Hidden Myth: Structure and Symbolism in Advertising

Real-time Linked Dataspaces: Enabling Data Ecosystems for Intelligent Systems

This publication has emanated from research funded in part from the European
Union’s Horizon 2020 research and innovation programme under grant agreement
No 645425 SSIX and Science Foundation
Ireland (SFI) under Grant Number SFI/12/RC/2289.
We would like in particular to thank Alexandros Poulis and
Juha Vilhunen from the Global Services for Machine Intelligence Group, Lionbridge Finland 6
ensuring the production word of high quality translations for our similarity
datasets.

SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages

In recent years word embedding/distributional semantic models evolved to become a fundamental component in many natural language processing (NLP) architectures due to their ability of capturing and quantifying semantic associations at scale. Word embedding models can be used to satisfy recurrent tasks in NLP such as lexical and semantic generalisation in machine learning tasks, finding similar or related words and computing semantic relatedness of terms. However, building and consuming specific word embedding models require the setting of a large set of configurations, such as corpus-dependant parameters, distance measures as well as compositional models. Despite their increasing relevance as a component in NLP architectures, existing frameworks provide limited options in their ability to systematically build, parametrise, compare and evaluate different models. To answer this demand, this paper describes INDRA, a multi-lingual word embedding/distributional semantics framework which supports the creation, use and evaluation of word embedding models. In addition to the tool, INDRA also shares more than 65 pre-computed models in 14 languages.

Indra: A Word Embedding and Semantic Relatedness Server

This task proposes a challenge to support the interaction between users and applications, micro-services and software APIs using natural language. The task aims for supporting the evaluation and evolution of the discussions surrounding the natural language processing approaches within the context of end-user natural language programming, under scenarios of high semantic heterogeneity/gap.

SemEval-2017 Task 11: End-User Development using Natural Language

Insight Centre for Data Analytics Digital Enterprise Research Institute (DERI) Doctor of Philosophy Schema-Agnostic Queries for Large-Schema Databases: A Distributional Semantics Approach by André Freitas The evolution of data environments towards the growth in the size, complexity, dynamicity and decentralisation (SCoDD) of schemas drastically impacts contemporary data management. The SCoDD trend emerges as a central data management concern in Big Data scenarios, where users and applications have a demand for more complete data, produced by independent data sources, under different semantic assumptions and contexts of use. Most Database Management Systems (DBMSs) today target a closed communication scenario, where the symbolic schema of the database is known a priori by the database user, which is able to interpret it in an unambiguous way. The context in which the data is consumed and produced is well-defined and it is typically the same context in which the data was created. In contrast, data management under the SCoDD conditions target an open communication scenario where the symbolic system of the database is unknown by the user and multiple interpretation contexts are possible. In this case the database can be created under a different context from the database user. The emergence of this new data environment demands the revisit of the semantic assumptions behind databases and the design of data access mechanisms which can support semantically heterogeneous (open communication) data environments. This work aims at filling this gap by proposing a complementary semantic model for databases, based on distributional semantic models. Distributional semantics provides a complementary perspective to the formal perspective of database semantics, which supports semantic approximation as a first-class database operation. Differently from models which describe uncertain and incomplete data or probabilistic databases, distributionalrelational models focuses on the construction of conceptual approximation approaches for databases, supported by a comprehensive semantic model automatically built from large-scale unstructured data external to the database, which serves as a semantic/commonsense knowledge base. The semantic model can be used to support schema-agnostic queries, i.e. abstracting the data consumer from a specific conceptualization behind the data. The proposed distributional-relational semantic model is supported by a distributional structured vector space model, named τ −Space, which represents structured data under a distributional semantic model representation which, in coordination with a query planning approach, supports a schema-agnostic query mechanism for large-schema databases. The query mechanism is materialized in the Treo query engine and is evaluated using schema-agnostic natural language queries. The evaluation of the query mechanism confirms that distributional semantics provides a high-recall, medium-high precision, and low maintainability solution to cope with the abstraction and conceptual-level differences in schema-agnostic queries over largeschema/schema-less open domain datasets. Moreover, the compositional semantic model defined by the query planning mechanism supports expressive schema-agnostic queries over large-schema/schema-less open domain datasets. The proposed distributionalrelational structured vector space model (τ − Space) materialized as an inverted index, supports the development of a schema-agnostic query mechanism with interactive query execution time.

Schema-agnostic queries for large-schema databases: A distributional semantics approach

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Schema-agnostic queries for large-schema databases: A distributional semantics approach