Rediscovering Greenberg's Word Order Universals in UD

International audienceThis paper discusses an empirical refoundation of selected Greenbergian word order univer-sals based on a data analysis of the Universal Dependencies project. The nature of the data we work on allows us to extract rich details for testing well-known typological universals and constitutes therefore a valuable basis for validating Greenberg's universals. Our results show that we can refine some Greenbergian universals in a more empirical and accurate way by means of a data-driven typological analysis

The relation between dependency distance and frequency

International audienceThis present pilot study investigates the relationship between dependency distance and frequency based on the analysis of an English dependency treebank. The preliminary result shows that there is a non-linear relation between dependency distance and frequency. This relation between them can be further formalized as a power law function which can be used to predict the distribution of dependency distance in a treebank

Rediscovering Greenberg's Word Order Universals in UD

International audienceThis paper discusses an empirical refoundation of selected Greenbergian word order univer-sals based on a data analysis of the Universal Dependencies project. The nature of the data we work on allows us to extract rich details for testing well-known typological universals and constitutes therefore a valuable basis for validating Greenberg's universals. Our results show that we can refine some Greenbergian universals in a more empirical and accurate way by means of a data-driven typological analysis

The relation between dependency distance and frequency

International audienceThis present pilot study investigates the relationship between dependency distance and frequency based on the analysis of an English dependency treebank. The preliminary result shows that there is a non-linear relation between dependency distance and frequency. This relation between them can be further formalized as a power law function which can be used to predict the distribution of dependency distance in a treebank

When Collaborative Treebank Curation Meets Graph Grammars: Arborator With a Grew Back-End

International audienceIn 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

Understanding the structure and meaning of Finnish texts: From corpus creation to deep language modelling

Natural Language Processing (NLP) is a cross-disciplinary field combining elements of computer science, artificial intelligence, and linguistics, with the objective of developing means for computational analysis, understanding or generation of human language. The primary aim of this thesis is to advance natural language processing in Finnish by providing more resources and investigating the most effective machine learning based practices for their use. The thesis focuses on NLP topics related to understanding the structure and meaning of written language, mainly concentrating on structural analysis (syntactic parsing) as well as exploring the semantic equivalence of statements that vary in their surface realization (paraphrase modelling). While the new resources presented in the thesis are developed for Finnish, most of the methodological contributions are language-agnostic, and the accompanying papers demonstrate the application and evaluation of these methods across multiple languages. The first set of contributions of this thesis revolve around the development of a state-of-the-art Finnish dependency parsing pipeline. Firstly, the necessary Finnish training data was converted to the Universal Dependencies scheme, integrating Finnish into this important treebank collection and establishing the foundations for Finnish UD parsing. Secondly, a novel word lemmatization method based on deep neural networks is introduced and assessed across a diverse set of over 50 languages. And finally, the overall dependency parsing pipeline is evaluated on a large number of languages, securing top ranks in two competitive shared tasks focused on multilingual dependency parsing. The overall outcome of this line of research is a parsing pipeline reaching state-of-the-art accuracy in Finnish dependency parsing, the parsing numbers obtained with the latest pre-trained language models approaching (at least near) human-level performance. The achievement of large language models in the area of dependency parsing— as well as in many other structured prediction tasks— brings up the hope of the large pre-trained language models genuinely comprehending language, rather than merely relying on simple surface cues. However, datasets designed to measure semantic comprehension in Finnish have been non-existent, or very scarce at the best. To address this limitation, and to reflect the general change of emphasis in the field towards task more semantic in nature, the second part of the thesis shifts its focus to language understanding through an exploration of paraphrase modelling. The second contribution of the thesis is the creation of a novel, large-scale, manually annotated corpus of Finnish paraphrases. A unique aspect of this corpus is that its examples have been manually extracted from two related text documents, with the objective of obtaining non-trivial paraphrase pairs valuable for training and evaluating various language understanding models on paraphrasing. We show that manual paraphrase extraction can yield a corpus featuring pairs that are both notably longer and less lexically overlapping than those produced through automated candidate selection, the current prevailing practice in paraphrase corpus construction. Another distinctive feature in the corpus is that the paraphrases are identified and distributed within their document context, allowing for richer modelling and novel tasks to be defined

Toward Annotation Efficiency in Biased Learning Settings for Natural Language Processing

The goal of this thesis is to improve the feasibility of building applied NLP systems for more diverse and niche real-world use-cases of extracting structured information from text. A core factor in determining this feasibility is the cost of manually annotating enough unbiased labeled data to achieve a desired level of system accuracy, and our goal is to reduce this cost. We focus on reducing this cost by making contributions in two directions: (1) easing the annotation burden by leveraging high-level expert knowledge in addition to labeled examples, thus making approaches more annotation-efficient; and (2) mitigating known biases in cheaper, imperfectly labeled real-world datasets so that we may use them to our advantage. A central theme of this thesis is that high-level expert knowledge about the data and task can allow for biased labeling processes that focus experts on only manually labeling aspects of the data that cannot be easily labeled through cheaper means. This combination allows for more accurate models with less human effort. We conduct our research on this general topic through three diverse problems with immediate applications to real-world settings. First, we study an applied problem in biased text classification. We encounter a rare-event text classification system that has been deployed for several years. We are tasked with improving this system's performance using only the severely biased incidental feedback provided by the experts over years of system use. We develop a method that combines importance weighting and an unlabeled data imputation scheme that exploits the selection-bias of the feedback to train an unbiased classifier without requiring additional labeled data. We experimentally demonstrate that this method considerably improves the system performance. Second, we tackle an applied problem in named entity recognition (NER) concerning learning tagging models from data that have very low recall for annotated entities. To solve this issue we propose a novel loss, the Expected Entity Ratio (EER), that uses an uncertain estimate of the proportion of entities in the data to counteract the false-negative bias in the data, encouraging the model to have the correct ratio of entities in expectation. We justify the principles of our approach by providing theory that shows it recovers the true tagging distribution under mild conditions. Additionally we provide extensive empirical results that show it to be practically useful. Empirically, we find that it meets or exceeds performance of state-of-the-art baselines across a variety of languages, annotation scenarios, and amounts of labeled data. We also show that, when combined with our approach, a novel sparse annotation scheme can outperform exhaustive annotation for modest annotation budgets. Third, we study the challenging problem of syntactic parsing in low-resource languages. We approach the problem from a cross-lingual perspective, building on a state-of-the-art transfer-learning approach that underperforms on ``distant'' languages that have little to no representation in the training corpus. Motivated by the field of syntactic typology, we introduce a general method called Expected Statistic Regularization (ESR) to regularize the parser on distant languages according to their expected typological syntax statistics. We also contribute general approaches for estimating the loss supervision parameters from the task formalism or small amounts of labeled data. We present seven broad classes of descriptive statistic families and provide extensive experimental evidence showing that using these statistics for regularization is complementary to deep learning approaches in low-resource transfer settings. In conclusion, this thesis contributes approaches for reducing the annotation cost of building applied NLP systems through the use of high-level expert knowledge to impart additional learning signal on models and cope with cheaper biased data. We publish implementations of our methods and results, so that they may facilitate future research and applications. It is our hope that the frameworks proposed in this thesis will help to democratize access to NLP for producing structured information from text in wider-reaching applications by making them faster and cheaper to build

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges