Annemarie Friedrich


2020

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The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain
Annemarie Friedrich | Heike Adel | Federico Tomazic | Johannes Hingerl | Renou Benteau | Anika Marusczyk | Lukas Lange
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper presents a new challenging information extraction task in the domain of materials science. We develop an annotation scheme for marking information on experiments related to solid oxide fuel cells in scientific publications, such as involved materials and measurement conditions. With this paper, we publish our annotation guidelines, as well as our SOFC-Exp corpus consisting of 45 open-access scholarly articles annotated by domain experts. A corpus and an inter-annotator agreement study demonstrate the complexity of the suggested named entity recognition and slot filling tasks as well as high annotation quality. We also present strong neural-network based models for a variety of tasks that can be addressed on the basis of our new data set. On all tasks, using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network on top seems beneficial. Our models will serve as competitive baselines in future work, and analysis of their performance highlights difficult cases when modeling the data and suggests promising research directions.

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ClusterDataSplit: Exploring Challenging Clustering-Based Data Splits for Model Performance Evaluation
Hanna Wecker | Annemarie Friedrich | Heike Adel
Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems

This paper adds to the ongoing discussion in the natural language processing community on how to choose a good development set. Motivated by the real-life necessity of applying machine learning models to different data distributions, we propose a clustering-based data splitting algorithm. It creates development (or test) sets which are lexically different from the training data while ensuring similar label distributions. Hence, we are able to create challenging cross-validation evaluation setups while abstracting away from performance differences resulting from label distribution shifts between training and test data. In addition, we present a Python-based tool for analyzing and visualizing data split characteristics and model performance. We illustrate the workings and results of our approach using a sentiment analysis and a patent classification task.

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RobertNLP at the IWPT 2020 Shared Task: Surprisingly Simple Enhanced UD Parsing for English
Stefan Grünewald | Annemarie Friedrich
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

This paper presents our system at the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies. Using a biaffine classifier architecture (Dozat and Manning, 2017) which operates directly on finetuned RoBERTa embeddings, our parser generates enhanced UD graphs by predicting the best dependency label (or absence of a dependency) for each pair of tokens in the sentence. We address label sparsity issues by replacing lexical items in relations with placeholders at prediction time, later retrieving them from the parse in a rule-based fashion. In addition, we ensure structural graph constraints using a simple set of heuristics. On the English blind test data, our system achieves a very high parsing accuracy, ranking 1st out of 10 with an ELAS F1 score of 88.94%.

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Unifying the Treatment of Preposition-Determiner Contractions in German Universal Dependencies Treebanks
Stefan Grünewald | Annemarie Friedrich
Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)

HDT-UD, the largest German UD treebank by a large margin, as well as the German-LIT treebank, currently do not analyze preposition-determiner contractions such as zum (= zu dem, “to the”) as multi-word tokens, which is inconsistent both with UD guidelines as well as other German UD corpora (GSD and PUD). In this paper, we show that harmonizing corpora with regard to this highly frequent phenomenon using a lookup-table based approach leads to a considerable increase in automatic parsing performance.

2019

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Proceedings of the 13th Linguistic Annotation Workshop
Annemarie Friedrich | Deniz Zeyrek | Jet Hoek
Proceedings of the 13th Linguistic Annotation Workshop

2017

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Annotating tense, mood and voice for English, French and German
Anita Ramm | Sharid Loáiciga | Annemarie Friedrich | Alexander Fraser
Proceedings of ACL 2017, System Demonstrations

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Classification of telicity using cross-linguistic annotation projection
Annemarie Friedrich | Damyana Gateva
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper addresses the automatic recognition of telicity, an aspectual notion. A telic event includes a natural endpoint (“she walked home”), while an atelic event does not (“she walked around”). Recognizing this difference is a prerequisite for temporal natural language understanding. In English, this classification task is difficult, as telicity is a covert linguistic category. In contrast, in Slavic languages, aspect is part of a verb’s meaning and even available in machine-readable dictionaries. Our contributions are as follows. We successfully leverage additional silver standard training data in the form of projected annotations from parallel English-Czech data as well as context information, improving automatic telicity classification for English significantly compared to previous work. We also create a new data set of English texts manually annotated with telicity.

2016

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Situation entity types: automatic classification of clause-level aspect
Annemarie Friedrich | Alexis Palmer | Manfred Pinkal
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016)
Annemarie Friedrich | Katrin Tomanek
Proceedings of the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016)

2015

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Automatic recognition of habituals: a three-way classification of clausal aspect
Annemarie Friedrich | Manfred Pinkal
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Annotating genericity: a survey, a scheme, and a corpus
Annemarie Friedrich | Alexis Palmer | Melissa Peate Sørensen | Manfred Pinkal
Proceedings of The 9th Linguistic Annotation Workshop

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Linking discourse modes and situation entity types in a cross-linguistic corpus study
Kleio-Isidora Mavridou | Annemarie Friedrich | Melissa Peate Sørensen | Alexis Palmer | Manfred Pinkal
Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics

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Semantically Enriched Models for Modal Sense Classification
Mengfei Zhou | Anette Frank | Annemarie Friedrich | Alexis Palmer
Proceedings of the First Workshop on Linking Computational Models of Lexical, Sentential and Discourse-level Semantics

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Discourse-sensitive Automatic Identification of Generic Expressions
Annemarie Friedrich | Manfred Pinkal
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)

2014

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Automatic prediction of aspectual class of verbs in context
Annemarie Friedrich | Alexis Palmer
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Situation Entity Annotation
Annemarie Friedrich | Alexis Palmer
Proceedings of LAW VIII - The 8th Linguistic Annotation Workshop

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LQVSumm: A Corpus of Linguistic Quality Violations in Multi-Document Summarization
Annemarie Friedrich | Marina Valeeva | Alexis Palmer
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present LQVSumm, a corpus of about 2000 automatically created extractive multi-document summaries from the TAC 2011 shared task on Guided Summarization, which we annotated with several types of linguistic quality violations. Examples for such violations include pronouns that lack antecedents or ungrammatical clauses. We give details on the annotation scheme and show that inter-annotator agreement is good given the open-ended nature of the task. The annotated summaries have previously been scored for Readability on a numeric scale by human annotators in the context of the TAC challenge; we show that the number of instances of violations of linguistic quality of a summary correlates with these intuitively assigned numeric scores. On a system-level, the average number of violations marked in a system’s summaries achieves higher correlation with the Readability scores than current supervised state-of-the-art methods for assigning a single readability score to a summary. It is our hope that our corpus facilitates the development of methods that not only judge the linguistic quality of automatically generated summaries as a whole, but which also allow for detecting, labeling, and fixing particular violations in a text.

2012

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Suffix Trees as Language Models
Casey Redd Kennington | Martin Kay | Annemarie Friedrich
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Suffix trees are data structures that can be used to index a corpus. In this paper, we explore how some properties of suffix trees naturally provide the functionality of an n-gram language model with variable n. We explain these properties of suffix trees, which we leverage for our Suffix Tree Language Model (STLM) implementation and explain how a suffix tree implicitly contains the data needed for n-gram language modeling. We also discuss the kinds of smoothing techniques appropriate to such a model. We then show that our suffix-tree language model implementation is competitive when compared to the state-of-the-art language model SRILM (Stolke, 2002) in statistical machine translation experiments.

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A Comparison of Knowledge-based Algorithms for Graded Word Sense Assignment
Annemarie Friedrich | Nikos Engonopoulos | Stefan Thater | Manfred Pinkal
Proceedings of COLING 2012: Posters