ymoslem/acl-abstracts huggingface dataset:
Marking Irony Activators in a Universal Dependencies Treebank: The Case of an Italian Twitter Corpus (2020)

The recognition of irony is a challenging task in the domain of Sentiment Analysis, and the availability of annotated corpora may be crucial for its automatic processing. In this paper we describe a fine-grained annotation scheme centered on irony, in which we highlight the tokens that are responsible for its activation, (irony activators) and their morpho-syntactic features. As our case study we therefore introduce a recently released Universal Dependencies treebank for Italian which includes ironic tweets: TWITTIR{\`O}-UD. For the purposes of this study, we enriched the existing annotation in the treebank, with a further level that includes irony activators. A description and discussion of the annotation scheme is provided with a definition of irony activators and the guidelines for their annotation. This qualitative study on the different layers of annotation applied on the same dataset can shed some light on the process of human annotation, and irony annotation in particular, and on the usefulness of this representation for developing computational models of irony to be used for training purposes.
HAHA 2019 Dataset: A Corpus for Humor Analysis in Spanish (2020)

This paper presents the development of a corpus of 30,000 Spanish tweets that were crowd-annotated with humor value and funniness score. The corpus contains approximately 38.6{\%} of humorous tweets with an average score of 2.04 in a scale from 1 to 5 for the humorous tweets. The corpus has been used in an automatic humor recognition and analysis competition, obtaining encouraging results from the participants.
Offensive Language Identification in Greek (2020)

As offensive language has become a rising issue for online communities and social media platforms, researchers have been investigating ways of coping with abusive content and developing systems to detect its different types: cyberbullying, hate speech, aggression, etc. With a few notable exceptions, most research on this topic so far has dealt with English. This is mostly due to the availability of language resources for English. To address this shortcoming, this paper presents the first Greek annotated dataset for offensive language identification: the Offensive Greek Tweet Dataset (OGTD). OGTD is a manually annotated dataset containing 4,779 posts from Twitter annotated as offensive and not offensive. Along with a detailed description of the dataset, we evaluate several computational models trained and tested on this data.
Syntax and Semantics in a Treebank for Esperanto (2020)

In this paper we describe and evaluate syntactic and semantic aspects of Arbobanko, a treebank for the artificial language Esperanto, as well as tools and methods used in the production of the treebank. In addition to classical morphosyntax and dependency structure, the treebank was enriched with a lexical-semantic layer covering named entities, a semantic type ontology for nouns and adjectives and a framenet-inspired semantic classification of verbs. For an under-resourced language, the quality of automatic syntactic and semantic pre-annotation is of obvious importance, and by evaluating the underlying parser and the coverage of its semantic ontologies, we try to answer the question whether the language`s extremely regular morphology and transparent semantic affixes translate into a more regular syntax and higher parsing accuracy. On the linguistic side, the treebank allows us to address and quantify typological issues such as the question of word order, auxiliary constructions, lexical transparency and semantic type ambiguity in Esperanto.
Implementation and Evaluation of an LFG-based Parser for Wolof (2020)

This paper reports on a parsing system for Wolof based on the LFG formalism. The parser covers core constructions of Wolof, including noun classes, cleft, copula, causative and applicative sentences. It also deals with several types of coordination, including same constituent coordination, asymmetric and asyndetic coordination. The system uses a cascade of finite-state transducers for word tokenization and morphological analysis as well as various lexicons. In addition, robust parsing techniques, including fragmenting and skimming, are used to optimize grammar coverage. Parsing coverage is evaluated by running test-suites of naturally occurring Wolof sentences through the parser. The evaluation of parsing coverage reveals that 72.72{\%} of the test sentences receive full parses; 27.27{\%} receive partial parses. To measure accuracy, the parsed sentences are disambiguated manually using an incremental parsebanking approach based on discriminants. The evaluation of parsing quality reveals that the parser achieves 67.2{\%} recall, 92.8{\%} precision and an f-score of 77.9{\%}.
The Treebank of Vedic Sanskrit (2020)

This paper introduces the first treebank of Vedic Sanskrit, a morphologically rich ancient Indian language that is of central importance for linguistic and historical research. The selection of the more than 3,700 sentences contained in this treebank reflects the development of metrical and prose texts over a period of 600 years. We discuss how these sentences are annotated in the Universal Dependencies scheme and which syntactic constructions required special attention. In addition, we describe a syntactic labeler based on neural networks that supports the initial annotation of the treebank, and whose evaluation can be helpful for setting up a full syntactic parser of Vedic Sanskrit.
Inherent Dependency Displacement Bias of Transition-Based Algorithms (2020)

A wide variety of transition-based algorithms are currently used for dependency parsers. Empirical studies have shown that performance varies across different treebanks in such a way that one algorithm outperforms another on one treebank and the reverse is true for a different treebank. There is often no discernible reason for what causes one algorithm to be more suitable for a certain treebank and less so for another. In this paper we shed some light on this by introducing the concept of an algorithm`s inherent dependency displacement distribution. This characterises the bias of the algorithm in terms of dependency displacement, which quantify both distance and direction of syntactic relations. We show that the similarity of an algorithm`s inherent distribution to a treebank`s displacement distribution is clearly correlated to the algorithm`s parsing performance on that treebank, specificially with highly significant and substantial correlations for the predominant sentence lengths in Universal Dependency treebanks. We also obtain results which show a more discrete analysis of dependency displacement does not result in any meaningful correlations.
A Gold Standard Dependency Treebank for Turkish (2020)

We introduce TWT; a new treebank for Turkish which consists of web and Wikipedia sentences that are annotated for segmentation, morphology, part-of-speech and dependency relations. To date, it is the largest publicly available human-annotated morpho-syntactic Turkish treebank in terms of the annotated word count. It is also the first large Turkish dependency treebank that has a dedicated Wikipedia section. We present the tagsets and the methodology that are used in annotating the treebank and also the results of the baseline experiments on Turkish dependency parsing with this treebank.
Chunk Different Kind of Spoken Discourse: Challenges for Machine Learning (2020)

This paper describes the development of a chunker for spoken data by supervised machine learning using the CRFs, based on a small reference corpus composed of two kinds of discourse: prepared monologue vs. spontaneous talk in interaction. The methodology considers the specific character of the spoken data. The machine learning uses the results of several available taggers, without correcting the results manually. Experiments show that the discourse type (monologue vs. free talk), the speech nature (spontaneous vs. prepared) and the corpus size can influence the results of the machine learning process and must be considered while interpreting the results.
GRAIN-S: Manually Annotated Syntax for German Interviews (2020)

We present GRAIN-S, a set of manually created syntactic annotations for radio interviews in German. The dataset extends an existing corpus GRAIN and comes with constituency and dependency trees for six interviews. The rare combination of gold- and silver-standard annotation layers coming from GRAIN with high-quality syntax trees can serve as a useful resource for speech- and text-based research. Moreover, since interviews can be put between carefully prepared speech and spontaneous conversational speech, they cover phenomena not seen in traditional newspaper-based treebanks. Therefore, GRAIN-S can contribute to research into techniques for model adaptation and for building more corpus-independent tools. GRAIN-S follows TIGER, one of the established syntactic treebanks of German. We describe the annotation process and discuss decisions necessary to adapt the original TIGER guidelines to the interviews domain. Next, we give details on the conversion from TIGER-style trees to dependency trees. We provide data statistics and demonstrate differences between the new dataset and existing out-of-domain test sets annotated with TIGER syntactic structures. Finally, we provide baseline parsing results for further comparison.
Yor\`ub\'a Dependency Treebank (YTB) (2020)

Low-resource languages present enormous NLP opportunities as well as varying degrees of difficulties. The newly released treebank of hand-annotated parts of the Yoruba Bible provides an avenue for dependency analysis of the Yoruba language; the application of a new grammar formalism to the language. In this paper, we discuss our choice of Universal Dependencies, important dependency annotation decisions considered in the creation of the first annotation guidelines for Yoruba and results of our parsing experiments. We also lay the foundation for future incorporation of other domains with the initial test on Yoruba Wikipedia articles and highlighted future directions for the rapid expansion of the treebank.
English Recipe Flow Graph Corpus (2020)

We present an annotated corpus of English cooking recipe procedures, and describe and evaluate computational methods for learning these annotations. The corpus consists of 300 recipes written by members of the public, which we have annotated with domain-specific linguistic and semantic structure. Each recipe is annotated with (1) {\textquoteleft}recipe named entities' (r-NEs) specific to the recipe domain, and (2) a flow graph representing in detail the sequencing of steps, and interactions between cooking tools, food ingredients and the products of intermediate steps. For these two kinds of annotations, inter-annotator agreement ranges from 82.3 to 90.5 F1, indicating that our annotation scheme is appropriate and consistent. We experiment with producing these annotations automatically. For r-NE tagging we train a deep neural network NER tool; to compute flow graphs we train a dependency-style parsing procedure which we apply to the entire sequence of r-NEs in a recipe. In evaluations, our systems achieve 71.1 to 87.5 F1, demonstrating that our annotation scheme is learnable.
Development of a General-Purpose Categorial Grammar Treebank (2020)

This paper introduces ABC Treebank, a general-purpose categorial grammar (CG) treebank for Japanese. It is {\textquoteleft}general-purpose' in the sense that it is not tailored to a specific variant of CG, but rather aims to offer a theory-neutral linguistic resource (as much as possible) which can be converted to different versions of CG (specifically, CCG and Type-Logical Grammar) relatively easily. In terms of linguistic analysis, it improves over the existing Japanese CG treebank (Japanese CCGBank) on the treatment of certain linguistic phenomena (passives, causatives, and control/raising predicates) for which the lexical specification of the syntactic information reflecting local dependencies turns out to be crucial. In this paper, we describe the underlying {\textquoteleft}theory' dubbed ABC Grammar that is taken as a basis for our treebank, outline the general construction of the corpus, and report on some preliminary results applying the treebank in a semantic parsing system for generating logical representations of sentences.
Dependency Parsing for Urdu: Resources, Conversions and Learning (2020)

This paper adds to the available resources for the under-resourced language Urdu by converting different types of existing treebanks for Urdu into a common format that is based on Universal Dependencies. We present comparative results for training two dependency parsers, the MaltParser and a transition-based BiLSTM parser on this new resource. The BiLSTM parser incorporates word embeddings which improve the parsing results significantly. The BiLSTM parser outperforms the MaltParser with a UAS of 89.6 and an LAS of 84.2 with respect to our standardized treebank resource.
Prague Dependency Treebank - Consolidated 1.0 (2020)

We present a richly annotated and genre-diversified language resource, the Prague Dependency Treebank-Consolidated 1.0 (PDT-C 1.0), the purpose of which is - as it always been the case for the family of the Prague Dependency Treebanks - to serve both as a training data for various types of NLP tasks as well as for linguistically-oriented research. PDT-C 1.0 contains four different datasets of Czech, uniformly annotated using the standard PDT scheme (albeit not everything is annotated manually, as we describe in detail here). The texts come from different sources: daily newspaper articles, Czech translation of the Wall Street Journal, transcribed dialogs and a small amount of user-generated, short, often non-standard language segments typed into a web translator. Altogether, the treebank contains around 180,000 sentences with their morphological, surface and deep syntactic annotation. The diversity of the texts and annotations should serve well the NLP applications as well as it is an invaluable resource for linguistic research, including comparative studies regarding texts of different genres. The corpus is publicly and freely available.
Training a Swedish Constituency Parser on Six Incompatible Treebanks (2020)

We investigate a transition-based parser that uses Eukalyptus, a function-tagged constituent treebank for Swedish which includes discontinuous constituents. In addition, we show that the accuracy of this parser can be improved by using a multitask learning architecture that makes it possible to train the parser on additional treebanks that use other annotation models.
Parsing as Tagging (2020)

We propose a simple yet accurate method for dependency parsing that treats parsing as tagging (PaT). That is, our approach addresses the parsing of dependency trees with a sequence model implemented with a bidirectional LSTM over BERT embeddings, where the {\textquotedblleft}tag{\textquotedblright} to be predicted at each token position is the relative position of the corresponding head. For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left. Despite its simplicity, our approach performs well. For example, our approach outperforms the state-of-the-art method of (Fern{\'a}ndez-Gonz{\'a}lez and G{\'o}mez-Rodr{\'i}guez, 2019) on Universal Dependencies (UD) by 1.76{\%} unlabeled attachment score (UAS) for English, 1.98{\%} UAS for French, and 1.16{\%} UAS for German. On average, on 12 UD languages, our method with minimal tuning performs comparably with this state-of-the-art approach: better by 0.11{\%} UAS, and worse by 0.58{\%} LAS.
The EDGeS Diachronic Bible Corpus (2020)

We present the EDGeS Diachronic Bible Corpus: a diachronically and synchronically parallel corpus of Bible translations in Dutch, English, German and Swedish, with texts from the 14th century until today. It is compiled in the context of an intended longitudinal and contrastive study of complex verb constructions in Germanic. The paper discusses the corpus design principles, its selection of 36 Bibles, and the information and metadata encoded for the corpus texts. The EDGeS corpus will be available in two forms: the whole corpus will be accessible for researchers behind a login in the well-known OPUS search infrastructure, and the open subpart of the corpus will be available for download.
Treebanking User-Generated Content: A Proposal for a Unified Representation in Universal Dependencies (2020)

The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD.
A Diachronic Treebank of Russian Spanning More Than a Thousand Years (2020)

We describe the Troms{\o} Old Russian and Old Church Slavonic Treebank (TOROT) that spans from the earliest Old Church Slavonic to modern Russian texts, covering more than a thousand years of continuous language history. We focus on the latest additions to the treebank, first of all, the modern subcorpus that was created by a high-quality conversion of the existing treebank of contemporary standard Russian (SynTagRus).
\AETHEL: Automatically Extracted Typelogical Derivations for Dutch (2020)

We present {\AE}THEL, a semantic compositionality dataset for written Dutch. {\AE}THEL consists of two parts. First, it contains a lexicon of supertags for about 900 000 words in context. The supertags correspond to types of the simply typed linear lambda-calculus, enhanced with dependency decorations that capture grammatical roles supplementary to function-argument structures. On the basis of these types, {\AE}THEL further provides 72 192 validated derivations, presented in four formats: natural-deduction and sequent-style proofs, linear logic proofnets and the associated programs (lambda terms) for meaning composition. {\AE}THEL`s types and derivations are obtained by means of an extraction algorithm applied to the syntactic analyses of LASSY Small, the gold standard corpus of written Dutch. We discuss the extraction algorithm and show how {\textquoteleft}virtual elements' in the original LASSY annotation of unbounded dependencies and coordination phenomena give rise to higher-order types. We suggest some example usecases highlighting the benefits of a type-driven approach at the syntax semantics interface. The following resources are open-sourced with {\AE}THEL: the lexical mappings between words and types, a subset of the dataset consisting of 7 924 semantic parses, and the Python code that implements the extraction algorithm.
AMALGUM -- A Free, Balanced, Multilayer English Web Corpus (2020)

We present a freely available, genre-balanced English web corpus totaling 4M tokens and featuring a large number of high-quality automatic annotation layers, including dependency trees, non-named entity annotations, coreference resolution, and discourse trees in Rhetorical Structure Theory. By tapping open online data sources the corpus is meant to offer a more sizable alternative to smaller manually created annotated data sets, while avoiding pitfalls such as imbalanced or unknown composition, licensing problems, and low-quality natural language processing. We harness knowledge from multiple annotation layers in order to achieve a {\textquotedblleft}better than NLP{\textquotedblright} benchmark and evaluate the accuracy of the resulting resource.
Typical Sentences as a Resource for Valence (2020)

Verb valence information can be derived from corpora by using subcorpora of typical sentences that are constructed in a language independent manner based on frequent POS structures. The inspection of typical sentences with a fixed verb in a certain position can show the valence information directly. Using verb fingerprints, consisting of the most typical sentence patterns the verb appears in, we are able to identify standard valence patterns and compare them against a language`s valence profile. With a very limited number of training data per language, valence information for other verbs can be derived as well. Based on the Norwegian valence patterns we are able to find comparative patterns in German where typical sentences are able to express the same situation in an equivalent way and can so construct verb valence pairs for a bilingual PolyVal dictionary. This contribution discusses this application with a focus on the Norwegian valence dictionary NorVal.
Recognizing Sentence-level Logical Document Structures with the Help of Context-free Grammars (2020)

Current sentence boundary detectors split documents into sequentially ordered sentences by detecting their beginnings and ends. Sentences, however, are more deeply structured even on this side of constituent and dependency structure: they can consist of a main sentence and several subordinate clauses as well as further segments (e.g. inserts in parentheses); they can even recursively embed whole sentences and then contain multiple sentence beginnings and ends. In this paper, we introduce a tool that segments sentences into tree structures to detect this type of recursive structure. To this end, we retrain different constituency parsers with the help of modified training data to transform them into sentence segmenters. With these segmenters, documents are mapped to sequences of sentence-related {\textquotedblleft}logical document structures{\textquotedblright}. The resulting segmenters aim to improve downstream tasks by providing additional structural information. In this context, we experiment with German dependency parsing. We show that for certain sentence categories, which can be determined automatically, improvements in German dependency parsing can be achieved using our segmenter for preprocessing. The assumption suggests that improvements in other languages and tasks can be achieved.
When Collaborative Treebank Curation Meets Graph Grammars (2020)

In this paper we present Arborator-Grew, a collaborative annotation tool for treebank development. Arborator-Grew combines the features of two preexisting tools: Arborator and Grew. Arborator is a widely used collaborative graphical online dependency treebank annotation tool. Grew is a tool for graph querying and rewriting specialized in structures needed in NLP, i.e. syntactic and semantic dependency trees and graphs. Grew also has an online version, Grew-match, where all Universal Dependencies treebanks in their classical, deep and surface-syntactic flavors can be queried. Arborator-Grew is a complete redevelopment and modernization of Arborator, replacing its own internal database storage by a new Grew API, which adds a powerful query tool to Arborator`s existing treebank creation and correction features. This includes complex access control for parallel expert and crowd-sourced annotation, tree comparison visualization, and various exercise modes for teaching and training of annotators. Arborator-Grew opens up new paths of collectively creating, updating, maintaining, and curating syntactic treebanks and semantic graph banks.