Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

Actes de la 6e conférence conjointe Journées d'Études sur la Parole (JEP, 33e édition), Traitement Automatique des Langues Naturelles (TALN, 27e édition), Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RÉCITAL, 22e édition. Volume 2 : Traitement Automatique des Langues Naturelles

@ 6ème conférence conjointe: JEP-TALN-RECITAL 2020no abstrac

Relation Classification with Limited Supervision

Large reams of unstructured data, for instance in form textual document collections containing entities and relations, exist in many domains. The process of deriving valuable domain insights and intelligence from such documents collections usually involves the extraction of information such as the relations between the entities in such collections. Relation classification is the task of detecting relations between entities. Supervised machine learning models, which have become the tool of choice for relation classification, require substantial quantities of annotated data for each relation in order to perform optimally. For many domains, such quantities of annotated data for relations may not be readily available, and manually curating such annotations may not be practical due to time and cost constraints. In this work, we develop both model-specific and model-agnostic approaches for relation classification with limited supervision. We start by proposing an approach for learning embeddings for contextual surface patterns, which are the set of surface patterns associated with entity pairs across a text corpus, to provide additional supervision signals for relation classification with limited supervision. We find that this approach improves classification performance on relations with limited supervision instances. However, this initial approach assumes the availability of at least one annotated instance per relation during training. In order to address this limitation, we propose an approach which formulates the task of relation classification as that of textual entailment. This reformulation allows us to use the textual descriptions of relations to classify their instances. It also allows us to utilize existing textual entailment datasets and models to classify relations with zero supervision instances. The two methods proposed previously rely on the use of specific model architectures for relation classification. Since a wide variety of models have been proposed for relation classification in the literature, a more general approach is thus desirable. We subsequently propose our first model-agnostic meta-learning algorithm for relation classification with limited supervision. This algorithm is applicable to any gradient-optimized relation classification model. We show that the proposed approach improves the predictive performance of two existing relation classification models when supervision for relations is limited. Next, because all the approaches we have proposed so far assume the availability of all supervision needed for classifying relations prior to model training, they are unable to handle the case when new supervision for relations becomes available after training. Such new supervision may need to be incorporated into the model to enable it classify new relations or to improve its performance on existing relations. Our last approach addresses this short-coming. We propose a model-agnostic algorithm which enables relation classification models to learn continually from new supervision as it becomes available, while doing so in a data-efficient manner and without forgetting knowledge of previous relations

Representation and Processing of Composition, Variation and Approximation in Language Resources and Tools

In my habilitation dissertation, meant to validate my capacity of and maturity for directingresearch activities, I present a panorama of several topics in computational linguistics, linguisticsand computer science.Over the past decade, I was notably concerned with the phenomena of compositionalityand variability of linguistic objects. I illustrate the advantages of a compositional approachto the language in the domain of emotion detection and I explain how some linguistic objects,most prominently multi-word expressions, defy the compositionality principles. I demonstratethat the complex properties of MWEs, notably variability, are partially regular and partiallyidiosyncratic. This fact places the MWEs on the frontiers between different levels of linguisticprocessing, such as lexicon and syntax.I show the highly heterogeneous nature of MWEs by citing their two existing taxonomies.After an extensive state-of-the art study of MWE description and processing, I summarizeMultiflex, a formalism and a tool for lexical high-quality morphosyntactic description of MWUs.It uses a graph-based approach in which the inflection of a MWU is expressed in function ofthe morphology of its components, and of morphosyntactic transformation patterns. Due tounification the inflection paradigms are represented compactly. Orthographic, inflectional andsyntactic variants are treated within the same framework. The proposal is multilingual: it hasbeen tested on six European languages of three different origins (Germanic, Romance and Slavic),I believe that many others can also be successfully covered. Multiflex proves interoperable. Itadapts to different morphological language models, token boundary definitions, and underlyingmodules for the morphology of single words. It has been applied to the creation and enrichmentof linguistic resources, as well as to morphosyntactic analysis and generation. It can be integratedinto other NLP applications requiring the conflation of different surface realizations of the sameconcept.Another chapter of my activity concerns named entities, most of which are particular types ofMWEs. Their rich semantic load turned them into a hot topic in the NLP community, which isdocumented in my state-of-the art survey. I present the main assumptions, processes and resultsissued from large annotation tasks at two levels (for named entities and for coreference), parts ofthe National Corpus of Polish construction. I have also contributed to the development of bothrule-based and probabilistic named entity recognition tools, and to an automated enrichment ofProlexbase, a large multilingual database of proper names, from open sources.With respect to multi-word expressions, named entities and coreference mentions, I pay aspecial attention to nested structures. This problem sheds new light on the treatment of complexlinguistic units in NLP. When these units start being modeled as trees (or, more generally, asacyclic graphs) rather than as flat sequences of tokens, long-distance dependencies, discontinu-ities, overlapping and other frequent linguistic properties become easier to represent. This callsfor more complex processing methods which control larger contexts than what usually happensin sequential processing. Thus, both named entity recognition and coreference resolution comesvery close to parsing, and named entities or mentions with their nested structures are analogous3to multi-word expressions with embedded complements.My parallel activity concerns finite-state methods for natural language and XML processing.My main contribution in this field, co-authored with 2 colleagues, is the first full-fledged methodfor tree-to-language correction, and more precisely for correcting XML documents with respectto a DTD. We have also produced interesting results in incremental finite-state algorithmics,particularly relevant to data evolution contexts such as dynamic vocabularies or user updates.Multilingualism is the leitmotif of my research. I have applied my methods to several naturallanguages, most importantly to Polish, Serbian, English and French. I have been among theinitiators of a highly multilingual European scientific network dedicated to parsing and multi-word expressions. I have used multilingual linguistic data in experimental studies. I believethat it is particularly worthwhile to design NLP solutions taking declension-rich (e.g. Slavic)languages into account, since this leads to more universal solutions, at least as far as nominalconstructions (MWUs, NEs, mentions) are concerned. For instance, when Multiflex had beendeveloped with Polish in mind it could be applied as such to French, English, Serbian and Greek.Also, a French-Serbian collaboration led to substantial modifications in morphological modelingin Prolexbase in its early development stages. This allowed for its later application to Polishwith very few adaptations of the existing model. Other researchers also stress the advantages ofNLP studies on highly inflected languages since their morphology encodes much more syntacticinformation than is the case e.g. in English.In this dissertation I am also supposed to demonstrate my ability of playing an active rolein shaping the scientific landscape, on a local, national and international scale. I describemy: (i) various scientific collaborations and supervision activities, (ii) roles in over 10 regional,national and international projects, (iii) responsibilities in collective bodies such as program andorganizing committees of conferences and workshops, PhD juries, and the National UniversityCouncil (CNU), (iv) activity as an evaluator and a reviewer of European collaborative projects.The issues addressed in this dissertation open interesting scientific perspectives, in whicha special impact is put on links among various domains and communities. These perspectivesinclude: (i) integrating fine-grained language data into the linked open data, (ii) deep parsingof multi-word expressions, (iii) modeling multi-word expression identification in a treebank as atree-to-language correction problem, and (iv) a taxonomy and an experimental benchmark fortree-to-language correction approaches