André Freitas

Also published as: Andre Freitas


2022

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To be or not to be an Integer? Encoding Variables for Mathematical Text
Deborah Ferreira | Mokanarangan Thayaparan | Marco Valentino | Julia Rozanova | Andre Freitas
Findings of the Association for Computational Linguistics: ACL 2022

The application of Natural Language Inference (NLI) methods over large textual corpora can facilitate scientific discovery, reducing the gap between current research and the available large-scale scientific knowledge. However, contemporary NLI models are still limited in interpreting mathematical knowledge written in Natural Language, even though mathematics is an integral part of scientific argumentation for many disciplines. One of the fundamental requirements towards mathematical language understanding, is the creation of models able to meaningfully represent variables. This problem is particularly challenging since the meaning of a variable should be assigned exclusively from its defining type, i.e., the representation of a variable should come from its context. Recent research has formalised the variable typing task, a benchmark for the understanding of abstract mathematical types and variables in a sentence. In this work, we propose VarSlot, a Variable Slot-based approach, which not only delivers state-of-the-art results in the task of variable typing, but is also able to create context-based representations for variables.

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Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective
Edoardo Manino | Julia Rozanova | Danilo Carvalho | Andre Freitas | Lucas Cordeiro
Findings of the Association for Computational Linguistics: ACL 2022

Metamorphic testing has recently been used to check the safety of neural NLP models. Its main advantage is that it does not rely on a ground truth to generate test cases. However, existing studies are mostly concerned with robustness-like metamorphic relations, limiting the scope of linguistic properties they can test. We propose three new classes of metamorphic relations, which address the properties of systematicity, compositionality and transitivity. Unlike robustness, our relations are defined over multiple source inputs, thus increasing the number of test cases that we can produce by a polynomial factor. With them, we test the internal consistency of state-of-the-art NLP models, and show that they do not always behave according to their expected linguistic properties. Lastly, we introduce a novel graphical notation that efficiently summarises the inner structure of metamorphic relations.

2021

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What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP
Oskar Wysocki | Malina Florea | Dónal Landers | André Freitas
Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems

SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems. This paper provides a systematic quantitative analysis of SemEval aiming to evidence the patterns of the contributions behind SemEval. By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.

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Switching Contexts: Transportability Measures for NLP
Guy Marshall | Mokanarangan Thayaparan | Philip Osborne | André Freitas
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

This paper explores the topic of transportability, as a sub-area of generalisability. By proposing the utilisation of metrics based on well-established statistics, we are able to estimate the change in performance of NLP models in new contexts. Defining a new measure for transportability may allow for better estimation of NLP system performance in new domains, and is crucial when assessing the performance of NLP systems in new tasks and domains. Through several instances of increasing complexity, we demonstrate how lightweight domain similarity measures can be used as estimators for the transportability in NLP applications. The proposed transportability measures are evaluated in the context of Named Entity Recognition and Natural Language Inference tasks.

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Encoding Explanatory Knowledge for Zero-shot Science Question Answering
Zili Zhou | Marco Valentino | Donal Landers | André Freitas
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms. We demonstrate that N-XKT is able to improve accuracy and generalization on science Question Answering (QA). Specifically, by leveraging facts from background explanatory knowledge corpora, the N-XKT model shows a clear improvement on zero-shot QA. Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset, enabling faster convergence and more accurate results. A systematic analysis is conducted to quantitatively analyze the performance of the N-XKT model and the impact of different categories of knowledge on the zero-shot generalization task.

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Do Natural Language Explanations Represent Valid Logical Arguments? Verifying Entailment in Explainable NLI Gold Standards
Marco Valentino | Ian Pratt-Hartmann | André Freitas
Proceedings of the 14th International Conference on Computational Semantics (IWCS)

An emerging line of research in Explainable NLP is the creation of datasets enriched with human-annotated explanations and rationales, used to build and evaluate models with step-wise inference and explanation generation capabilities. While human-annotated explanations are used as ground-truth for the inference, there is a lack of systematic assessment of their consistency and rigour. In an attempt to provide a critical quality assessment of Explanation Gold Standards (XGSs) for NLI, we propose a systematic annotation methodology, named Explanation Entailment Verification (EEV), to quantify the logical validity of human-annotated explanations. The application of EEV on three mainstream datasets reveals the surprising conclusion that a majority of the explanations, while appearing coherent on the surface, represent logically invalid arguments, ranging from being incomplete to containing clearly identifiable logical errors. This conclusion confirms that the inferential properties of explanations are still poorly formalised and understood, and that additional work on this line of research is necessary to improve the way Explanation Gold Standards are constructed.

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Supporting Context Monotonicity Abstractions in Neural NLI Models
Julia Rozanova | Deborah Ferreira | Mokanarangan Thayaparan | Marco Valentino | André Freitas
Proceedings of the 1st and 2nd Workshops on Natural Logic Meets Machine Learning (NALOMA)

Natural language contexts display logical regularities with respect to substitutions of related concepts: these are captured in a functional order-theoretic property called monotonicity. For a certain class of NLI problems where the resulting entailment label depends only on the context monotonicity and the relation between the substituted concepts, we build on previous techniques that aim to improve the performance of NLI models for these problems, as consistent performance across both upward and downward monotone contexts still seems difficult to attain even for state of the art models. To this end, we reframe the problem of context monotonicity classification to make it compatible with transformer-based pre-trained NLI models and add this task to the training pipeline. Furthermore, we introduce a sound and complete simplified monotonicity logic formalism which describes our treatment of contexts as abstract units. Using the notions in our formalism, we adapt targeted challenge sets to investigate whether an intermediate context monotonicity classification task can aid NLI models’ performance on examples exhibiting monotonicity reasoning.

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Explainable Inference Over Grounding-Abstract Chains for Science Questions
Mokanarangan Thayaparan | Marco Valentino | André Freitas
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders
Giangiacomo Mercatali | André Freitas
Findings of the Association for Computational Linguistics: EMNLP 2021

The ability of learning disentangled representations represents a major step for interpretable NLP systems as it allows latent linguistic features to be controlled. Most approaches to disentanglement rely on continuous variables, both for images and text. We argue that despite being suitable for image datasets, continuous variables may not be ideal to model features of textual data, due to the fact that most generative factors in text are discrete. We propose a Variational Autoencoder based method which models language features as discrete variables and encourages independence between variables for learning disentangled representations. The proposed model outperforms continuous and discrete baselines on several qualitative and quantitative benchmarks for disentanglement as well as on a text style transfer downstream application.

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Unification-based Reconstruction of Multi-hop Explanations for Science Questions
Marco Valentino | Mokanarangan Thayaparan | André Freitas
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

This paper presents a novel framework for reconstructing multi-hop explanations in science Question Answering (QA). While existing approaches for multi-hop reasoning build explanations considering each question in isolation, we propose a method to leverage explanatory patterns emerging in a corpus of scientific explanations. Specifically, the framework ranks a set of atomic facts by integrating lexical relevance with the notion of unification power, estimated analysing explanations for similar questions in the corpus. An extensive evaluation is performed on the Worldtree corpus, integrating k-NN clustering and Information Retrieval (IR) techniques. We present the following conclusions: (1) The proposed method achieves results competitive with Transformers, yet being orders of magnitude faster, a feature that makes it scalable to large explanatory corpora (2) The unification-based mechanism has a key role in reducing semantic drift, contributing to the reconstruction of many hops explanations (6 or more facts) and the ranking of complex inference facts (+12.0 Mean Average Precision) (3) Crucially, the constructed explanations can support downstream QA models, improving the accuracy of BERT by up to 10% overall.

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STAR: Cross-modal [STA]tement [R]epresentation for selecting relevant mathematical premises
Deborah Ferreira | André Freitas
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Mathematical statements written in natural language are usually composed of two different modalities: mathematical elements and natural language. These two modalities have several distinct linguistic and semantic properties. State-of-the-art representation techniques have demonstrated an inability in capturing such an entangled style of discourse. In this work, we propose STAR, a model that uses cross-modal attention to learn how to represent mathematical text for the task of Natural Language Premise Selection. This task uses conjectures written in both natural and mathematical language to recommend premises that most likely will be relevant to prove a particular statement. We found that STAR not only outperforms baselines that do not distinguish between natural language and mathematical elements, but it also achieves better performance than state-of-the-art models.

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Does My Representation Capture X? Probe-Ably
Deborah Ferreira | Julia Rozanova | Mokanarangan Thayaparan | Marco Valentino | André Freitas
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models. Naive probing studies may have misleading results, but various recent works have suggested more reliable methodologies that compensate for the possible pitfalls of probing. However, these best practices are numerous and fast-evolving. To simplify the process of running a set of probing experiments in line with suggested methodologies, we introduce Probe-Ably: an extendable probing framework which supports and automates the application of probing methods to the user’s inputs.

2020

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Premise Selection in Natural Language Mathematical Texts
Deborah Ferreira | André Freitas
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The discovery of supporting evidence for addressing complex mathematical problems is a semantically challenging task, which is still unexplored in the field of natural language processing for mathematical text. The natural language premise selection task consists in using conjectures written in both natural language and mathematical formulae to recommend premises that most likely will be useful to prove a particular statement. We propose an approach to solve this task as a link prediction problem, using Deep Convolutional Graph Neural Networks. This paper also analyses how different baselines perform in this task and shows that a graph structure can provide higher F1-score, especially when considering multi-hop premise selection.

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Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text
Deborah Ferreira | André Freitas
Proceedings of the 12th Language Resources and Evaluation Conference

Mathematical text is written using a combination of words and mathematical expressions. This combination, along with a specific way of structuring sentences makes it challenging for state-of-art NLP tools to understand and reason on top of mathematical discourse. In this work, we propose a new NLP task, the natural premise selection, which is used to retrieve supporting definitions and supporting propositions that are useful for generating an informal mathematical proof for a particular statement. We also make available a dataset, NL-PS, which can be used to evaluate different approaches for the natural premise selection task. Using different baselines, we demonstrate the underlying interpretation challenges associated with the task.

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A Framework for Evaluation of Machine Reading Comprehension Gold Standards
Viktor Schlegel | Marco Valentino | Andre Freitas | Goran Nenadic | Riza Batista-Navarro
Proceedings of the 12th Language Resources and Evaluation Conference

Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph of text. While neural MRC systems gain popularity and achieve noticeable performance, issues are being raised with the methodology used to establish their performance, particularly concerning the data design of gold standards that are used to evaluate them. There is but a limited understanding of the challenges present in this data, which makes it hard to draw comparisons and formulate reliable hypotheses. As a first step towards alleviating the problem, this paper proposes a unifying framework to systematically investigate the present linguistic features, required reasoning and background knowledge and factual correctness on one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other hand. We propose a qualitative annotation schema for the first and a set of approximative metrics for the latter. In a first application of the framework, we analyse modern MRC gold standards and present our findings: the absence of features that contribute towards lexical ambiguity, the varying factual correctness of the expected answers and the presence of lexical cues, all of which potentially lower the reading comprehension complexity and quality of the evaluation data.

2019

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MinWikiSplit: A Sentence Splitting Corpus with Minimal Propositions
Christina Niklaus | André Freitas | Siegfried Handschuh
Proceedings of the 12th International Conference on Natural Language Generation

We compiled a new sentence splitting corpus that is composed of 203K pairs of aligned complex source and simplified target sentences. Contrary to previously proposed text simplification corpora, which contain only a small number of split examples, we present a dataset where each input sentence is broken down into a set of minimal propositions, i.e. a sequence of sound, self-contained utterances with each of them presenting a minimal semantic unit that cannot be further decomposed into meaningful propositions. This corpus is useful for developing sentence splitting approaches that learn how to transform sentences with a complex linguistic structure into a fine-grained representation of short sentences that present a simple and more regular structure which is easier to process for downstream applications and thus facilitates and improves their performance.

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DisSim: A Discourse-Aware Syntactic Text Simplification Framework for English and German
Christina Niklaus | Matthias Cetto | André Freitas | Siegfried Handschuh
Proceedings of the 12th International Conference on Natural Language Generation

We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications. For this purpose, we turn input sentences into a two-layered semantic hierarchy in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them. In that way, we preserve the coherence structure of the input and, hence, its interpretability for downstream tasks.

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Identifying and Explaining Discriminative Attributes
Armins Stepanjans | André Freitas
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Identifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task. This paper describes an explicit word vector representation model (WVM) to support the identification of discriminative attributes. A core contribution of the paper is a quantitative and qualitative comparative analysis of different types of data sources and Knowledge Bases in the construction of explainable and explicit WVMs: (i) knowledge graphs built from dictionary definitions, (ii) entity-attribute-relationships graphs derived from images and (iii) commonsense knowledge graphs. Using a detailed quantitative and qualitative analysis, we demonstrate that these data sources have complementary semantic aspects, supporting the creation of explicit semantic vector spaces. The explicit vector spaces are evaluated using the task of discriminative attribute identification, showing comparable performance to the state-of-the-art systems in the task (F1-score = 0.69), while delivering full model transparency and explainability.

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Identifying Supporting Facts for Multi-hop Question Answering with Document Graph Networks
Mokanarangan Thayaparan | Marco Valentino | Viktor Schlegel | André Freitas
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

Recent advances in reading comprehension have resulted in models that surpass human performance when the answer is contained in a single, continuous passage of text. However, complex Question Answering (QA) typically requires multi-hop reasoning - i.e. the integration of supporting facts from different sources, to infer the correct answer. This paper proposes Document Graph Network (DGN), a message passing architecture for the identification of supporting facts over a graph-structured representation of text. The evaluation on HotpotQA shows that DGN obtains competitive results when compared to a reading comprehension baseline operating on raw text, confirming the relevance of structured representations for supporting multi-hop reasoning.

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DBee: A Database for Creating and Managing Knowledge Graphs and Embeddings
Viktor Schlegel | André Freitas
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

This paper describes DBee, a database to support the construction of data-intensive AI applications. DBee provides a unique data model which operates jointly over large-scale knowledge graphs (KGs) and embedding vector spaces (VSs). This model supports queries which exploit the semantic properties of both types of representations (KGs and VSs). Additionally, DBee aims to facilitate the construction of KGs and VSs, by providing a library of generators, which can be used to create, integrate and transform data into KGs and VSs.

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Transforming Complex Sentences into a Semantic Hierarchy
Christina Niklaus | Matthias Cetto | André Freitas | Siegfried Handschuh
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a two-layered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall. To enable reproducible research, all code is provided online.

2018

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Indra: A Word Embedding and Semantic Relatedness Server
Juliano Efson Sales | Leonardo Souza | Siamak Barzegar | Brian Davis | André Freitas | Siegfried Handschuh
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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A Multilingual Test Collection for the Semantic Search of Entity Categories
Juliano Efson Sales | Siamak Barzegar | Wellington Franco | Bernhard Bermeitinger | Tiago Cunha | Brian Davis | André Freitas | Siegfried Handschuh
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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The SSIX Corpora: Three Gold Standard Corpora for Sentiment Analysis in English, Spanish and German Financial Microblogs
Thomas Gaillat | Manel Zarrouk | André Freitas | Brian Davis
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Building a Knowledge Graph from Natural Language Definitions for Interpretable Text Entailment Recognition
Vivian Silva | André Freitas | Siegfried Handschuh
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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SemR-11: A Multi-Lingual Gold-Standard for Semantic Similarity and Relatedness for Eleven Languages
Siamak Barzegar | Brian Davis | Manel Zarrouk | Siegfried Handschuh | Andre Freitas
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Graphene: Semantically-Linked Propositions in Open Information Extraction
Matthias Cetto | Christina Niklaus | André Freitas | Siegfried Handschuh
Proceedings of the 27th International Conference on Computational Linguistics

We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.

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A Survey on Open Information Extraction
Christina Niklaus | Matthias Cetto | André Freitas | Siegfried Handschuh
Proceedings of the 27th International Conference on Computational Linguistics

We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction. We present the major challenges that such systems face, show the evolution of the suggested approaches over time and depict the specific issues they address. In addition, we provide a critique of the commonly applied evaluation procedures for assessing the performance of Open IE systems and highlight some directions for future work.

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Graphene: a Context-Preserving Open Information Extraction System
Matthias Cetto | Christina Niklaus | André Freitas | Siegfried Handschuh
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

We introduce Graphene, an Open IE system whose goal is to generate accurate, meaningful and complete propositions that may facilitate a variety of downstream semantic applications. For this purpose, we transform syntactically complex input sentences into clean, compact structures in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them in order to maintain their semantic relationship. In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions.

2017

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SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News
Keith Cortis | André Freitas | Tobias Daudert | Manuela Huerlimann | Manel Zarrouk | Siegfried Handschuh | Brian Davis
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper discusses the “Fine-Grained Sentiment Analysis on Financial Microblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two tracks, where the first one concerns Microblog messages and the second one covers News Statements and Headlines. The main goal behind both tracks was to predict the sentiment score for each of the mentioned companies/stocks. The sentiment scores for each text instance adopted floating point values in the range of -1 (very negative/bearish) to 1 (very positive/bullish), with 0 designating neutral sentiment. This task attracted a total of 32 participants, with 25 participating in Track 1 and 29 in Track 2.

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SemEval-2017 Task 11: End-User Development using Natural Language
Juliano Sales | Siegfried Handschuh | André Freitas
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

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.

2016

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Semantic Relation Classification: Task Formalisation and Refinement
Vivian Santos | Manuela Huerliman | Brian Davis | Siegfried Handschuh | André Freitas
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded, allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.

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Categorization of Semantic Roles for Dictionary Definitions
Vivian Silva | Siegfried Handschuh | André Freitas
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

Understanding the semantic relationships between terms is a fundamental task in natural language processing applications. While structured resources that can express those relationships in a formal way, such as ontologies, are still scarce, a large number of linguistic resources gathering dictionary definitions is becoming available, but understanding the semantic structure of natural language definitions is fundamental to make them useful in semantic interpretation tasks. Based on an analysis of a subset of WordNet’s glosses, we propose a set of semantic roles that compose the semantic structure of a dictionary definition, and show how they are related to the definition’s syntactic configuration, identifying patterns that can be used in the development of information extraction frameworks and semantic models.

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A Compositional-Distributional Semantic Model for Searching Complex Entity Categories
Juliano Efson Sales | André Freitas | Brian Davis | Siegfried Handschuh
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

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A Sentence Simplification System for Improving Relation Extraction
Christina Niklaus | Bernhard Bermeitinger | Siegfried Handschuh | André Freitas
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

We present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems. As syntactically complex sentences often pose a challenge for current Open RE approaches, we have developed a simplification framework that performs a pre-processing step by taking a single sentence as input and using a set of syntactic-based transformation rules to create a textual input that is easier to process for subsequently applied Open RE systems.

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NNBlocks: A Deep Learning Framework for Computational Linguistics Neural Network Models
Frederico Tommasi Caroli | André Freitas | João Carlos Pereira da Silva | Siegfried Handschuh
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Lately, with the success of Deep Learning techniques in some computational linguistics tasks, many researchers want to explore new models for their linguistics applications. These models tend to be very different from what standard Neural Networks look like, limiting the possibility to use standard Neural Networks frameworks. This work presents NNBlocks, a new framework written in Python to build and train Neural Networks that are not constrained by a specific kind of architecture, making it possible to use it in computational linguistics.

2015

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How hard is this query? Measuring the Semantic Complexity of Schema-agnostic Queries
André Freitas | Juliano Efson Sales | Siegfried Handschuh | Edward Curry
Proceedings of the 11th International Conference on Computational Semantics