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Found 33 papers, 10 papers with code
Is this Enough?-Evaluation of Malayalam Wordnet
Quality of a product is the degree to which a product meets the customer’s expectation, which must also be valid for the case of lexical semantic resources.
Building the Mongolian WordNet
The manual evaluation of the resource1 estimated its quality at 96. 4%.
ZiNet: Linking Chinese Characters Spanning Three Thousand Years
In addition, powered by the knowledge of radical systems in ZiNet, this paper introduces glyph similarity measurement between ancient Chinese characters, which could capture similar glyph pairs that are potentially related in origins or semantics.
MorphyNet: a Large Multilingual Database of Derivational and Inflectional Morphology
Large-scale morphological databases provide essential input to a wide range of NLP applications.
Deep Attention Diffusion Graph Neural Networks for Text Classification
Text classification is a fundamental task with broad applications in natural language processing.
A Taxonomic Classification of WordNet Polysemy Types
WordNet represents polysemous terms by capturing the different meanings of these terms at the lexical level, but without giving emphasis on the polysemy types such terms belong to.
The SIGMORPHON 2022 Shared Task on Morpheme Segmentation
The SIGMORPHON 2022 shared task on morpheme segmentation challenged systems to decompose a word into a sequence of morphemes and covered most types of morphology: compounds, derivations, and inflections.
Concept-level Debugging of Part-Prototype Networks
Part-prototype Networks (ProtoPNets) are concept-based classifiers designed to achieve the same performance as black-box models without compromising transparency.
Lifelong Personal Context Recognition
We focus on the development of AIs which live in lifelong symbiosis with a human.
UniMorph 4.0: Universal Morphology
The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema.
Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship
We capture the phenomenon of diversity through the notions of lexical gap and language-specific word and use a systematic method to infer gaps semi-automatically on a large scale.
Language Diversity: Visible to Humans, Exploitable by Machines
The Universal Knowledge Core (UKC) is a large multilingual lexical database with a focus on language diversity and covering over a thousand languages.
Visual Ground Truth Construction as Faceted Classification
Recent work in Machine Learning and Computer Vision has provided evidence of systematic design flaws in the development of major object recognition benchmark datasets.
Object Recognition as Classification via Visual Properties
We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification.
Toward a Unified Framework for Debugging Concept-based Models
In this paper, we tackle interactive debugging of "gray-box" concept-based models (CBMs).
Streaming and Learning the Personal Context
The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efficiency.
Interactive Label Cleaning with Example-based Explanations
We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples.
Classifying concepts via visual properties
We assume that substances in the world are represented by two types of concepts, namely substance concepts and classification concepts, the former instrumental to (visual) perception, the latter to (language based) classification.
iTelos -- Purpose Driven Knowledge Graph Generation
When building a new application we are more and more confronted with the need of reusing and integrating pre-existing knowledge, e. g., ontologies, schemas, data of any kind, from multiple sources.
Stratified Data Integration
We propose a novel approach to the problem of semantic heterogeneity where data are organized into a set of stratified and independent representation layers, namely: conceptual(where a set of unique alinguistic identifiers are connected inside a graph codifying their meaning), language(where sets of synonyms, possibly from multiple languages, annotate concepts), knowledge(in the form of a graph where nodes are entity types and links are properties), and data(in the form of a graph of entities populating the previous knowledge graph).
Towards Visual Semantics
In this paper we provide a theory and an algorithm for how to build substance concepts which are in a one-to-one correspondence with classifications concepts, thus paving the way to the seamless integration between natural language descriptions and visual perception.
Towards Algorithmic Transparency: A Diversity Perspective
As the role of algorithmic systems and processes increases in society, so does the risk of bias, which can result in discrimination against individuals and social groups.
Topological Regularization for Graph Neural Networks Augmentation
The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision.
Human-in-the-loop Handling of Knowledge Drift
Motivated by this, we introduce TRCKD, a novel approach that combines automated drift detection and adaptation with an interactive stage in which the user is asked to disambiguate between different kinds of KD.
Exploring the Language of Data
We set out to uncover the unique grammatical properties of an important yet so far under-researched type of natural language text: that of short labels typically found within structured datasets.
Multi-Modal Subjective Context Modelling and Recognition
Second, existing models typically assume that context is objective, whereas in most applications context is best viewed from the user's perspective.
Learning in the Wild with Incremental Skeptical Gaussian Processes
The ability to learn from human supervision is fundamental for personal assistants and other interactive applications of AI.
A Major Wordnet for a Minority Language: Scottish Gaelic
We present a new wordnet resource for Scottish Gaelic, a Celtic minority language spoken by about 60, 000 speakers, most of whom live in Northwestern Scotland.
Continual egocentric object recognition
We present a framework capable of tackilng the problem of continual object recognition in a setting which resembles that under whichhumans see and learn.
CogNet: A Large-Scale Cognate Database
This paper introduces CogNet, a new, large-scale lexical database that provides cognates -words of common origin and meaning- across languages.
TrentoTeam at SemEval-2017 Task 3: An application of Grice Maxims in Ranking Community Question Answers
In this paper we present the Tren-toTeam system which participated to thetask 3 at SemEval-2017 (Nakov et al., 2017). We concentrated our work onapplying Grice Maxims(used in manystate-of-the-art Machine learning applica-tions(Vogel et al., 2013; Kheirabadiand Aghagolzadeh, 2012; Dale and Re-iter, 1995; Franke, 2011)) to ranking an-swers of a question by answers relevancy. Particularly, we created a ranker systembased on relevancy scores, assigned by 3main components: Named entity recogni-tion, similarity score, sentiment analysis. Our system obtained a comparable resultsto Machine learning systems.
Compositional Learning of Relation Path Embedding for Knowledge Base Completion
In this paper, we study the problem of how to better embed entities and relations of knowledge bases into different low-dimensional spaces by taking full advantage of the additional semantics of relation paths, and we propose a compositional learning model of relation path embedding (RPE).
