In the course of building a multilingual Event-type Ontology resource called SynSemClass, it was necessary to provide the maintainers and the annotators with a set of tools to facilitate their job, achieve data format consistency, and in general obtain high-quality data. We have adapted a previously existing tool (Urešová et al., 2018b), developed to assist the work in capturing bilingual synonymy. This tool needed to be both substantially expanded with some new features and fundamentally changed in the context of developing the resource for more languages, which necessarily is to be done in parallel. We are thus presenting here the tool, the new data structure design which had to change at the same time, and the associated workflow.

This paper presents ongoing work in the expansion of the multilingual semantic event-type ontology SynSemClass (Czech-English-German) to include Spanish. As in previous versions of the lexicon, Spanish verbal synonyms have been collected from a sentence-aligned parallel corpus and classified into classes based on their syntactic-semantic properties. Each class member is linked to a number of syntactic and/or semantic resources specific to each language, thus enriching the annotation and enabling interoperability. This paper describes the procedure for the data extraction and annotation of Spanish verbal synonyms in the lexicon.

The rhetoric strategy of hedging serves to attenuate speech acts and their semantic content, as in English ‘kind of’ or ‘somehow’. While hedging has recently met with increasing interest in linguistic research, most studies deal with modern languages, preferably English, and take a synchronic approach. This paper complements this research by tracing the diachronic syntactic flexibilization of the Vedic Sanskrit particle iva from a marker of comparison (‘like’) to a full-fledged adaptor. We discuss the outcomes of a diachronic Bayesian framework applied to iva constructions in a Universal Dependencies treebank, and supplement these results with a qualitative discussion of relevant text passages.

The Japanese CCGBank serves as training and evaluation data for developing Japanese CCG parsers. However, since it is automatically generated from the Kyoto Corpus, a dependency treebank, its linguistic validity still needs to be sufficiently verified. In this paper, we focus on the analysis of passive/causative constructions in the Japanese CCGBank and show that, together with the compositional semantics of ccg2lambda, a semantic parsing system, it yields empirically wrong predictions for the nested construction of passives and causatives.

Constituency parsing is an important task of informing how words are combined to form sentences. While constituency parsing in English has seen significant progress in the last few years, tools for constituency parsing in Indonesian remain few and far between. In this work, we publish ICON (Indonesian CONstituency treebank), the hitherto largest publicly-available manually-annotated benchmark constituency treebank for the Indonesian language with a size of 10,000 sentences and approximately 124,000 constituents and 182,000 tokens, which can support the training of state-of-the-art transformer-based models. We establish strong baselines on the ICON dataset using the Berkeley Neural Parser with transformer-based pre-trained embeddings, with the best performance of 88.85% F1 score coming from our own version of SpanBERT (IndoSpanBERT). We further analyze the predictions made by our best-performing model to reveal certain idiosyncrasies in the Indonesian language that pose challenges for constituency parsing.

Historical treebanking within the generative framework has gained in popularity. However, there are still many languages and historical periods yet to be represented. For German, a constituency treebank exists for historical Low German, but not Early New High German. We begin to fill this gap by presenting our initial work on the Parsed Corpus of Early New High German (PCENHG). We present the methodological considerations and workflow for the treebank’s annotations and development. Given the limited amount of currently available PCENHG treebank data, we treat it as a low-resource language and leverage a larger, closely related variety—Middle Low German—to build a parser to help facilitate faster post-annotation correction. We present an analysis on annotation speeds and conclude with a small pilot use-case, highlighting potential for future linguistic analyses. In doing so we highlight the value of the treebank’s development for historical linguistic analysis and demonstrate the benefits and challenges of developing a parser using two closely related historical Germanic varieties.

Searching dependency graphs and manipulating them can be a time consuming and challenging task to get right. We document Semgrex, a system for searching dependency graphs, and introduce Ssurgeon, a system for manipulating the output of Semgrex. The compact language used by these systems allows for easy command line or API processing of dependencies. Additionally, integration with publicly released toolkits in Java and Python allows for searching text relations and attributes over natural text.

This paper presents detailed mappings between the structures used in Abstract Meaning Representation (AMR) and those used in Uniform Meaning Representation (UMR). These structures include general semantic roles, rolesets, and concepts that are largely shared between AMR and UMR, but with crucial differences. While UMR annotation of new low-resource languages is ongoing, AMR-annotated corpora already exist for many languages, and these AMR corpora are ripe for conversion to UMR format. Rather than focusing on semantic coverage that is new to UMR (which will likely need to be dealt with manually), this paper serves as a resource (with illustrated mappings) for users looking to understand the fine-grained adjustments that have been made to the representation techniques for semantic categoriespresent in both AMR and UMR.

Recent work has formulated the task for computational construction grammar as producing a constructicon given a corpus of usage. Previous work has evaluated these unsupervised grammars using both internal metrics (for example, Minimum Description Length) and external metrics (for example, performance on a dialectology task). This paper instead takes a linguistic approach to evaluation, first learning a constructicon and then analyzing its contents from a linguistic perspective. This analysis shows that a learned constructicon can be divided into nine major types of constructions, of which Verbal and Nominal are the most common. The paper also shows that both the token and type frequency of constructions can be used to model variation across registers and dialects.

There is a serious theoretical and methodological dilemma in usage-based construction grammar: how to identify constructions based on corpus pattern analysis. The present paper provides an overview of this dilemma, focusing on argument structure constructions (ASCs) in general. It seeks to answer the question of how a data-driven construction grammatical description can be built on the collocation data extracted from corpora. The study is of meta-scientific interest: it compares theoretical proposals in construction grammar regarding how they handle co-occurrences emerging from a corpus. Discussing alternative bottom-up approaches to the notion of construction, the paper concludes that there is no one-to-one correspondence between corpus patterns and constructions. Therefore, a careful analysis of the former can empirically ground both the identification and the description of constructions.

This paper presents a novel framework for evaluating Neural Language Models’ linguistic abilities using a constructionist approach. Not only is the usage-based model in line with the un- derlying stochastic philosophy of neural architectures, but it also allows the linguist to keep meaning as a determinant factor in the analysis. We outline the framework and present two possible scenarios for its application.

Current language processing tools presuppose input in the form of a sequence of high-dimensional vectors with continuous values. Lexical items can be converted to such vectors with standard methodology and subsequent processing is assumed to handle structural features of the string. Constructional features do typically not fit in that processing pipeline: they are not as clearly sequential, they overlap with other items, and the fact that they are combinations of lexical items obscures their ontological status as observable linguistic items in their own right. Constructional grammar frameworks allow for a more general view on how to understand lexical items and their configurations in a common framework. This paper introduces an approach to accommodate that understanding in a vector symbolic architecture, a processing framework which allows for combinations of continuous vectors and discrete items, convenient for various downstream processing using e.g. neural processing or other tools which expect input in vector form.

This paper revisits tokenization from a theoretical perspective, and argues for the necessity of a constructivist approach to tokenization for semantic parsing and modeling language acquisition. We consider two problems: (1) (semi-) automatically converting existing lexicalist annotations, e.g. those of the Penn TreeBank, into constructivist annotations, and (2) automatic tokenization of raw texts. We demonstrate that (1) a heuristic rule-based constructivist tokenizer is able to yield relatively satisfactory accuracy when gold standard Penn TreeBank part-of-speech tags are available, but that some manual annotations are still necessary to obtain gold standard results, and (2) a neural tokenizer is able to provide accurate automatic constructivist tokenization results from raw character sequences. Our research output also includes a set of high-quality morpheme-tokenized corpora, which enable the training of computational models that more closely align with language comprehension and acquisition.

Fluid Construction Grammar (FCG) is a computational framework that provides a formalism for representing construction grammars and a processing engine that supports construction-based language comprehension and production. FCG is conceived as a computational operationalisation of the basic tenets of construction grammar. It thereby aims to establish more solid foundations for constructionist theories of language, while expanding their application potential in the fields of artificial intelligence and natural language understanding. This paper aims to provide a brief introduction to the FCG research programme, reflecting on what has been achieved so far and identifying key challenges for the future.

In this paper we introduce a freely available treebank that includes argument structure construction (ASC) annotation. We then use the treebank to train probabilistic annotation models that rely on verb lemmas and/ or syntactic frames. We also use the treebank data to train a highly accurate transformer-based annotation model (F1 = 91.8%). Future directions for the development of the treebank and annotation models are discussed.

One important aspect of language is how speakers generate utterances and texts to convey their intended meanings. In this paper, we bring various aspects of the Construction Grammar (CxG) and the Systemic Functional Grammar (SFG) theories in a deep learning computational framework to model empathic language. Our corpus consists of 440 essays written by premed students as narrated simulated patient–doctor interactions. We start with baseline classifiers (state-of-the-art recurrent neural networks and transformer models). Then, we enrich these models with a set of linguistic constructions proving the importance of this novel approach to the task of empathy classification for this dataset. Our results indicate the potential of such constructions to contribute to the overall empathy profile of first-person narrative essays.

This paper discusses the challenges of annotating the predicate-argument structure of Chinese verb compounds in Uniform Meaning Representation (UMR), a recent meaning representation framework that extends Abstract Meaning Representation (AMR) to cross-linguistic settings. The key issue is to decide whether to annotate the argument structure of a verb compound as a whole, or to annotate the argument structure of their component verbs as well as the relations between them. We examine different types of Chinese verb compounds, and propose how to annotate them based on the principle of compositionality, level of grammaticalization, and productivity of component verbs. We propose a solution to the practical problem of having to define the semantic roles for Chinese verb compounds that are quite open-ended by separating compositional verb compounds from verb compounds that are non-compositional or have grammaticalized verb components. For compositional verb compounds, instead of annotating the argument structure of the verb compound as a whole, we annotate the argument structure of the component verbs as well as the semantic relations between them as creating an exhaustive list of such verb compounds is infeasible. Verb compounds with grammaticalized verb components also tend to be productive and we represent grammaticalized verb compounds as either attributes of the primary verb or as relations.

Construction Grammar (CxG) has recently been used as the basis for probing studies that have investigated the performance of large pretrained language models (PLMs) with respect to the structure and meaning of constructions. In this position paper, we make suggestions for the continuation and augmentation of this line of research. We look at probing methodology that was not designed with CxG in mind, as well as probing methodology that was designed for specific constructions. We analyse selected previous work in detail, and provide our view of the most important challenges and research questions that this promising new field faces.

This paper reports on negative results in a task of automatic identification of schematic clausal constructions and their elements in Brazilian Portuguese. The experiment was set up so as to test whether form and meaning properties of constructions, modeled in terms of Universal Dependencies and FrameNet Frames in a Constructicon, would improve the performance of transformer models in the task. Qualitative analysis of the results indicate that alternatives to the linearization of those properties, dataset size and a post-processing module should be explored in the future as a means to make use of information in Constructicons for NLP tasks.

In this paper, we discuss the challenges that we faced during the construction of a Universal Dependencies treebank for Abaza, a polysynthetic Northwest Caucasian language. We propose an alternative to the morpheme-level annotation of polysynthetic languages introduced in Park et al. (2021). Our approach aims at reducing the number of morphological features, yet providing all the necessary information for the comprehensive representation of all the syntactic relations. Besides, we suggest to add one language-specific relation needed for annotating repetitions in spoken texts and present several solutions that aim at increasing cross-linguistic comparability of our data.

This paper presents the harmonisation process carried out on the five treebanks available for Latin in Universal Dependencies, with the aim of eliminating the discrepancies in their annotation styles. Indeed, this is the first issue to be addressed when parsing Latin, as significant drops in parsing accuracy on different Latin treebanks have been repeatedly observed. Latin syntactic variability surely accounts for this, but parsing results are as well affected by divergent annotation choices. By analysing where annotations differ, we propose a Python-based alignment of the five UD treebanks. Consequently, the impact of annotation choices on accuracy scores is assessed by performing parsing experiments with UDPipe and Stanza.

This paper reports the development of the first dependency treebank for the Sinhala language (STB). Sinhala, which is morphologically rich, is a low-resource language with few linguistic and computational resources available publicly. This treebank consists of 100 sentences taken from a large contemporary written text corpus. These sentences were annotated manually according to the Universal Dependencies framework. In this paper, apart from elaborating on the approach that has been followed to create the treebank, we have also discussed some interesting syntactic constructions found in the corpus and how we have handled them using the current Universal Dependencies specification.

Pomak is an endangered oral Slavic language of Thrace/Greece. We present a short description of its interesting morphological and syntactic features in the UD framework. Because the morphological annotation of the treebank takes advantage of existing resources, it requires a different methodological approach from the one adopted for syntactic annotation that has started from scratch. It also requires the option of obtaining morphological predictions/evaluation separately from the syntactic ones with state-of-the-art NLP tools. Active annotation is applied in various settings in order to identify the best model that would facilitate the ongoing syntactic annotation.

This article presents a comparative analysis of four different syntactic typological approaches applied to 20 different languages. We compared three specific quantitative methods, using parallel CoNLL-U corpora, to the classification obtained via syntactic features provided by a typological database (lang2vec). First, we analyzed the Marsagram linear approach which consists of extracting the frequency word-order patterns regarding the position of components inside syntactic nodes. The second approach considers the relative position of heads and dependents, and the third is based simply on the relative position of verbs and objects. From the results, it was possible to observe that each method provides different language clusters which can be compared to the classic genealogical classification (the lang2vec and the head and dependent methods being the closest). As different word-order phenomena are considered in these specific typological strategies, each one provides a different angle of analysis to be applied according to the precise needs of the researchers.

In this paper, we present a system for generating semantic representations from Universal Dependencies syntactic parses. The foundation of our pipeline is a rule-based interpretation system, designed to be as universal as possible, which produces the correct semantic structure; the content of this structure can then be filled in by additional (sometimes language-specific) post-processing. The rules which generate semantic resources rely as far as possible on the UD parse alone, so that they can apply to any language for which such a parse can be given (a much larger number than the number of languages for which detailed semantically annotated corpora are available). We discuss our general approach, and highlight areas where the UD annotation scheme makes semantic interpretation less straightforward. We compare our results with the Parallel Meaning Bank, and show that when it comes to modelling semantic structure, our approach shows potential, but also discuss some areas for expansion.

Recent efforts to consolidate guidelines and treebanks in the Universal Dependencies project raise the expectation that joint training and dataset comparison is increasingly possible for high-resource languages such as English, which have multiple corpora. Focusing on the two largest UD English treebanks, we examine progress in data consolidation and answer several questions: Are UD English treebanks becoming more internally consistent? Are they becoming more like each other and to what extent? Is joint training a good idea, and if so, since which UD version? Our results indicate that while consolidation has made progress, joint models may still suffer from inconsistencies, which hamper their ability to leverage a larger pool of training data.

This paper discusses the need for including morphological features in Japanese Universal Dependencies (UD). In the current version (v2.11) of the Japanese UD treebanks, sentences are tokenized at the morpheme level, and almost no morphological feature annotation is used. However, Japanese is not an isolating language that lacks morphological inflection but is an agglutinative language. Given this situation, we introduce a tentative scheme for retokenization and morphological feature annotation for Japanese UD. Then, we measure and compare the morphological complexity of Japanese with other languages to demonstrate that the proposed tokenizations show similarities to synthetic languages reflecting the linguistic typology.

How does the preference for dependency length minimization (DLM) develop in early child language? This study takes up this question with the dative alternation in English as the test case. We built a large-scale dataset of dative constructions using transcripts of naturalistic child-parent interactions. Across different developmental stages of children, there appears to be a strong tendency for DLM. The tendency emerges between the age range of 12-18 months, slightly decreases until 30-36 months, then becomes more pronounced afterwards and approaches parents’ production preferences after 48 months. We further show the extent of DLM depends on how a given dative construction is realized: the tendency for shorter dependencies is much more pronounced in double object structures, whereas the prepositional object structures are associated with longer dependencies.

Text classification is a popular and well-studied problem in Natural Language Processing. Most previous work on text classification has focused on deep neural networks such as LSTMs and CNNs. However, text classification studies using syntactic and semantic information are very limited in the literature. In this study, we propose a model using Graph Attention Network (GAT) that incorporates semantic and syntactic information as input for the text classification task. The semantic representations of UCCA and AMR are used as semantic information and the dependency tree is used as syntactic information. Extensive experimental results and in-depth analysis show that UCCA-GAT model, which is a semantic-aware model outperforms the AMR-GAT and DEP-GAT, which are semantic and syntax-aware models respectively. We also provide a comprehensive analysis of the proposed model to understand the limitations of the representations for the problem.

In this paper, we provide an explicit interface to formal semantics for Dependency Grammar, based on Glue Semantics. Glue Semantics has mostly been developed in the context of Lexical Functional Grammar, which shares two crucial assumptions with Dependency Grammar: lexical integrity and allowance of nonbinary-branching syntactic structure. We show how Glue can be adapted to the Dependency Grammar setting and provide sample semantic analyses of quantifier scope, control infinitives and relative clauses.

Nodes in Abstract Meaning Representation (AMR) are generally thought of as neo-Davidsonian entities. We review existing translation into neo-Davidsonian representations and show that these translations inconsistently handle copula sentences. We link the problem to an asymmetry arising from a problematic handling of words with no associated PropBank frames for the underlying predicate. We introduce a method to automatically and uniformly decompose AMR nodes into an entity-part and a predicative part, which offers a consistent treatment of copula sentences and quasi- predicates such as brother or client.

In this paper, we propose a new model for annotating dependency relations at the Mandarin character level with the aim of building treebanks to cope with the unsatisfactory performance of existing word segmentation and syntactic analysis models in specific scientific domains, such as Chinese patent texts. The result is a treebank of 100 sentences annotated according to our scheme, which also serves as a training corpus that facilitates the subsequent development of a joint word segmenter and dependency analyzer that enables downstream tasks in Chinese to be separated from the non-standardized pre-processing step of word segmentation.

Building upon existing work on word order freedom and syntactic annotation, this paper investigates whether we can differentiate between findings that reveal inherent properties of natural languages and their syntax, and features dependent on annotations used in computing the measures. An existing quantifiable and linguistically interpretable measure of word order freedom in language is applied to take a closer look at the robustness of the basic measure (word order entropy) to variations in dependency corpora used in the analysis. Measures are compared at three levels of generality, applied to corpora annotated according to the Universal Dependencies v1 and v2 annotation guidelines, selecting 31 languages for analysis. Preliminary results show that certain measures, such as subject-object relation order freedom, are sensitive to slight changes in annotation guidelines, while simpler measures are more robust, highlighting aspects of these metrics that should be taken into consideration when using dependency corpora for linguistic analysis and generalisation.

This paper introduces a typometric measure of flexibility, which quantifies the variability of head-dependent word order on the whole set of treebanks of a language or on specific constructions. The measure is based on the notion of head-initiality and we show that it can be computed for all of languages of the Universal Dependency treebank set, that it does not require ad-hoc thresholds to categorize languages or constructions, and that it can be applied with any granularity of constructions and languages. We compare our results with Bakker’s (1998) categorical flexibility index. Typometric flexibility is shown to be a good measure for characterizing the language distribution with respect to word order for a given construction, and for estimating whether a construction predicts the global word order behavior of a language.

Nominal classifiers categorize nouns based on salient semantic properties. Past studies have long debated whether sortal classifiers (related to intrinsic semantic noun features) and mensural classifiers (related to quantity) should be considered as the same grammatical category. Suggested diagnostic tests rely on functional and distributional criteria, typically evaluated in terms of isolated example sentences obtained through elicitation. This paper offers a systematic re-evaluation of this long-standing question: using 981,076 nominal phrases from a 489 MB dependency-parsed word corpus, corresponding extracted contextual word embeddings from a Chinese BERT model, and information-theoretic measures of mutual information, we show that mensural classifiers can be distributionally and functionally distinguished from sortal classifiers justifying the existence of distinct syntactic categories for mensural and sortal classifiers. Our study also entails broader implications for the typological study of classifier systems.

We present work in progress that aims to address the coverage issue faced by rule-based text generators. We propose a pipeline for extracting abstract dependency template (predicate-argument structures) from Wikipedia text to be used as input for generating text from structured data with the FORGe system. The pipeline comprises three main components: (i) candidate sentence retrieval, (ii) clause extraction, ranking and selection, and (iii) conversion to predicate-argument form. We present an approach and preliminary evaluation for the ranking and selection module.