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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.