similar semantic states, each of which is in one of the sentences. We encode the perceived coherence of a text by a vector, which represents patterns of changes in salient information that relates adjacent sentences. Our experiments demonstrate that our approach is beneficial for two downstream tasks: Readability assessment, in which our model achieves new state-of-the-art results; and essay scoring, in which the combination of our coherence vectors and other task-dependent features significantly improves the performance of a strong essay scorer.

pdf bib
Transparent, Efficient, and Robust Word Embedding Access with WOMBAT
Mark-Christoph Müller | Michael Strube
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

We present WOMBAT, a Python tool which supports NLP practitioners in accessing word embeddings from code. WOMBAT addresses common research problems, including unified access, scaling, and robust and reproducible preprocessing. Code that uses WOMBAT for accessing word embeddings is not only cleaner, more readable, and easier to reuse, but also much more efficient than code using standard in-memory methods: a Python script using WOMBAT for evaluating seven large word embedding collections (8.7M embedding vectors in total) on a simple SemEval sentence similarity task involving 250 raw sentence pairs completes in under ten seconds end-to-end on a standard notebook computer.

pdf bib
Unrestricted Bridging Resolution
Yufang Hou | Katja Markert | Michael Strube
Computational Linguistics, Volume 44, Issue 2 - June 2018

In contrast to identity anaphors, which indicate coreference between a noun phrase and its antecedent, bridging anaphors link to their antecedent(s) via lexico-semantic, frame, or encyclopedic relations. Bridging resolution involves recognizing bridging anaphors and finding links to antecedents. In contrast to most prior work, we tackle both problems. Our work also follows a more wide-ranging definition of bridging than most previous work and does not impose any restrictions on the type of bridging anaphora or relations between anaphor and antecedent. We create a corpus (ISNotes) annotated for information status (IS), bridging being one of the IS subcategories. The annotations reach high reliability for all categories and marginal reliability for the bridging subcategory. We use a two-stage statistical global inference method for bridging resolution. Given all mentions in a document, the first stage, bridging anaphora recognition, recognizes bridging anaphors as a subtask of learning fine-grained IS. We use a cascading collective classification method where (i) collective classification allows us to investigate relations among several mentions and autocorrelation among IS classes and (ii) cascaded classification allows us to tackle class imbalance, important for minority classes such as bridging. We show that our method outperforms current methods both for IS recognition overall as well as for bridging, specifically. The second stage, bridging antecedent selection, finds the antecedents for all predicted bridging anaphors. We investigate the phenomenon of semantically or syntactically related bridging anaphors that share the same antecedent, a phenomenon we call sibling anaphors. We show that taking sibling anaphors into account in a joint inference model improves antecedent selection performance. In addition, we develop semantic and salience features for antecedent selection and suggest a novel method to build the candidate antecedent list for an anaphor, using the discourse scope of the anaphor. Our model outperforms previous work significantly.

pdf bib
Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing
Mark Alfano | Dirk Hovy | Margaret Mitchell | Michael Strube
Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing

2017

pdf bib
Lexical Features in Coreference Resolution: To be Used With Caution
Nafise Sadat Moosavi | Michael Strube
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Lexical features are a major source of information in state-of-the-art coreference resolvers. Lexical features implicitly model some of the linguistic phenomena at a fine granularity level. They are especially useful for representing the context of mentions. In this paper we investigate a drawback of using many lexical features in state-of-the-art coreference resolvers. We show that if coreference resolvers mainly rely on lexical features, they can hardly generalize to unseen domains. Furthermore, we show that the current coreference resolution evaluation is clearly flawed by only evaluating on a specific split of a specific dataset in which there is a notable overlap between the training, development and test sets.

pdf bib
Trust, but Verify! Better Entity Linking through Automatic Verification
Benjamin Heinzerling | Michael Strube | Chin-Yew Lin
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We introduce automatic verification as a post-processing step for entity linking (EL). The proposed method trusts EL system results collectively, by assuming entity mentions are mostly linked correctly, in order to create a semantic profile of the given text using geospatial and temporal information, as well as fine-grained entity types. This profile is then used to automatically verify each linked mention individually, i.e., to predict whether it has been linked correctly or not. Verification allows leveraging a rich set of global and pairwise features that would be prohibitively expensive for EL systems employing global inference. Evaluation shows consistent improvements across datasets and systems. In particular, when applied to state-of-the-art systems, our method yields an absolute improvement in linking performance of up to 1.7 F1 on AIDA/CoNLL’03 and up to 2.4 F1 on the English TAC KBP 2015 TEDL dataset.

pdf bib
Event Argument Identification on Dependency Graphs with Bidirectional LSTMs
Alex Judea | Michael Strube
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In this paper we investigate the performance of event argument identification. We show that the performance is tied to syntactic complexity. Based on this finding, we propose a novel and effective system for event argument identification. Recurrent Neural Networks learn to produce meaningful representations of long and short dependency paths. Convolutional Neural Networks learn to decompose the lexical context of argument candidates. They are combined into a simple system which outperforms a feature-based, state-of-the-art event argument identifier without any manual feature engineering.

pdf bib
Proceedings of the IJCNLP 2017, Tutorial Abstracts
Sadao Kurohashi | Michael Strube
Proceedings of the IJCNLP 2017, Tutorial Abstracts

pdf bib
Use Generalized Representations, But Do Not Forget Surface Features
Nafise Sadat Moosavi | Michael Strube
Proceedings of the 2nd Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2017)

Only a year ago, all state-of-the-art coreference resolvers were using an extensive amount of surface features. Recently, there was a paradigm shift towards using word embeddings and deep neural networks, where the use of surface features is very limited. In this paper, we show that a simple SVM model with surface features outperforms more complex neural models for detecting anaphoric mentions. Our analysis suggests that using generalized representations and surface features have different strength that should be both taken into account for improving coreference resolution.

pdf bib
Proceedings of the First ACL Workshop on Ethics in Natural Language Processing
Dirk Hovy | Shannon Spruit | Margaret Mitchell | Emily M. Bender | Michael Strube | Hanna Wallach
Proceedings of the First ACL Workshop on Ethics in Natural Language Processing

pdf bib
Using a Graph-based Coherence Model in Document-Level Machine Translation
Leo Born | Mohsen Mesgar | Michael Strube
Proceedings of the Third Workshop on Discourse in Machine Translation

Although coherence is an important aspect of any text generation system, it has received little attention in the context of machine translation (MT) so far. We hypothesize that the quality of document-level translation can be improved if MT models take into account the semantic relations among sentences during translation. We integrate the graph-based coherence model proposed by Mesgar and Strube, (2016) with Docent (Hardmeier et al., 2012, Hardmeier, 2014) a document-level machine translation system. The application of this graph-based coherence modeling approach is novel in the context of machine translation. We evaluate the coherence model and its effects on the quality of the machine translation. The result of our experiments shows that our coherence model slightly improves the quality of translation in terms of the average Meteor score.

pdf bib
Revisiting Selectional Preferences for Coreference Resolution
Benjamin Heinzerling | Nafise Sadat Moosavi | Michael Strube
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Selectional preferences have long been claimed to be essential for coreference resolution. However, they are modeled only implicitly by current coreference resolvers. We propose a dependency-based embedding model of selectional preferences which allows fine-grained compatibility judgments with high coverage. Incorporating our model improves performance, matching state-of-the-art results of a more complex system. However, it comes with a cost that makes it debatable how worthwhile are such improvements.

2016

pdf bib
Generating Coherent Summaries of Scientific Articles Using Coherence Patterns
Daraksha Parveen | Mohsen Mesgar | Michael Strube
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

pdf bib
Search Space Pruning: A Simple Solution for Better Coreference Resolvers
Nafise Sadat Moosavi | Michael Strube
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Lexical Coherence Graph Modeling Using Word Embeddings
Mohsen Mesgar | Michael Strube
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Incremental Global Event Extraction
Alex Judea | Michael Strube
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Event extraction is a difficult information extraction task. Li et al. (2014) explore the benefits of modeling event extraction and two related tasks, entity mention and relation extraction, jointly. This joint system achieves state-of-the-art performance in all tasks. However, as a system operating only at the sentence level, it misses valuable information from other parts of the document. In this paper, we present an incremental easy-first approach to make the global context of the entire document available to the intra-sentential, state-of-the-art event extractor. We show that our method robustly increases performance on two datasets, namely ACE 2005 and TAC 2015.

pdf bib
Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric
Nafise Sadat Moosavi | Michael Strube
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Feature-Rich Error Detection in Scientific Writing Using Logistic Regression
Madeline Remse | Mohsen Mesgar | Michael Strube
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

pdf bib
Microblog Emotion Classification by Computing Similarity in Text, Time, and Space
Anja Summa | Bernd Resch | Michael Strube
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

Most work in NLP analysing microblogs focuses on textual content thus neglecting temporal and spatial information. We present a new interdisciplinary method for emotion classification that combines linguistic, temporal, and spatial information into a single metric. We create a graph of labeled and unlabeled tweets that encodes the relations between neighboring tweets with respect to their emotion labels. Graph-based semi-supervised learning labels all tweets with an emotion.

2015

pdf bib
Topical Coherence for Graph-based Extractive Summarization
Daraksha Parveen | Hans-Martin Ramsl | Michael Strube
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

pdf bib
Analyzing and Visualizing Coreference Resolution Errors
Sebastian Martschat | Thierry Göckel | Michael Strube
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

pdf bib
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Chengqing Zong | Michael Strube
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

pdf bib
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Chengqing Zong | Michael Strube
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

pdf bib
Visual Error Analysis for Entity Linking
Benjamin Heinzerling | Michael Strube
Proceedings of ACL-IJCNLP 2015 System Demonstrations

pdf bib
Plug Latent Structures and Play Coreference Resolution
Sebastian Martschat | Patrick Claus | Michael Strube
Proceedings of ACL-IJCNLP 2015 System Demonstrations

pdf bib
Event Extraction as Frame-Semantic Parsing
Alex Judea | Michael Strube
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

pdf bib
Graph-based Coherence Modeling For Assessing Readability
Mohsen Mesgar | Michael Strube
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

pdf bib
Latent Structures for Coreference Resolution
Sebastian Martschat | Michael Strube
Transactions of the Association for Computational Linguistics, Volume 3

Machine learning approaches to coreference resolution vary greatly in the modeling of the problem: while early approaches operated on the mention pair level, current research focuses on ranking architectures and antecedent trees. We propose a unified representation of different approaches to coreference resolution in terms of the structure they operate on. We represent several coreference resolution approaches proposed in the literature in our framework and evaluate their performance. Finally, we conduct a systematic analysis of the output of these approaches, highlighting differences and similarities.

2014

pdf bib
Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation
Sameer Pradhan | Xiaoqiang Luo | Marta Recasens | Eduard Hovy | Vincent Ng | Michael Strube
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

pdf bib
A Latent Variable Model for Discourse-aware Concept and Entity Disambiguation
Angela Fahrni | Michael Strube
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

pdf bib
Normalized Entity Graph for Computing Local Coherence
Mohsen Mesgar | Michael Strube
Proceedings of TextGraphs-9: the workshop on Graph-based Methods for Natural Language Processing

pdf bib
Multi-document Summarization Using Bipartite Graphs
Daraksha Parveen | Michael Strube
Proceedings of TextGraphs-9: the workshop on Graph-based Methods for Natural Language Processing

pdf bib
Unsupervised Coreference Resolution by Utilizing the Most Informative Relations
Nafise Sadat Moosavi | Michael Strube
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

pdf bib
Recall Error Analysis for Coreference Resolution
Sebastian Martschat | Michael Strube
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

pdf bib
A Rule-Based System for Unrestricted Bridging Resolution: Recognizing Bridging Anaphora and Finding Links to Antecedents
Yufang Hou | Katja Markert | Michael Strube
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

pdf bib
Cascading Collective Classification for Bridging Anaphora Recognition using a Rich Linguistic Feature Set
Yufang Hou | Katja Markert | Michael Strube
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

pdf bib
Proceedings of the SIGDIAL 2013 Conference
Maxine Eskenazi | Michael Strube | Barbara Di Eugenio | Jason D. Williams
Proceedings of the SIGDIAL 2013 Conference

pdf bib
Graph-based Local Coherence Modeling
Camille Guinaudeau | Michael Strube
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Global Inference for Bridging Anaphora Resolution
Yufang Hou | Katja Markert | Michael Strube
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

pdf bib
Collective Classification for Fine-grained Information Status
Katja Markert | Yufang Hou | Michael Strube
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts
Michael Strube
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

pdf bib
Concept-based Selectional Preferences and Distributional Representations from Wikipedia Articles
Alex Judea | Vivi Nastase | Michael Strube
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper describes the derivation of distributional semantic representations for open class words relative to a concept inventory, and of concepts relative to open class words through grammatical relations extracted from Wikipedia articles. The concept inventory comes from WikiNet, a large-scale concept network derived from Wikipedia. The distinctive feature of these representations are their relation to a concept network, through which we can compute selectional preferences of open-class words relative to general concepts. The resource thus derived provides a meaning representation that complements the relational representation captured in the concept network. It covers English open-class words, but the concept base is language independent. The resource can be extended to other languages, with the use of language specific dependency parsers. Good results in metonymy resolution show the resource's potential use for NLP applications.

pdf bib
A Multigraph Model for Coreference Resolution
Sebastian Martschat | Jie Cai | Samuel Broscheit | Éva Mújdricza-Maydt | Michael Strube
Joint Conference on EMNLP and CoNLL - Shared Task

pdf bib
Jointly Disambiguating and Clustering Concepts and Entities with Markov Logic
Angela Fahrni | Michael Strube
Proceedings of COLING 2012

pdf bib
Local and Global Context for Supervised and Unsupervised Metonymy Resolution
Vivi Nastase | Alex Judea | Katja Markert | Michael Strube
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

pdf bib
Unrestricted Coreference Resolution via Global Hypergraph Partitioning
Jie Cai | Éva Mújdricza-Maydt | Michael Strube
Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task

pdf bib
Fine-Grained Sentiment Analysis with Structural Features
Cäcilia Zirn | Mathias Niepert | Heiner Stuckenschmidt | Michael Strube
Proceedings of 5th International Joint Conference on Natural Language Processing

pdf bib
WikiNetTK – A Tool Kit for EmbeddingWorld Knowledge in NLP Applications
Alex Judea | Vivi Nastase | Michael Strube
Proceedings of the IJCNLP 2011 System Demonstrations

2010

pdf bib
Evaluation Metrics For End-to-End Coreference Resolution Systems
Jie Cai | Michael Strube
Proceedings of the SIGDIAL 2010 Conference

pdf bib
End-to-End Coreference Resolution via Hypergraph Partitioning
Jie Cai | Michael Strube
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

pdf bib
WikiNet: A Very Large Scale Multi-Lingual Concept Network
Vivi Nastase | Michael Strube | Benjamin Boerschinger | Caecilia Zirn | Anas Elghafari
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper describes a multi-lingual large-scale concept network obtained automatically by mining for concepts and relations and exploiting a variety of sources of knowledge from Wikipedia. Concepts and their lexicalizations are extracted from Wikipedia pages, in particular from article titles, hyperlinks, disambiguation pages and cross-language links. Relations are extracted from the category and page network, from the category names, from infoboxes and the body of the articles. The resulting network has two main components: (i) a central, language independent index of concepts, which serves to keep track of the concepts' lexicalizations both within a language and across languages, and to separate linguistic expressions of concepts from the relations in which they are involved (concepts themselves are represented as numeric IDs); (ii) a large network built on the basis of the relations extracted, represented as relations between concepts (more specifically, the numeric IDs). The various stages of obtaining the network were separately evaluated, and the results show a qualitative resource.

2009

pdf bib
Combining Collocations, Lexical and Encyclopedic Knowledge for Metonymy Resolution
Vivi Nastase | Michael Strube
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

pdf bib
Creating an Annotated Corpus for Generating Walking Directions
Stephanie Schuldes | Michael Roth | Anette Frank | Michael Strube
Proceedings of the 2009 Workshop on Language Generation and Summarisation (UCNLG+Sum 2009)

pdf bib
Finding Hedges by Chasing Weasels: Hedge Detection Using Wikipedia Tags and Shallow Linguistic Features
Viola Ganter | Michael Strube
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

pdf bib
Tree Linearization in English: Improving Language Model Based Approaches
Katja Filippova | Michael Strube
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

pdf bib
Extracting World and Linguistic Knowledge from Wikipedia
Simone Paolo Ponzetto | Michael Strube
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Tutorial Abstracts

2008

pdf bib
Knowledge Sources for Bridging Resolution in Multi-Party Dialog
Mark-Christoph Mueller | Margot Mieskes | Michael Strube
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper we investigate the coverage of the two knowledge sources WordNet and Wikipedia for the task of bridging resolution. We report on an annotation experiment which yielded pairs of bridging anaphors and their antecedents in spoken multi-party dialog. Manual inspection of the two knowledge sources showed that, with some interesting exceptions, Wikipedia is superior to WordNet when it comes to the coverage of information necessary to resolve the bridging anaphors in our data set. We further describe a simple procedure for the automatic extraction of the required knowledge from Wikipedia by means of an API, and discuss some of the implications of the procedure’s performance.

pdf bib
A Three-stage Disfluency Classifier for Multi Party Dialogues
Margot Mieskes | Michael Strube
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We present work on a three-stage system to detect and classify disfluencies in multi party dialogues. The system consists of a regular expression based module and two machine learning based modules. The results are compared to other work on multi party dialogues and we show that our system outperforms previously reported ones.

pdf bib
Acquiring a Taxonomy from the German Wikipedia
Laura Kassner | Vivi Nastase | Michael Strube
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

This paper presents the process of acquiring a large, domain independent, taxonomy from the German Wikipedia. We build upon a previously implemented platform that extracts a semantic network and taxonomy from the English version of the Wikipedia. We describe two accomplishments of our work: the semantic network for the German language in which isa links are identified and annotated, and an expansion of the platform for easy adaptation for a new language. We identify the platform’s strengths and shortcomings, which stem from the scarcity of free processing resources for languages other than English. We show that the taxonomy induction process is highly reliable - evaluated against the German version of WordNet, GermaNet, the resource obtained shows an accuracy of 83.34%.

pdf bib
Parameters for Topic Boundary Detection in Multi-Party Dialogues
Margot Mieskes | Michael Strube
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We present a topic boundary detection method that searches for connections between sequences of utterances in multi party dialogues. The connections are established based on word identity. We compare our method to a state-of-the art automatic Topic boundary detection method that was also used on multi party dialogues. We checked various methods of preprocessing of the data, including stemming, lemmatization and stopword filtering with a text-based as well as speech-based stopword lists. Using standard evaluation methods we found that our method outperformed the state-of-the art method.

pdf bib
Dependency Tree Based Sentence Compression
Katja Filippova | Michael Strube
Proceedings of the Fifth International Natural Language Generation Conference

pdf bib
Sentence Fusion via Dependency Graph Compression
Katja Filippova | Michael Strube
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2007

pdf bib
Extending the Entity-grid Coherence Model to Semantically Related Entities
Katja Filippova | Michael Strube
Proceedings of the Eleventh European Workshop on Natural Language Generation (ENLG 07)

pdf bib
Generating Constituent Order in German Clauses
Katja Filippova | Michael Strube
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

pdf bib
An API for Measuring the Relatedness of Words in Wikipedia
Simone Paolo Ponzetto | Michael Strube
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

2006

pdf bib
Part-of-Speech Tagging of Transcribed Speech
Margot Mieskes | Michael Strube
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We used four Part-of-Speech taggers, which are available for research purposes and were originally trained on text to tag a corpus of transcribed multiparty spoken dialogues. The assigned tags were then manually corrected. The correction was first used to evaluate the four taggers, then to retrain them. Despite limited resources in time, money and annotators we reached results comparable to those reported for the taggers on text. Based on our experience we present guidelines to produce reliably POS tagged corpora of new domains.

pdf bib
Using linguistically motivated features for paragraph boundary identification
Katja Filippova | Michael Strube
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

pdf bib
Semantic Role Labeling for Coreference Resolution
Simone Paolo Ponzetto | Michael Strube
Demonstrations

pdf bib
Exploiting Semantic Role Labeling, WordNet and Wikipedia for Coreference Resolution
Simone Paolo Ponzetto | Michael Strube
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference

2005

pdf bib
Beyond the Pipeline: Discrete Optimization in NLP
Tomasz Marciniak | Michael Strube
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

pdf bib
Semantic Role Labeling Using Lexical Statistical Information
Simone Paolo Ponzetto | Michael Strube
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

pdf bib
Discrete Optimization as an Alternative to Sequential Processing in NLG
Tomasz Marciniak | Michael Strube
Proceedings of the Tenth European Workshop on Natural Language Generation (ENLG-05)

2004

pdf bib
Semantic Similarity Applied to Spoken Dialogue Summarization
Iryna Gurevych | Michael Strube
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

pdf bib
Multi-Level Annotation in MMAX
Christoph Müller | Michael Strube
Proceedings of the Fourth SIGdial Workshop of Discourse and Dialogue

pdf bib
A Machine Learning Approach to Pronoun Resolution in Spoken Dialogue
Michael Strube | Christoph Müller
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

pdf bib
Book Reviews: Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms by Thorsten Joachims; Anaphora Resolution by Ruslan Mitkov
Roberto Basili | Michael Strube
Computational Linguistics, Volume 29, Number 4, December 2003

2002

pdf bib
Annotating the Semantic Consistency of Speech Recognition Hypotheses
Iryna Gurevych | Robert Porzel | Michael Strube
Proceedings of the Third SIGdial Workshop on Discourse and Dialogue

pdf bib
The Influence of Minimum Edit Distance on Reference Resolution
Michael Strube | Stefan Rapp | Christoph Müller
Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002)

pdf bib
Applying Co-Training to Reference Resolution
Christoph Mueller | Stefan Rapp | Michael Strube
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

pdf bib
An Iterative Data Collection Approach for Multimodal Dialogue Systems
Stefan Rapp | Michael Strube
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

pdf bib
An API for Discourse-level Access to XML-encoded Corpora
Christoph Müller | Michael Strube
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

2001

pdf bib
Annotating Anaphoric and Bridging Relations with MMAX
Christoph Mueller | Michael Strube
Proceedings of the Second SIGdial Workshop on Discourse and Dialogue

2000

pdf bib
A Probabilistic Genre-Independent Model of Pronominalization
Michael Strube | Maria Wolters
1st Meeting of the North American Chapter of the Association for Computational Linguistics

1999

pdf bib
Resolving Discourse Deictic Anaphora in Dialogues
Miriam Eckert | Michael Strube
Ninth Conference of the European Chapter of the Association for Computational Linguistics

pdf bib
Building a Tool for Annotating Reference in Discourse
Jonathan DeCristofaro | Michael Strube | Kathleen E. McCoy
The Relation of Discourse/Dialogue Structure and Reference

pdf bib
Generating Anaphoric Expressions: Pronoun or Definite Description?
Kathleen E. McCoy | Michael Strube
The Relation of Discourse/Dialogue Structure and Reference

pdf bib
Functional Centering – Grounding Referential Coherence on Information Structure
Michael Strube | Udo Hahn
Computational Linguistics, Volume 25, Number 3, September 1999

1998

pdf bib
Never Look Back: An Alternative to Centering
Michael Strube
COLING 1998 Volume 2: The 17th International Conference on Computational Linguistics

pdf bib
Never Look Back: An Alternative to Centering
Michael Strube
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 2

1997

pdf bib
Centering in-the-Large: Computing Referential Discourse Segments
Udo Hahn | Michael Strube
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

1996

pdf bib
Functional Centering
Michael Strube | Udo Hahn
34th Annual Meeting of the Association for Computational Linguistics

pdf bib
Processing Complex Sentences in the Centering Framework
Michael Strube
34th Annual Meeting of the Association for Computational Linguistics

pdf bib
Bridging Textual Ellipses
Udo Hahn | Michael Strube | Katja Markert
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

1995

pdf bib
ParseTalk about Sentence- and Text-Level Anaphora
Michael Strube | Udo Hahn
Seventh Conference of the European Chapter of the Association for Computational Linguistics