Anette Frank


2020

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Implicit Knowledge in Argumentative Texts: An Annotated Corpus
Maria Becker | Katharina Korfhage | Anette Frank
Proceedings of the 12th Language Resources and Evaluation Conference

When speaking or writing, people omit information that seems clear and evident, such that only part of the message is expressed in words. Especially in argumentative texts it is very common that (important) parts of the argument are implied and omitted. We hypothesize that for argument analysis it will be beneficial to reconstruct this implied information. As a starting point for filling knowledge gaps, we build a corpus consisting of high-quality human annotations of missing and implied information in argumentative texts. To learn more about the characteristics of both the argumentative texts and the added information, we further annotate the data with semantic clause types and commonsense knowledge relations. The outcome of our work is a carefully designed and richly annotated dataset, for which we then provide an in-depth analysis by investigating characteristic distributions and correlations of the assigned labels. We reveal interesting patterns and intersections between the annotation categories and properties of our dataset, which enable insights into the characteristics of both argumentative texts and implicit knowledge in terms of structural features and semantic information. The results of our analysis can help to assist automated argument analysis and can guide the process of revealing implicit information in argumentative texts automatically.

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Social Commonsense Reasoning with Multi-Head Knowledge Attention
Debjit Paul | Anette Frank
Findings of the Association for Computational Linguistics: EMNLP 2020

Social Commonsense Reasoning requires understanding of text, knowledge about social events and their pragmatic implications, as well as commonsense reasoning skills. In this work we propose a novel multi-head knowledge attention model that encodes semi-structured commonsense inference rules and learns to incorporate them in a transformer-based reasoning cell.We assess the model’s performance on two tasks that require different reasoning skills: Abductive Natural Language Inference and Counterfactual Invariance Prediction as a new task. We show that our proposed model improves performance over strong state-of-the-art models (i.e., RoBERTa) across both reasoning tasks. Notably we are, to the best of our knowledge, the first to demonstrate that a model that learns to perform counterfactual reasoning helps predicting the best explanation in an abductive reasoning task. We validate the robustness of the model’s reasoning capabilities by perturbing the knowledge and provide qualitative analysis on the model’s knowledge incorporation capabilities.

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X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset
Angel Daza | Anette Frank
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Even though SRL is researched for many languages, major improvements have mostly been obtained for English, for which more resources are available. In fact, existing multilingual SRL datasets contain disparate annotation styles or come from different domains, hampering generalization in multilingual learning. In this work we propose a method to automatically construct an SRL corpus that is parallel in four languages: English, French, German, Spanish, with unified predicate and role annotations that are fully comparable across languages. We apply high-quality machine translation to the English CoNLL-09 dataset and use multilingual BERT to project its high-quality annotations to the target languages. We include human-validated test sets that we use to measure the projection quality, and show that projection is denser and more precise than a strong baseline. Finally, we train different SOTA models on our novel corpus for mono- and multilingual SRL, showing that the multilingual annotations improve performance especially for the weaker languages.

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AMR Similarity Metrics from Principles
Juri Opitz | Letitia Parcalabescu | Anette Frank
Transactions of the Association for Computational Linguistics, Volume 8

Different metrics have been proposed to compare Abstract Meaning Representation (AMR) graphs. The canonical Smatch metric (Cai and Knight, 2013) aligns the variables of two graphs and assesses triple matches. The recent SemBleu metric (Song and Gildea, 2019) is based on the machine-translation metric Bleu (Papineni et al., 2002) and increases computational efficiency by ablating the variable-alignment. In this paper, i) we establish criteria that enable researchers to perform a principled assessment of metrics comparing meaning representations like AMR; ii) we undertake a thorough analysis of Smatch and SemBleu where we show that the latter exhibits some undesirable properties. For example, it does not conform to the identity of indiscernibles rule and introduces biases that are hard to control; and iii) we propose a novel metric S2 match that is more benevolent to only very slight meaning deviations and targets the fulfilment of all established criteria. We assess its suitability and show its advantages over Smatch and SemBleu.

2019

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Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling
Angel Daza | Anette Frank
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We propose a Cross-lingual Encoder-Decoder model that simultaneously translates and generates sentences with Semantic Role Labeling annotations in a resource-poor target language. Unlike annotation projection techniques, our model does not need parallel data during inference time. Our approach can be applied in monolingual, multilingual and cross-lingual settings and is able to produce dependency-based and span-based SRL annotations. We benchmark the labeling performance of our model in different monolingual and multilingual settings using well-known SRL datasets. We then train our model in a cross-lingual setting to generate new SRL labeled data. Finally, we measure the effectiveness of our method by using the generated data to augment the training basis for resource-poor languages and perform manual evaluation to show that it produces high-quality sentences and assigns accurate semantic role annotations. Our proposed architecture offers a flexible method for leveraging SRL data in multiple languages.

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Discourse-Aware Semantic Self-Attention for Narrative Reading Comprehension
Todor Mihaylov | Anette Frank
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In this work, we propose to use linguistic annotations as a basis for a Discourse-Aware Semantic Self-Attention encoder that we employ for reading comprehension on narrative texts. We extract relations between discourse units, events, and their arguments as well as coreferring mentions, using available annotation tools. Our empirical evaluation shows that the investigated structures improve the overall performance (up to +3.4 Rouge-L), especially intra-sentential and cross-sentential discourse relations, sentence-internal semantic role relations, and long-distance coreference relations. We show that dedicating self-attention heads to intra-sentential relations and relations connecting neighboring sentences is beneficial for finding answers to questions in longer contexts. Our findings encourage the use of discourse-semantic annotations to enhance the generalization capacity of self-attention models for reading comprehension.

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Assessing the Difficulty of Classifying ConceptNet Relations in a Multi-Label Classification Setting
Maria Becker | Michael Staniek | Vivi Nastase | Anette Frank
RELATIONS - Workshop on meaning relations between phrases and sentences

Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in ConceptNet, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types. We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the specific properties of the ConceptNet resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work.

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Dissecting Content and Context in Argumentative Relation Analysis
Juri Opitz | Anette Frank
Proceedings of the 6th Workshop on Argument Mining

When assessing relations between argumentative units (e.g., support or attack), computational systems often exploit disclosing indicators or markers that are not part of elementary argumentative units (EAUs) themselves, but are gained from their context (position in paragraph, preceding tokens, etc.). We show that this dependency is much stronger than previously assumed. In fact, we show that by completely masking the EAU text spans and only feeding information from their context, a competitive system may function even better. We argue that an argument analysis system that relies more on discourse context than the argument’s content is unsafe, since it can easily be tricked. To alleviate this issue, we separate argumentative units from their context such that the system is forced to model and rely on an EAU’s content. We show that the resulting classification system is more robust, and argue that such models are better suited for predicting argumentative relations across documents.

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Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs
Debjit Paul | Anette Frank
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

To make machines better understand sentiments, research needs to move from polarity identification to understanding the reasons that underlie the expression of sentiment. Categorizing the goals or needs of humans is one way to explain the expression of sentiment in text. Humans are good at understanding situations described in natural language and can easily connect them to the character’s psychological needs using commonsense knowledge. We present a novel method to extract, rank, filter and select multi-hop relation paths from a commonsense knowledge resource to interpret the expression of sentiment in terms of their underlying human needs. We efficiently integrate the acquired knowledge paths in a neural model that interfaces context representations with knowledge using a gated attention mechanism. We assess the model’s performance on a recently published dataset for categorizing human needs. Selectively integrating knowledge paths boosts performance and establishes a new state-of-the-art. Our model offers interpretability through the learned attention map over commonsense knowledge paths. Human evaluation highlights the relevance of the encoded knowledge.

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Automatic Accuracy Prediction for AMR Parsing
Juri Opitz | Anette Frank
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Abstract Meaning Representation (AMR) represents sentences as directed, acyclic and rooted graphs, aiming at capturing their meaning in a machine readable format. AMR parsing converts natural language sentences into such graphs. However, evaluating a parser on new data by means of comparison to manually created AMR graphs is very costly. Also, we would like to be able to detect parses of questionable quality, or preferring results of alternative systems by selecting the ones for which we can assess good quality. We propose AMR accuracy prediction as the task of predicting several metrics of correctness for an automatically generated AMR parse – in absence of the corresponding gold parse. We develop a neural end-to-end multi-output regression model and perform three case studies: firstly, we evaluate the model’s capacity of predicting AMR parse accuracies and test whether it can reliably assign high scores to gold parses. Secondly, we perform parse selection based on predicted parse accuracies of candidate parses from alternative systems, with the aim of improving overall results. Finally, we predict system ranks for submissions from two AMR shared tasks on the basis of their predicted parse accuracy averages. All experiments are carried out across two different domains and show that our method is effective.

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An Argument-Marker Model for Syntax-Agnostic Proto-Role Labeling
Juri Opitz | Anette Frank
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Semantic proto-role labeling (SPRL) is an alternative to semantic role labeling (SRL) that moves beyond a categorical definition of roles, following Dowty’s feature-based view of proto-roles. This theory determines agenthood vs. patienthood based on a participant’s instantiation of more or less typical agent vs. patient properties, such as, for example, volition in an event. To perform SPRL, we develop an ensemble of hierarchical models with self-attention and concurrently learned predicate-argument markers. Our method is competitive with the state-of-the art, overall outperforming previous work in two formulations of the task (multi-label and multi-variate Likert scale pre- diction). In contrast to previous work, our results do not depend on gold argument heads derived from supplementary gold tree banks.

2018

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DeModify: A Dataset for Analyzing Contextual Constraints on Modifier Deletion
Vivi Nastase | Devon Fritz | Anette Frank
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling
Ana Marasović | Anette Frank
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question “Who expressed what kind of sentiment towards what?”. Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). We suspect this is due to the scarcity of labeled training data and address this issue using different multi-task learning (MTL) techniques with a related task which has substantially more data, i.e. Semantic Role Labeling (SRL). We show that two MTL models improve significantly over the single-task model for labeling of both holders and targets, on the development and the test sets. We found that the vanilla MTL model, which makes predictions using only shared ORL and SRL features, performs the best. With deeper analysis we determine what works and what might be done to make further improvements for ORL.

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Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge
Todor Mihaylov | Anette Frank
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We introduce a neural reading comprehension model that integrates external commonsense knowledge, encoded as a key-value memory, in a cloze-style setting. Instead of relying only on document-to-question interaction or discrete features as in prior work, our model attends to relevant external knowledge and combines this knowledge with the context representation before inferring the answer. This allows the model to attract and imply knowledge from an external knowledge source that is not explicitly stated in the text, but that is relevant for inferring the answer. Our model improves results over a very strong baseline on a hard Common Nouns dataset, making it a strong competitor of much more complex models. By including knowledge explicitly, our model can also provide evidence about the background knowledge used in the RC process.

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A Sequence-to-Sequence Model for Semantic Role Labeling
Angel Daza | Anette Frank
Proceedings of The Third Workshop on Representation Learning for NLP

We explore a novel approach for Semantic Role Labeling (SRL) by casting it as a sequence-to-sequence process. We employ an attention-based model enriched with a copying mechanism to ensure faithful regeneration of the input sequence, while enabling interleaved generation of argument role labels. We apply this model in a monolingual setting, performing PropBank SRL on English language data. The constrained sequence generation set-up enforced with the copying mechanism allows us to analyze the performance and special properties of the model on manually labeled data and benchmarking against state-of-the-art sequence labeling models. We show that our model is able to solve the SRL argument labeling task on English data, yet further structural decoding constraints will need to be added to make the model truly competitive. Our work represents the first step towards more advanced, generative SRL labeling setups.

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Addressing the Winograd Schema Challenge as a Sequence Ranking Task
Juri Opitz | Anette Frank
Proceedings of the First International Workshop on Language Cognition and Computational Models

The Winograd Schema Challenge targets pronominal anaphora resolution problems which require the application of cognitive inference in combination with world knowledge. These problems are easy to solve for humans but most difficult to solve for machines. Computational models that previously addressed this task rely on syntactic preprocessing and incorporation of external knowledge by manually crafted features. We address the Winograd Schema Challenge from a new perspective as a sequence ranking task, and design a Siamese neural sequence ranking model which performs significantly better than a random baseline, even when solely trained on sequences of words. We evaluate against a baseline and a state-of-the-art system on two data sets and show that anonymization of noun phrase candidates strongly helps our model to generalize.

2017

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Classifying Semantic Clause Types: Modeling Context and Genre Characteristics with Recurrent Neural Networks and Attention
Maria Becker | Michael Staniek | Vivi Nastase | Alexis Palmer | Anette Frank
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

Detecting aspectual properties of clauses in the form of situation entity types has been shown to depend on a combination of syntactic-semantic and contextual features. We explore this task in a deep-learning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. Apart from implicitly capturing task relevant features, the advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable. We present experiments for English and German that achieve competitive performance. We present a novel take on modeling and exploiting genre information and showcase the adaptation of our system from one language to another.

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Assessing SRL Frameworks with Automatic Training Data Expansion
Silvana Hartmann | Éva Mújdricza-Maydt | Ilia Kuznetsov | Iryna Gurevych | Anette Frank
Proceedings of the 11th Linguistic Annotation Workshop

We present the first experiment-based study that explicitly contrasts the three major semantic role labeling frameworks. As a prerequisite, we create a dataset labeled with parallel FrameNet-, PropBank-, and VerbNet-style labels for German. We train a state-of-the-art SRL tool for German for the different annotation styles and provide a comparative analysis across frameworks. We further explore the behavior of the frameworks with automatic training data generation. VerbNet provides larger semantic expressivity than PropBank, and we find that its generalization capacity approaches PropBank in SRL training, but it benefits less from training data expansion than the sparse-data affected FrameNet.

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Story Cloze Ending Selection Baselines and Data Examination
Todor Mihaylov | Anette Frank
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

This paper describes two supervised baseline systems for the Story Cloze Test Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using features based on word embeddings and semantic similarity computation. We further implement a neural LSTM system with different encoding strategies that try to model the relation between the story and the provided endings. Our experiments show that a model using representation features based on average word embedding vectors over the given story words and the candidate ending sentences words, joint with similarity features between the story and candidate ending representations performed better than the neural models. Our best model based on achieves an accuracy of 72.42, ranking 3rd in the official evaluation.

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What do we need to know about an unknown word when parsing German
Bich-Ngoc Do | Ines Rehbein | Anette Frank
Proceedings of the First Workshop on Subword and Character Level Models in NLP

We propose a new type of subword embedding designed to provide more information about unknown compounds, a major source for OOV words in German. We present an extrinsic evaluation where we use the compound embeddings as input to a neural dependency parser and compare the results to the ones obtained with other types of embeddings. Our evaluation shows that adding compound embeddings yields a significant improvement of 2% LAS over using word embeddings when no POS information is available. When adding POS embeddings to the input, however, the effect levels out. This suggests that it is not the missing information about the semantics of the unknown words that causes problems for parsing German, but the lack of morphological information for unknown words. To augment our evaluation, we also test the new embeddings in a language modelling task that requires both syntactic and semantic information.

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Universal Dependencies are Hard to Parse – or are They?
Ines Rehbein | Julius Steen | Bich-Ngoc Do | Anette Frank
Proceedings of the Fourth International Conference on Dependency Linguistics (Depling 2017)

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A Mention-Ranking Model for Abstract Anaphora Resolution
Ana Marasović | Leo Born | Juri Opitz | Anette Frank
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Resolving abstract anaphora is an important, but difficult task for text understanding. Yet, with recent advances in representation learning this task becomes a more tangible aim. A central property of abstract anaphora is that it establishes a relation between the anaphor embedded in the anaphoric sentence and its (typically non-nominal) antecedent. We propose a mention-ranking model that learns how abstract anaphors relate to their antecedents with an LSTM-Siamese Net. We overcome the lack of training data by generating artificial anaphoric sentence–antecedent pairs. Our model outperforms state-of-the-art results on shell noun resolution. We also report first benchmark results on an abstract anaphora subset of the ARRAU corpus. This corpus presents a greater challenge due to a mixture of nominal and pronominal anaphors and a greater range of confounders. We found model variants that outperform the baselines for nominal anaphors, without training on individual anaphor data, but still lag behind for pronominal anaphors. Our model selects syntactically plausible candidates and – if disregarding syntax – discriminates candidates using deeper features.

2016

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Combining Semantic Annotation of Word Sense & Semantic Roles: A Novel Annotation Scheme for VerbNet Roles on German Language Data
Éva Mújdricza-Maydt | Silvana Hartmann | Iryna Gurevych | Anette Frank
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a VerbNet-based annotation scheme for semantic roles that we explore in an annotation study on German language data that combines word sense and semantic role annotation. We reannotate a substantial portion of the SALSA corpus with GermaNet senses and a revised scheme of VerbNet roles. We provide a detailed evaluation of the interaction between sense and role annotation. The resulting corpus will allow us to compare VerbNet role annotation for German to FrameNet and PropBank annotation by mapping to existing role annotations on the SALSA corpus. We publish the annotated corpus and detailed guidelines for the new role annotation scheme.

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Modal Sense Classification At Large: Paraphrase-Driven Sense Projection, Semantically Enriched Classification Models and Cross-Genre Evaluations
Ana Marasović | Mengfei Zhou | Alexis Palmer | Anette Frank
Linguistic Issues in Language Technology, Volume 14, 2016 - Modality: Logic, Semantics, Annotation, and Machine Learning

Modal verbs have different interpretations depending on their context. Their sense categories – epistemic, deontic and dynamic – provide important dimensions of meaning for the interpretation of discourse. Previous work on modal sense classification achieved relatively high performance using shallow lexical and syntactic features drawn from small-size annotated corpora. Due to the restricted empirical basis, it is difficult to assess the particular difficulties of modal sense classification and the generalization capacity of the proposed models. In this work we create large-scale, high-quality annotated corpora for modal sense classification using an automatic paraphrase-driven projection approach. Using the acquired corpora, we investigate the modal sense classification task from different perspectives.

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Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings
Todor Mihaylov | Anette Frank
Proceedings of the CoNLL-16 shared task

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Multilingual Modal Sense Classification using a Convolutional Neural Network
Ana Marasović | Anette Frank
Proceedings of the 1st Workshop on Representation Learning for NLP

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Deriving Players & Themes in the Regesta Imperii using SVMs and Neural Networks
Juri Opitz | Anette Frank
Proceedings of the 10th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities

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Argumentative texts and clause types
Maria Becker | Alexis Palmer | Anette Frank
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

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A Web-based Tool for the Integrated Annotation of Semantic and Syntactic Structures
Richard Eckart de Castilho | Éva Mújdricza-Maydt | Seid Muhie Yimam | Silvana Hartmann | Iryna Gurevych | Anette Frank | Chris Biemann
Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH)

We introduce the third major release of WebAnno, a generic web-based annotation tool for distributed teams. New features in this release focus on semantic annotation tasks (e.g. semantic role labelling or event annotation) and allow the tight integration of semantic annotations with syntactic annotations. In particular, we introduce the concept of slot features, a novel constraint mechanism that allows modelling the interaction between semantic and syntactic annotations, as well as a new annotation user interface. The new features were developed and used in an annotation project for semantic roles on German texts. The paper briefly introduces this project and reports on experiences performing annotations with the new tool. On a comparative evaluation, our tool reaches significant speedups over WebAnno 2 for a semantic annotation task.

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Implicit Semantic Roles in a Multilingual Setting
Jennifer Sikos | Yannick Versley | Anette Frank
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

2015

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Inducing Implicit Arguments from Comparable Texts: A Framework and Its Applications
Michael Roth | Anette Frank
Computational Linguistics, Volume 41, Issue 4 - December 2015

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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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Analyzing Sentiment in Classical Chinese Poetry
Yufang Hou | Anette Frank
Proceedings of the 9th SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH)

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Seed-Based Event Trigger Labeling: How far can event descriptions get us?
Ofer Bronstein | Ido Dagan | Qi Li | Heng Ji | Anette Frank
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)

2014

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Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)
Johan Bos | Anette Frank | Roberto Navigli
Proceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)

2013

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Predicate-specific Annotations for Implicit Role Binding: Corpus Annotation, Data Analysis and Evaluation Experiments
Tatjana Moor | Michael Roth | Anette Frank
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Short Papers

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Automatically Identifying Implicit Arguments to Improve Argument Linking and Coherence Modeling
Michael Roth | Anette Frank
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2012

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Casting Implicit Role Linking as an Anaphora Resolution Task
Carina Silberer | Anette Frank
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Aligning Predicate Argument Structures in Monolingual Comparable Texts: A New Corpus for a New Task
Michael Roth | Anette Frank
*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012)

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Aligning Predicates across Monolingual Comparable Texts using Graph-based Clustering
Michael Roth | Anette Frank
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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Exploring Supervised LDA Models for Assigning Attributes to Adjective-Noun Phrases
Matthias Hartung | Anette Frank
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Assessing Interpretable, Attribute-related Meaning Representations for Adjective-Noun Phrases in a Similarity Prediction Task
Matthias Hartung | Anette Frank
Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics

2010

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Assessing the Challenge of Fine-Grained Named Entity Recognition and Classification
Asif Ekbal | Eva Sourjikova | Anette Frank | Simone Paolo Ponzetto
Proceedings of the 2010 Named Entities Workshop

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A Structured Vector Space Model for Hidden Attribute Meaning in Adjective-Noun Phrases
Matthias Hartung | Anette Frank
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Computing EM-based Alignments of Routes and Route Directions as a Basis for Natural Language Generation
Michael Roth | Anette Frank
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Identifying Generic Noun Phrases
Nils Reiter | Anette Frank
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

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Using NLP Methods for the Analysis of Rituals
Nils Reiter | Oliver Hellwig | Anand Mishra | Anette Frank | Jens Burkhardt
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper gives an overview of an interdisciplinary research project that is concerned with the application of computational linguistics methods to the analysis of the structure and variance of rituals, as investigated in ritual science. We present motivation and prospects of a computational approach to ritual research, and explain the choice of specific analysis techniques. We discuss design decisions for data collection and processing and present the general NLP architecture. For the analysis of ritual descriptions, we apply the frame semantics paradigm with newly invented frames where appropriate. Using scientific ritual research literature, we experimented with several techniques of automatic extraction of domain terms for the domain of rituals. As ritual research is a highly interdisciplinary endeavour, a vocabulary common to all sub-areas of ritual research can is hard to specify and highly controversial. The domain terms extracted from ritual research literature are used as a basis for a common vocabulary and thus help the creation of ritual specific frames. We applied the tf*idf, 2 and PageRank algorithm to our ritual research literature corpus and two non-domain corpora: The British National Corpus and the British Academic Written English corpus. All corpora have been part of speech tagged and lemmatized. The domain terms have been evaluated by two ritual experts independently. Interestingly, the results of the algorithms were different for different parts of speech. This finding is in line with the fact that the inter-annotator agreement also differs between parts of speech.

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A Semi-supervised Type-based Classification of Adjectives: Distinguishing Properties and Relations
Matthias Hartung | Anette Frank
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

We present a semi-supervised machine-learning approach for the classification of adjectives into property- vs. relation-denoting adjectives, a distinction that is highly relevant for ontology learning. The feasibility of this classification task is evaluated in a human annotation experiment. We observe that token-level annotation of these classes is expensive and difficult. Yet, a careful corpus analysis reveals that adjective classes tend to be stable, with few occurrences of class shifts observed at the token level. As a consequence, we opt for a type-based semi-supervised classification approach. The class labels obtained from manual annotation are projected to large amounts of unannotated token samples. Training on heuristically labeled data yields high classification performance on our own data and on a data set compiled from WordNet. Our results suggest that it is feasible to automatically distinguish adjectives denoting properties and relations, using small amounts of annotated data.

2009

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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)

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A NLG-based Application for Walking Directions
Michael Roth | Anette Frank
Proceedings of the ACL-IJCNLP 2009 Software Demonstrations

2008

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Formalising Multi-layer Corpora in OWL DL - Lexicon Modelling, Querying and Consistency Control
Aljoscha Burchardt | Sebastian Padó | Dennis Spohr | Anette Frank | Ulrich Heid
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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Projection-based Acquisition of a Temporal Labeller
Kathrin Spreyer | Anette Frank
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

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A Resource-Poor Approach for Linking Ontology Classes to Wikipedia Articles
Nils Reiter | Matthias Hartung | Anette Frank
Semantics in Text Processing. STEP 2008 Conference Proceedings

2007

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A Semantic Approach To Textual Entailment: System Evaluation and Task Analysis
Aljoscha Burchardt | Nils Reiter | Stefan Thater | Anette Frank
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

2006

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The SALSA Corpus: a German Corpus Resource for Lexical Semantics
Aljoscha Burchardt | Katrin Erk | Anette Frank | Andrea Kowalski | Sebastian Padó | Manfred Pinkal
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

This paper describes the SALSA corpus, a large German corpus manually annotated with manual role-semantic annotation, based on the syntactically annotated TIGER newspaper corpus. The first release, comprising about 20,000 annotated predicate instances (about half the TIGER corpus), is scheduled for mid-2006. In this paper we discuss the annotation framework (frame semantics) and its cross-lingual applicability, problems arising from exhaustive annotation, strategies for quality control, and possible applications.

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SALTO - A Versatile Multi-Level Annotation Tool
Aljoscha Burchardt | Katrin Erk | Anette Frank | Andrea Kowalski | Sebastian Pado
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In this paper, we describe the SALTO tool. It was originally developed for the annotation of semantic roles in the frame semantics paradigm, but can be used for graphical annotation of treebanks with general relational information in a simple drag-and-drop fashion. The tool additionally supports corpus management and quality control.

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Contextual phenomena and thematic relations in database QA dialogues: results from a Wizard-of-Oz Experiment
Núria Bertomeu | Hans Uszkoreit | Anette Frank | Hans-Ulrich Krieger | Brigitte Jörg
Proceedings of the Interactive Question Answering Workshop at HLT-NAACL 2006

2005

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The TIGER 700 RMRS Bank: RMRS Construction from Dependencies
Kathrin Spreyer | Anette Frank
Proceedings of the Sixth International Workshop on Linguistically Interpreted Corpora (LINC-2005)

2004

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Constraint-based RMRS Construction from Shallow Grammars
Anette Frank
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Corpus-based Induction of an LFG Syntax-Semantics Interface for Frame Semantic Processing
Anette Frank | Jirí Semecky
Proceedings of the 5th International Workshop on Linguistically Interpreted Corpora

2003

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Integrated Shallow and Deep Parsing: TopP Meets HPSG
Anette Frank | Markus Becker | Berthold Crysmann | Bernd Kiefer | Ulrich Schäfer
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

2002

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An Integrated Archictecture for Shallow and Deep Processing
Berthold Crysmann | Anette Frank | Bernd Kiefer | Stefan Mueller | Guenter Neumann | Jakub Piskorski | Ulrich Schaefer | Melanie Siegel | Hans Uszkoreit | Feiyu Xu | Markus Becker | Hans-Ulrich Krieger
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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A Stochastic Topological Parser for German
Markus Becker | Anette Frank
COLING 2002: The 19th International Conference on Computational Linguistics

1998

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Syntactic and Semantic Transfer with F-Structures
Michael Dorna | Anette Frank | Josef van Genabith | Martin C. Emele
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Syntactic and Semantic Transfer with F-Structures
Michael Dorna | Anette Frank | Josef van Genabith | Martin C. Emele
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

1995

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Principle Based Semantics for HPSG
Anette Frank | Uwe Reyle
Seventh Conference of the European Chapter of the Association for Computational Linguistics