Experiments on applying relaxation labeling to map multilingual hierarchies

This paper explores the automatic construction of a multilingual Lexical Knowledge Base from preexisting lexical resources. This paper presents a new approach for linking already existing hierarchies. The Relaxation labeling algorithm is used to select --among all the candidate connections proposed by a bilingual dictionary-- the right conection for each node in the taxonomy.Postprint (published version

Mapping Persian Words to WordNet Synsets

Lexical ontologies are one of the main resources for developing natural language processing and semantic web applications. Mapping lexical ontologies of different languages is very important for inter-lingual tasks. On the other hand mapping approaches can be implied to build lexical ontologies for a new language based on pre-existing resources of other languages. In this paper we propose a semantic approach for mapping Persian words to Princeton WordNet Synsets. As there is no lexical ontology for Persian, our approach helps not only in building one for this language but also enables semantic web applications on Persian documents. To do the mapping, we calculate the similarity of Persian words and English synsets using their features such as super-classes and subclasses, domain and related words. Our approach is an improvement of an existing one applying in a new domain, which increases the recall noticeably

WordNets mapped to Central Ontology

The deliverable describes the progress that has been made toward ontologizing the OntoWordNet with respect to the ontological meta properties of rigidity and non-rigidty, and the progress that has been made toward mapping the Dutch, English, and Italian wordnets onto OntoWordNet. We give a typology of wordnet to ontology mappings, and finally we provide a preliminary discussion of cross lingual rigidity validation within the context of the KYOTO framework

Modélisation des comportements de recherche basé sur les interactions des utilisateurs

Les utilisateurs de systèmes d'information divisent normalement les tâches en une séquence de plusieurs étapes pour les résoudre. En particulier, les utilisateurs divisent les tâches de recherche en séquences de requêtes, en interagissant avec les systèmes de recherche pour mener à bien le processus de recherche d'informations. Les interactions des utilisateurs sont enregistrées dans des journaux de requêtes, ce qui permet de développer des modèles pour apprendre automatiquement les comportements de recherche à partir des interactions des utilisateurs avec les systèmes de recherche. Ces modèles sont à la base de multiples applications d'assistance aux utilisateurs qui aident les systèmes de recherche à être plus interactifs, faciles à utiliser, et cohérents. Par conséquent, nous proposons les contributions suivantes : un modèle neuronale pour apprendre à détecter les limites des tâches de recherche dans les journaux de requête ; une architecture de regroupement profond récurrent qui apprend simultanément les représentations de requête et regroupe les requêtes en tâches de recherche ; un modèle non supervisé et indépendant d'utilisateur pour l'identification des tâches de recherche prenant en charge les requêtes dans seize langues ; et un modèle de tâche de recherche multilingue, une approche non supervisée qui modélise simultanément l'intention de recherche de l'utilisateur et les tâches de recherche. Les modèles proposés améliorent les méthodes existantes de modélisation, en tenant compte de la confidentialité des utilisateurs, des réponses en temps réel et de l'accessibilité linguistique. Le respect de la vie privée de l'utilisateur est une préoccupation majeure, tandis que des réponses rapides sont essentielles pour les systèmes de recherche qui interagissent avec les utilisateurs en temps réel, en particulier dans la recherche par conversation. Dans le même temps, l'accessibilité linguistique est essentielle pour aider les utilisateurs du monde entier, qui interagissent avec les systèmes de recherche dans de nombreuses langues. Les contributions proposées peuvent bénéficier à de nombreuses applications d'assistance aux utilisateurs, en aidant ces derniers à mieux résoudre leurs tâches de recherche lorsqu'ils accèdent aux systèmes de recherche pour répondre à leurs besoins d'information.Users of information systems normally divide tasks in a sequence of multiple steps to solve them. In particular, users divide search tasks into sequences of queries, interacting with search systems to carry out the information seeking process. User interactions are registered on search query logs, enabling the development of models to automatically learn search patterns from the users' interactions with search systems. These models underpin multiple user assisting applications that help search systems to be more interactive, user-friendly, and coherent. User assisting applications include query suggestion, the ranking of search results based on tasks, query reformulation analysis, e-commerce applications, retrieval of advertisement, query-term prediction, mapping of queries to search tasks, and so on. Consequently, we propose the following contributions: a neural model for learning to detect search task boundaries in query logs; a recurrent deep clustering architecture that simultaneously learns query representations through self-training, and cluster queries into groups of search tasks; Multilingual Graph-Based Clustering, an unsupervised, user-agnostic model for search task identification supporting queries in sixteen languages; and Language-agnostic Search Task Model, an unsupervised approach that simultaneously models user search intent and search tasks. Proposed models improve on existing methods for modeling user interactions, taking into account user privacy, realtime response times, and language accessibility. User privacy is a major concern in Ethics for intelligent systems, while fast responses are critical for search systems interacting with users in realtime, particularly in conversational search. At the same time, language accessibility is essential to assist users worldwide, who interact with search systems in many languages. The proposed contributions can benefit many user assisting applications, helping users to better solve their search tasks when accessing search systems to fulfill their information needs

SEAL: Simultaneous Label Hierarchy Exploration And Learning

Label hierarchy is an important source of external knowledge that can enhance classification performance. However, most existing methods rely on predefined label hierarchies that may not match the data distribution. To address this issue, we propose Simultaneous label hierarchy Exploration And Learning (SEAL), a new framework that explores the label hierarchy by augmenting the observed labels with latent labels that follow a prior hierarchical structure. Our approach uses a 1-Wasserstein metric over the tree metric space as an objective function, which enables us to simultaneously learn a data-driven label hierarchy and perform (semi-)supervised learning. We evaluate our method on several datasets and show that it achieves superior results in both supervised and semi-supervised scenarios and reveals insightful label structures. Our implementation is available at https://github.com/tzq1999/SEAL

The Role of Linguistics in Probing Task Design

Over the past decades natural language processing has evolved from a niche research area into a fast-paced and multi-faceted discipline that attracts thousands of contributions from academia and industry and feeds into real-world applications. Despite the recent successes, natural language processing models still struggle to generalize across domains, suffer from biases and lack transparency. Aiming to get a better understanding of how and why modern NLP systems make their predictions for complex end tasks, a line of research in probing attempts to interpret the behavior of NLP models using basic probing tasks. Linguistic corpora are a natural source of such tasks, and linguistic phenomena like part of speech, syntax and role semantics are often used in probing studies. The goal of probing is to find out what information can be easily extracted from a pre-trained NLP model or representation. To ensure that the information is extracted from the NLP model and not learned during the probing study itself, probing models are kept as simple and transparent as possible, exposing and augmenting conceptual inconsistencies between NLP models and linguistic resources. In this thesis we investigate how linguistic conceptualization can affect probing models, setups and results. In Chapter 2 we investigate the gap between the targets of classical type-level word embedding models like word2vec, and the items of lexical resources and similarity benchmarks. We show that the lack of conceptual alignment between word embedding vocabularies and lexical resources penalizes the word embedding models in both benchmark-based and our novel resource-based evaluation scenario. We demonstrate that simple preprocessing techniques like lemmatization and POS tagging can partially mitigate the issue, leading to a better match between word embeddings and lexicons. Linguistics often has more than one way of describing a certain phenomenon. In Chapter 3 we conduct an extensive study of the effects of lingustic formalism on probing modern pre-trained contextualized encoders like BERT. We use role semantics as an excellent example of a data-rich multi-framework phenomenon. We show that the choice of linguistic formalism can affect the results of probing studies, and deliver additional insights on the impact of dataset size, domain, and task architecture on probing. Apart from mere labeling choices, linguistic theories might differ in the very way of conceptualizing the task. Whereas mainstream NLP has treated semantic roles as a categorical phenomenon, an alternative, prominence-based view opens new opportunities for probing. In Chapter 4 we investigate prominence-based probing models for role semantics, incl. semantic proto-roles and our novel regression-based role probe. Our results indicate that pre-trained language models like BERT might encode argument prominence. Finally, we propose an operationalization of thematic role hierarchy - a widely used linguistic tool to describe syntactic behavior of verbs, and show that thematic role hierarchies can be extracted from text corpora and transfer cross-lingually. The results of our work demonstrate the importance of linguistic conceptualization for probing studies, and highlight the dangers and the opportunities associated with using linguistics as a meta-langauge for NLP model interpretation

Nordic and Baltic wordnets aligned and compared through “WordTies”

Peer reviewe