Laura Kallmeyer


2021

pdf bib
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)
Kilian Evang | Laura Kallmeyer | Rainer Osswald | Jakub Waszczuk | Torsten Zesch
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

pdf bib
Combining text and vision in compound semantics: Towards a cognitively plausible multimodal model
Abhijeet Gupta | Fritz Günther | Ingo Plag | Laura Kallmeyer | Stefan Conrad
Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)

2020

pdf bib
Corpus-based Identification of Verbs Participating in Verb Alternations Using Classification and Manual Annotation
Esther Seyffarth | Laura Kallmeyer
Proceedings of the 28th International Conference on Computational Linguistics

English verb alternations allow participating verbs to appear in a set of syntactically different constructions whose associated semantic frames are systematically related. We use ENCOW and VerbNet data to train classifiers to predict the instrument subject alternation and the causative-inchoative alternation, relying on count-based and vector-based features as well as perplexity-based language model features, which are intended to reflect each alternation’s felicity by simulating it. Beyond the prediction task, we use the classifier results as a source for a manual annotation step in order to identify new, unseen instances of each alternation. This is possible because existing alternation datasets contain positive, but no negative instances and are not comprehensive. Over several sequences of classification-annotation steps, we iteratively extend our sets of alternating verbs. Our hybrid approach to the identification of new alternating verbs reduces the required annotation effort by only presenting annotators with the highest-scoring candidates from the previous classification. Due to the success of semi-supervised and unsupervised features, our approach can easily be transferred to further alternations.

pdf bib
Statistical Parsing of Tree Wrapping Grammars
Tatiana Bladier | Jakub Waszczuk | Laura Kallmeyer
Proceedings of the 28th International Conference on Computational Linguistics

We describe an approach to statistical parsing with Tree-Wrapping Grammars (TWG). TWG is a tree-rewriting formalism which includes the tree-combination operations of substitution, sister-adjunction and tree-wrapping substitution. TWGs can be extracted from constituency treebanks and aim at representing long distance dependencies (LDDs) in a linguistically adequate way. We present a parsing algorithm for TWGs based on neural supertagging and A* parsing. We extract a TWG for English from the treebanks for Role and Reference Grammar and discuss first parsing results with this grammar.

pdf bib
Do you Feel Certain about your Annotation? A Web-based Semantic Frame Annotation Tool Considering Annotators’ Concerns and Behaviors
Regina Stodden | Behrang QasemiZadeh | Laura Kallmeyer
Proceedings of the 12th Language Resources and Evaluation Conference

In this system demonstration paper, we present an open-source web-based application with a responsive design for modular semantic frame annotation (SFA). Besides letting experienced and inexperienced users do suggestion-based and slightly-controlled annotations, the system keeps track of the time and changes during the annotation process and stores the users’ confidence with the current annotation. This collected metadata can be used to get insights regarding the difficulty of an annotation with the same type or frame or can be used as an input of an annotation cost measurement for an active learning algorithm. The tool was already used to build a manually annotated corpus with semantic frames and its arguments for task 2 of SemEval 2019 regarding unsupervised lexical frame induction (QasemiZadeh et al., 2019). Although English sentences from the Wall Street Journal corpus of the Penn Treebank were annotated for this task, it is also possible to use the proposed tool for the annotation of sentences in other languages.

pdf bib
Supervised Disambiguation of German Verbal Idioms with a BiLSTM Architecture
Rafael Ehren | Timm Lichte | Laura Kallmeyer | Jakub Waszczuk
Proceedings of the Second Workshop on Figurative Language Processing

Supervised disambiguation of verbal idioms (VID) poses special demands on the quality and quantity of the annotated data used for learning and evaluation. In this paper, we present a new VID corpus for German and perform a series of VID disambiguation experiments on it. Our best classifier, based on a neural architecture, yields an error reduction across VIDs of 57% in terms of accuracy compared to a simple majority baseline.

pdf bib
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories
Kilian Evang | Laura Kallmeyer | Rafael Ehren | Simon Petitjean | Esther Seyffarth | Djamé Seddah
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories

pdf bib
Automatic Extraction of Tree-Wrapping Grammars for Multiple Languages
Tatiana Bladier | Laura Kallmeyer | Rainer Osswald | Jakub Waszczuk
Proceedings of the 19th International Workshop on Treebanks and Linguistic Theories

pdf bib
A multi-lingual and cross-domain analysis of features for text simplification
Regina Stodden | Laura Kallmeyer
Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI)

In text simplification and readability research, several features have been proposed to estimate or simplify a complex text, e.g., readability scores, sentence length, or proportion of POS tags. These features are however mainly developed for English. In this paper, we investigate their relevance for Czech, German, English, Spanish, and Italian text simplification corpora. Our multi-lingual and multi-domain corpus analysis shows that the relevance of different features for text simplification is different per corpora, language, and domain. For example, the relevance of the lexical complexity is different across all languages, the BLEU score across all domains, and 14 features within the web domain corpora. Overall, the negative statistical tests regarding the other features across and within domains and languages lead to the assumption that text simplification models may be transferable between different domains or different languages.

2019

pdf bib
Towards a Compositional Analysis of German Light Verb Constructions (LVCs) Combining Lexicalized Tree Adjoining Grammar (LTAG) with Frame Semantics
Jens Fleischhauer | Thomas Gamerschlag | Laura Kallmeyer | Simon Petitjean
Proceedings of the 13th International Conference on Computational Semantics - Long Papers

Complex predicates formed of a semantically ‘light’ verbal head and a noun or verb which contributes the major part of the meaning are frequently referred to as ‘light verb constructions’ (LVCs). In the paper, we present a case study of LVCs with the German posture verb stehen ‘stand’. In our account, we model the syntactic as well as semantic composition of such LVCs by combining Lexicalized Tree Adjoining Grammar (LTAG) with frames. Starting from the analysis of the literal uses of posture verbs, we show how the meaning components of the literal uses are systematically exploited in the interpretation of stehen-LVCs. The paper constitutes an important step towards a compositional and computational analysis of LVCs. We show that LTAG allows us to separate constructional from lexical meaning components and that frames enable elegant generalizations over event types and related constraints.

pdf bib
A Neural Graph-based Approach to Verbal MWE Identification
Jakub Waszczuk | Rafael Ehren | Regina Stodden | Laura Kallmeyer
Proceedings of the Joint Workshop on Multiword Expressions and WordNet (MWE-WN 2019)

We propose to tackle the problem of verbal multiword expression (VMWE) identification using a neural graph parsing-based approach. Our solution involves encoding VMWE annotations as labellings of dependency trees and, subsequently, applying a neural network to model the probabilities of different labellings. This strategy can be particularly effective when applied to discontinuous VMWEs and, thanks to dense, pre-trained word vector representations, VMWEs unseen during training. Evaluation of our approach on three PARSEME datasets (German, French, and Polish) shows that it allows to achieve performance on par with the previous state-of-the-art (Al Saied et al., 2018).

pdf bib
SemEval-2019 Task 2: Unsupervised Lexical Frame Induction
Behrang QasemiZadeh | Miriam R. L. Petruck | Regina Stodden | Laura Kallmeyer | Marie Candito
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper presents Unsupervised Lexical Frame Induction, Task 2 of the International Workshop on Semantic Evaluation in 2019. Given a set of prespecified syntactic forms in context, the task requires that verbs and their arguments be clustered to resemble semantic frame structures. Results are useful in identifying polysemous words, i.e., those whose frame structures are not easily distinguished, as well as discerning semantic relations of the arguments. Evaluation of unsupervised frame induction methods fell into two tracks: Task A) Verb Clustering based on FrameNet 1.7; and B) Argument Clustering, with B.1) based on FrameNet’s core frame elements, and B.2) on VerbNet 3.2 semantic roles. The shared task attracted nine teams, of whom three reported promising results. This paper describes the task and its data, reports on methods and resources that these systems used, and offers a comparison to human annotation.

2018

pdf bib
Multi-Dialect Arabic POS Tagging: A CRF Approach
Kareem Darwish | Hamdy Mubarak | Ahmed Abdelali | Mohamed Eldesouki | Younes Samih | Randah Alharbi | Mohammed Attia | Walid Magdy | Laura Kallmeyer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks
Mohammed Attia | Younes Samih | Ali Elkahky | Laura Kallmeyer
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

pdf bib
German and French Neural Supertagging Experiments for LTAG Parsing
Tatiana Bladier | Andreas van Cranenburgh | Younes Samih | Laura Kallmeyer
Proceedings of ACL 2018, Student Research Workshop

We present ongoing work on data-driven parsing of German and French with Lexicalized Tree Adjoining Grammars. We use a supertagging approach combined with deep learning. We show the challenges of extracting LTAG supertags from the French Treebank, introduce the use of left- and right-sister-adjunction, present a neural architecture for the supertagger, and report experiments of n-best supertagging for French and German.

pdf bib
TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions
Regina Stodden | Behrang QasemiZadeh | Laura Kallmeyer
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

We describe the TRAPACC system and its variant TRAPACCS that participated in the closed track of the PARSEME Shared Task 2018 on labeling verbal multiword expressions (VMWEs). TRAPACC is a modified arc-standard transition system based on Constant and Nivre’s (2016) model of joint syntactic and lexical analysis in which the oracle is approximated using a classifier. For TRAPACC, the classifier consists of a data-independent dimension reduction and a convolutional neural network (CNN) for learning and labelling transitions. TRAPACCS extends TRAPACC by replacing the softmax layer of the CNN with a support vector machine (SVM). We report the results obtained for 19 languages, for 8 of which our system yields the best results compared to other participating systems in the closed-track of the shared task.

pdf bib
Coarse Lexical Frame Acquisition at the Syntax–Semantics Interface Using a Latent-Variable PCFG Model
Laura Kallmeyer | Behrang QasemiZadeh | Jackie Chi Kit Cheung
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

We present a method for unsupervised lexical frame acquisition at the syntax–semantics interface. Given a set of input strings derived from dependency parses, our method generates a set of clusters that resemble lexical frame structures. Our work is motivated not only by its practical applications (e.g., to build, or expand the coverage of lexical frame databases), but also to gain linguistic insight into frame structures with respect to lexical distributions in relation to grammatical structures. We model our task using a hierarchical Bayesian network and employ tools and methods from latent variable probabilistic context free grammars (L-PCFGs) for statistical inference and parameter fitting, for which we propose a new split and merge procedure. We show that our model outperforms several baselines on a portion of the Wall Street Journal sentences that we have newly annotated for evaluation purposes.

2017

pdf bib
A Neural Architecture for Dialectal Arabic Segmentation
Younes Samih | Mohammed Attia | Mohamed Eldesouki | Ahmed Abdelali | Hamdy Mubarak | Laura Kallmeyer | Kareem Darwish
Proceedings of the Third Arabic Natural Language Processing Workshop

The automated processing of Arabic Dialects is challenging due to the lack of spelling standards and to the scarcity of annotated data and resources in general. Segmentation of words into its constituent parts is an important processing building block. In this paper, we show how a segmenter can be trained using only 350 annotated tweets using neural networks without any normalization or use of lexical features or lexical resources. We deal with segmentation as a sequence labeling problem at the character level. We show experimentally that our model can rival state-of-the-art methods that rely on additional resources.

pdf bib
Depictives in English: An LTAG Approach
Benjamin Burkhardt | Timm Lichte | Laura Kallmeyer
Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms

pdf bib
Combining Predicate-Argument Structure and Operator Projection: Clause Structure in Role and Reference Grammar
Laura Kallmeyer | Rainer Osswald
Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms

pdf bib
Modeling Quantification with Polysemous Nouns
Laura Kallmeyer | Rainer Osswald
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

pdf bib
HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment
Behrang QasemiZadeh | Laura Kallmeyer
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity. We introduce our unsupervised embedding learning technique and describe how it was employed and configured to address the problems of monolingual and multilingual word similarity measurement. This paper reports from empirical evaluations on the benchmark provided by the task’s organizers.

pdf bib
Learning from Relatives: Unified Dialectal Arabic Segmentation
Younes Samih | Mohamed Eldesouki | Mohammed Attia | Kareem Darwish | Ahmed Abdelali | Hamdy Mubarak | Laura Kallmeyer
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Arabic dialects do not just share a common koiné, but there are shared pan-dialectal linguistic phenomena that allow computational models for dialects to learn from each other. In this paper we build a unified segmentation model where the training data for different dialects are combined and a single model is trained. The model yields higher accuracies than dialect-specific models, eliminating the need for dialect identification before segmentation. We also measure the degree of relatedness between four major Arabic dialects by testing how a segmentation model trained on one dialect performs on the other dialects. We found that linguistic relatedness is contingent with geographical proximity. In our experiments we use SVM-based ranking and bi-LSTM-CRF sequence labeling.

pdf bib
Projection Aléatoire Non-Négative pour le Calcul de Word Embedding / Non-Negative Randomized Word Embedding
Behrang Qasemizadeh | Laura Kallmeyer | Aurelie Herbelot
Actes des 24ème Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 - Articles longs

Non-Negative Randomized Word Embedding We propose a word embedding method which is based on a novel random projection technique. We show that weighting methods such as positive pointwise mutual information (PPMI) can be applied to our models after their construction and at a reduced dimensionality. Hence, the proposed technique can efficiently transfer words onto semantically discriminative spaces while demonstrating high computational performance, besides benefits such as ease of update and a simple mechanism for interoperability. We report the performance of our method on several tasks and show that it yields competitive results compared to neural embedding methods in monolingual corpus-based setups.

2016

pdf bib
Argument linking in LTAG: A constraint-based implementation with XMG
Laura Kallmeyer | Timm Lichte | Rainer Osswald | Simon Petitjean
Proceedings of the 12th International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+12)

pdf bib
CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings
Mohammed Attia | Suraj Maharjan | Younes Samih | Laura Kallmeyer | Thamar Solorio
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

This paper describes our system submission to the CogALex-2016 Shared Task on Corpus-Based Identification of Semantic Relations. Our system won first place for Task-1 and second place for Task-2. The evaluation results of our system on the test set is 88.1% (79.0% for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76.0% (42.3% when excluding RANDOM) for Task-2 on identifying finer-grained semantic relations. In our experiments, we try word analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task-1), while CNNs have better performance in the multi-class semantic classification (Task-2). We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one-to-many and many-to-many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.

pdf bib
Multilingual Code-switching Identification via LSTM Recurrent Neural Networks
Younes Samih | Suraj Maharjan | Mohammed Attia | Laura Kallmeyer | Thamar Solorio
Proceedings of the Second Workshop on Computational Approaches to Code Switching

pdf bib
SAWT: Sequence Annotation Web Tool
Younes Samih | Wolfgang Maier | Laura Kallmeyer
Proceedings of the Second Workshop on Computational Approaches to Code Switching

pdf bib
Random Positive-Only Projections: PPMI-Enabled Incremental Semantic Space Construction
Behrang QasemiZadeh | Laura Kallmeyer
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

2015

pdf bib
LR Parsing for LCFRS
Laura Kallmeyer | Wolfgang Maier
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2013

pdf bib
Data-Driven Parsing using Probabilistic Linear Context-Free Rewriting Systems
Laura Kallmeyer | Wolfgang Maier
Computational Linguistics, Volume 39, Issue 1 - March 2013

2012

pdf bib
A Frame-Based Semantics of Locative Alternation in LTAG
Yulia Zinova | Laura Kallmeyer
Proceedings of the 11th International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+11)

pdf bib
A Formal Model for Plausible Dependencies in Lexicalized Tree Adjoining Grammar
Laura Kallmeyer | Marco Kuhlmann
Proceedings of the 11th International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+11)

pdf bib
PLCFRS Parsing Revisited: Restricting the Fan-Out to Two
Wolfgang Maier | Miriam Kaeshammer | Laura Kallmeyer
Proceedings of the 11th International Workshop on Tree Adjoining Grammars and Related Formalisms (TAG+11)

2011

pdf bib
PLCFRS Parsing of English Discontinuous Constituents
Kilian Evang | Laura Kallmeyer
Proceedings of the 12th International Conference on Parsing Technologies

2010

pdf bib
Gapping through TAG derivation trees
Timm Lichte | Laura Kallmeyer
Proceedings of the 10th International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+10)

pdf bib
Discontinuity and Non-Projectivity: Using Mildly Context-Sensitive Formalisms for Data-Driven Parsing
Wolfgang Maier | Laura Kallmeyer
Proceedings of the 10th International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+10)

pdf bib
Data-Driven Parsing with Probabilistic Linear Context-Free Rewriting Systems
Laura Kallmeyer | Wolfgang Maier
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

pdf bib
A Polynomial-Time Parsing Algorithm for TT-MCTAG
Laura Kallmeyer | Giorgio Satta
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

pdf bib
An Earley Parsing Algorithm for Range Concatenation Grammars
Laura Kallmeyer | Wolfgang Maier | Yannick Parmentier
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers

pdf bib
Un Algorithme d’Analyse de Type Earley pour Grammaires à Concaténation d’Intervalles
Laura Kallmeyer | Wolfgang Maier | Yannick Parmentier
Actes de la 16ème conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

Nous présentons ici différents algorithmes d’analyse pour grammaires à concaténation d’intervalles (Range Concatenation Grammar, RCG), dont un nouvel algorithme de type Earley, dans le paradigme de l’analyse déductive. Notre travail est motivé par l’intérêt porté récemment à ce type de grammaire, et comble un manque dans la littérature existante.

pdf bib
An Incremental Earley Parser for Simple Range Concatenation Grammar
Laura Kallmeyer | Wolfgang Maier
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

pdf bib
Synchronous Rewriting in Treebanks
Laura Kallmeyer | Wolfgang Maier | Giorgio Satta
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

2008

pdf bib
TuLiPA: Towards a Multi-Formalism Parsing Environment for Grammar Engineering
Laura Kallmeyer | Timm Lichte | Wolfgang Maier | Yannick Parmentier | Johannes Dellert | Kilian Evang
Coling 2008: Proceedings of the workshop on Grammar Engineering Across Frameworks

pdf bib
Factorizing Complementation in a TT-MCTAG for German
Timm Lichte | Laura Kallmeyer
Proceedings of the Ninth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+9)

pdf bib
TuLiPA: A syntax-semantics parsing environment for mildly context-sensitive formalisms
Yannick Parmentier | Laura Kallmeyer | Wolfgang Maier | Timm Lichte | Johannes Dellert
Proceedings of the Ninth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+9)

pdf bib
Developing a TT-MCTAG for German with an RCG-based Parser
Laura Kallmeyer | Timm Lichte | Wolfgang Maier | Yannick Parmentier | Johannes Dellert
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Developing linguistic resources, in particular grammars, is known to be a complex task in itself, because of (amongst others) redundancy and consistency issues. Furthermore some languages can reveal themselves hard to describe because of specific characteristics, e.g. the free word order in German. In this context, we present (i) a framework allowing to describe tree-based grammars, and (ii) an actual fragment of a core multicomponent tree-adjoining grammar with tree tuples (TT-MCTAG) for German developed using this framework. This framework combines a metagrammar compiler and a parser based on range concatenation grammar (RCG) to respectively check the consistency and the correction of the grammar. The German grammar being developed within this framework already deals with a wide range of scrambling and extraction phenomena.

pdf bib
Convertir des grammaires d’arbres adjoints à composantes multiples avec tuples d’arbres (TT-MCTAG) en grammaires à concaténation d’intervalles (RCG)
Laura Kallmeyer | Yannick Parmentier
Actes de la 15ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Cet article étudie la relation entre les grammaires d’arbres adjoints à composantes multiples avec tuples d’arbres (TT-MCTAG), un formalisme utilisé en linguistique informatique, et les grammaires à concaténation d’intervalles (RCG). Les RCGs sont connues pour décrire exactement la classe PTIME, il a en outre été démontré que les RCGs « simples » sont même équivalentes aux systèmes de réécriture hors-contextes linéaires (LCFRS), en d’autres termes, elles sont légèrement sensibles au contexte. TT-MCTAG a été proposé pour modéliser les langages à ordre des mots libre. En général ces langages sont NP-complets. Dans cet article, nous définissons une contrainte additionnelle sur les dérivations autorisées par le formalisme TT-MCTAG. Nous montrons ensuite comment cette forme restreinte de TT-MCTAG peut être convertie en une RCG simple équivalente. Le résultat est intéressant pour des raisons théoriques (puisqu’il montre que la forme restreinte de TT-MCTAG est légèrement sensible au contexte), mais également pour des raisons pratiques (la transformation proposée ici a été utilisée pour implanter un analyseur pour TT-MCTAG).

2006

pdf bib
Proceedings of the Eighth International Workshop on Tree Adjoining Grammar and Related Formalisms
Tilman Becker | Laura Kallmeyer
Proceedings of the Eighth International Workshop on Tree Adjoining Grammar and Related Formalisms

pdf bib
Quantifier Scope in German: An MCTAG Analysis
Laura Kallmeyer | Maribel Romero
Proceedings of the Eighth International Workshop on Tree Adjoining Grammar and Related Formalisms

pdf bib
Licensing German Negative Polarity Items in LTAG
Timm Lichte | Laura Kallmeyer
Proceedings of the Eighth International Workshop on Tree Adjoining Grammar and Related Formalisms

pdf bib
Constraint-Based Computational Semantics: A Comparison between LTAG and LRS
Laura Kallmeyer | Frank Richter
Proceedings of the Eighth International Workshop on Tree Adjoining Grammar and Related Formalisms

2005

pdf bib
Tree-Local Multicomponent Tree-Adjoining Grammars with Shared Nodes
Laura Kallmeyer
Computational Linguistics, Volume 31, Number 2, June 2005

pdf bib
A Descriptive Characterization of Multicomponent Tree Adjoining Grammars
Laura Kallmeyer
Actes de la 12ème conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

Multicomponent Tree Adjoining Grammars (MCTAG) is a formalism that has been shown to be useful for many natural language applications. The definition of MCTAG however is problematic since it refers to the process of the derivation itself: a simultaneity constraint must be respected concerning the way the members of the elementary tree sets are added. Looking only at the result of a derivation (i.e., the derived tree and the derivation tree), this simultaneity is no longer visible and therefore cannot be checked. I.e., this way of characterizing MCTAG does not allow to abstract away from the concrete order of derivation. Therefore, in this paper, we propose an alternative definition of MCTAG that characterizes the trees in the tree language of an MCTAG via the properties of the derivation trees the MCTAG licences.

2004

pdf bib
LTAG Analysis for Pied-Piping and Stranding of wh-Phrases
Laura Kallmeyer | Tatjana Scheffler
Proceedings of the 7th International Workshop on Tree Adjoining Grammar and Related Formalisms

pdf bib
Tree-local MCTAG with Shared Nodes: Word Order Variation in German and Korean
Laura Kallmeyer | SinWon Yoon
Proceedings of the 7th International Workshop on Tree Adjoining Grammar and Related Formalisms

pdf bib
LTAG Semantics with Semantic Unification
Laura Kallmeyer | Maribel Romero
Proceedings of the 7th International Workshop on Tree Adjoining Grammar and Related Formalisms

pdf bib
LTAG Semantics for Questions
Maribel Romero | Laura Kallmeyer | Olga Babko-Malaya
Proceedings of the 7th International Workshop on Tree Adjoining Grammar and Related Formalisms

pdf bib
Tree-local MCTAG with Shared Nodes: An Analysis ofWord Order Variation in German and Korean
Laura Kallmeyer | SinWon Yoon
Actes de la 11ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

Tree Adjoining Grammars (TAG) are known not to be powerful enough to deal with scrambling in free word order languages. The TAG-variants proposed so far in order to account for scrambling are not entirely satisfying. Therefore, an alternative extension of TAG is introduced based on the notion of node sharing. Considering data from German and Korean, it is shown that this TAG-extension can adequately analyse scrambling data, also in combination with extraposition and topicalization.

2003

pdf bib
Semantic construction in F-TAG
Claire Gardent | Laura Kallmeyer
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

pdf bib
VIQTORYA – A Visual Query Tool for Syntactically Annotated Corpora
Ilona Steiner | Laura Kallmeyer
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

pdf bib
Using an Enriched TAG Derivation Structure as Basis for Semantics
Laura Kallmeyer
Proceedings of the Sixth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+6)

2000

pdf bib
A Query Tool for Syntactically Frame Acquisition
Laura Kallmeyer
2000 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora

pdf bib
Scrambling in German and the non-locality of local TDGs
Laura Kallmeyer
Proceedings of the Fifth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+5)

1998

pdf bib
A hierarchy of local TDGs
Laura Kallmeyer
Proceedings of the Fourth International Workshop on Tree Adjoining Grammars and Related Frameworks (TAG+4)