Alfio Gliozzo

Also published as: Alfio M. Gliozzo, Alfio Massimiliano Gliozzo, Alfio Massimiliano Gliozzo


2020

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Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer
Chao Shang | Sarthak Dash | Md. Faisal Mahbub Chowdhury | Nandana Mihindukulasooriya | Alfio Gliozzo
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Extracting lexico-semantic relations as graph-structured taxonomies, also known as taxonomy construction, has been beneficial in a variety of NLP applications. Recently Graph Neural Network (GNN) has shown to be powerful in successfully tackling many tasks. However, there has been no attempt to exploit GNN to create taxonomies. In this paper, we propose Graph2Taxo, a GNN-based cross-domain transfer framework for the taxonomy construction task. Our main contribution is to learn the latent features of taxonomy construction from existing domains to guide the structure learning of an unseen domain. We also propose a novel method of directed acyclic graph (DAG) generation for taxonomy construction. Specifically, our proposed Graph2Taxo uses a noisy graph constructed from automatically extracted noisy hyponym hypernym candidate pairs, and a set of taxonomies for some known domains for training. The learned model is then used to generate taxonomy for a new unknown domain given a set of terms for that domain. Experiments on benchmark datasets from science and environment domains show that our approach attains significant improvements correspondingly over the state of the art.

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Span Selection Pre-training for Question Answering
Michael Glass | Alfio Gliozzo | Rishav Chakravarti | Anthony Ferritto | Lin Pan | G P Shrivatsa Bhargav | Dinesh Garg | Avi Sil
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

BERT (Bidirectional Encoder Representations from Transformers) and related pre-trained Transformers have provided large gains across many language understanding tasks, achieving a new state-of-the-art (SOTA). BERT is pretrained on two auxiliary tasks: Masked Language Model and Next Sentence Prediction. In this paper we introduce a new pre-training task inspired by reading comprehension to better align the pre-training from memorization to understanding. Span Selection PreTraining (SSPT) poses cloze-like training instances, but rather than draw the answer from the model’s parameters, it is selected from a relevant passage. We find significant and consistent improvements over both BERT-BASE and BERT-LARGE on multiple Machine Reading Comprehension (MRC) datasets. Specifically, our proposed model has strong empirical evidence as it obtains SOTA results on Natural Questions, a new benchmark MRC dataset, outperforming BERT-LARGE by 3 F1 points on short answer prediction. We also show significant impact in HotpotQA, improving answer prediction F1 by 4 points and supporting fact prediction F1 by 1 point and outperforming the previous best system. Moreover, we show that our pre-training approach is particularly effective when training data is limited, improving the learning curve by a large amount.

2019

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Automatic Taxonomy Induction and Expansion
Nicolas Rodolfo Fauceglia | Alfio Gliozzo | Sarthak Dash | Md. Faisal Mahbub Chowdhury | Nandana Mihindukulasooriya
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

The Knowledge Graph Induction Service (KGIS) is an end-to-end knowledge induction system. One of its main capabilities is to automatically induce taxonomies from input documents using a hybrid approach that takes advantage of linguistic patterns, semantic web and neural networks. KGIS allows the user to semi-automatically curate and expand the induced taxonomy through a component called Smart SpreadSheet by exploiting distributional semantics. In this paper, we describe these taxonomy induction and expansion features of KGIS. A screencast video demonstrating the system is available in https://ibm.box.com/v/emnlp-2019-demo .

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Learning Relational Representations by Analogy using Hierarchical Siamese Networks
Gaetano Rossiello | Alfio Gliozzo | Robert Farrell | Nicolas Fauceglia | Michael Glass
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. Following this idea, we collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. We leverage this dataset to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. We evaluate our model in a one-shot learning task by showing a promising generalization capability in order to classify unseen relation types, which makes this approach suitable to perform automatic knowledge base population with minimal supervision. Moreover, the model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a downstream relation extraction task.

2018

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Discovering Implicit Knowledge with Unary Relations
Michael Glass | Alfio Gliozzo
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

State-of-the-art relation extraction approaches are only able to recognize relationships between mentions of entity arguments stated explicitly in the text and typically localized to the same sentence. However, the vast majority of relations are either implicit or not sententially localized. This is a major problem for Knowledge Base Population, severely limiting recall. In this paper we propose a new methodology to identify relations between two entities, consisting of detecting a very large number of unary relations, and using them to infer missing entities. We describe a deep learning architecture able to learn thousands of such relations very efficiently by using a common deep learning based representation. Our approach largely outperforms state of the art relation extraction technology on a newly introduced web scale knowledge base population benchmark, that we release to the research community.

2016

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Joint Learning of Local and Global Features for Entity Linking via Neural Networks
Thien Huu Nguyen | Nicolas Fauceglia | Mariano Rodriguez Muro | Oktie Hassanzadeh | Alfio Massimiliano Gliozzo | Mohammad Sadoghi
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Previous studies have highlighted the necessity for entity linking systems to capture the local entity-mention similarities and the global topical coherence. We introduce a novel framework based on convolutional neural networks and recurrent neural networks to simultaneously model the local and global features for entity linking. The proposed model benefits from the capacity of convolutional neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. Our evaluation on multiple datasets demonstrates the effectiveness of the model and yields the state-of-the-art performance on such datasets. In addition, we examine the entity linking systems on the domain adaptation setting that further demonstrates the cross-domain robustness of the proposed model.

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An Entity-Focused Approach to Generating Company Descriptions
Gavin Saldanha | Or Biran | Kathleen McKeown | Alfio Gliozzo
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2014

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Lexical Substitution for the Medical Domain
Martin Riedl | Michael Glass | Alfio Gliozzo
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Word Semantic Representations using Bayesian Probabilistic Tensor Factorization
Jingwei Zhang | Jeremy Salwen | Michael Glass | Alfio Gliozzo
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2013

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Semantic Technologies in IBM Watson
Alfio Gliozzo | Or Biran | Siddharth Patwardhan | Kathleen McKeown
Proceedings of the Fourth Workshop on Teaching NLP and CL

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JoBimText Visualizer: A Graph-based Approach to Contextualizing Distributional Similarity
Chris Biemann | Bonaventura Coppola | Michael R. Glass | Alfio Gliozzo | Matthew Hatem | Martin Riedl
Proceedings of TextGraphs-8 Graph-based Methods for Natural Language Processing

2012

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Natural Language Processing in Watson
Alfio M. Gliozzo | Aditya Kalyanpur | James Fan
Tutorial Abstracts at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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When Did that Happen? — Linking Events and Relations to Timestamps
Dirk Hovy | James Fan | Alfio Gliozzo | Siddharth Patwardhan | Christopher Welty
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

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Structured Term Recognition in Medical Text
Michael Glass | Alfio Gliozzo
Proceedings of COLING 2012

2009

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Bridging Languages by SuperSense Entity Tagging
Davide Picca | Alfio Massimiliano Gliozzo | Simone Campora
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

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Kernel Methods for Minimally Supervised WSD
Claudio Giuliano | Alfio Massimiliano Gliozzo | Carlo Strapparava
Computational Linguistics, Volume 35, Number 4, December 2009

2008

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LMM: an OWL-DL MetaModel to Represent Heterogeneous Lexical Knowledge
Davide Picca | Alfio Massimiliano Gliozzo | Aldo Gangemi
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper we present a Linguistic Meta-Model (LMM) allowing a semiotic-cognitive representation of knowledge. LMM is freely available and integrates the schemata of linguistic knowledge resources, such as WordNet and FrameNet, as well as foundational ontologies, such as DOLCE and its extensions. In addition, LMM is able to deal with multilinguality and to represent individuals and facts in an open domain perspective.

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Supersense Tagger for Italian
Davide Picca | Alfio Massimiliano Gliozzo | Massimiliano Ciaramita
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In this paper we present the procedure we followed to develop the Italian Super Sense Tagger. In particular, we adapted the English SuperSense Tagger to the Italian Language by exploiting a parallel sense labeled corpus for training. As for English, the Italian tagger uses a fixed set of 26 semantic labels, called supersenses, achieving a slightly lower accuracy due to the lower quality of the Italian training data. Both taggers accomplish the same task of identifying entities and concepts belonging to a common set of ontological types. This parallelism allows us to define effective methodologies for a broad range of cross-language knowledge acquisition tasks

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Instance-Based Ontology Population Exploiting Named-Entity Substitution
Claudio Giuliano | Alfio Gliozzo
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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The Domain Restriction Hypothesis: Relating Term Similarity and Semantic Consistency
Alfio Massimiliano Gliozzo | Marco Pennacchiotti | Patrick Pantel
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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FBK-irst: Lexical Substitution Task Exploiting Domain and Syntagmatic Coherence
Claudio Giuliano | Alfio Gliozzo | Carlo Strapparava
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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Instance Based Lexical Entailment for Ontology Population
Claudio Giuliano | Alfio Gliozzo
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Direct Word Sense Matching for Lexical Substitution
Ido Dagan | Oren Glickman | Alfio Gliozzo | Efrat Marmorshtein | Carlo Strapparava
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Exploiting Comparable Corpora and Bilingual Dictionaries for Cross-Language Text Categorization
Alfio Gliozzo | Carlo Strapparava
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Syntagmatic Kernels: a Word Sense Disambiguation Case Study
Claudio Giuliano | Alfio Gliozzo | Carlo Strapparava
Proceedings of the Workshop on Learning Structured Information in Natural Language Applications

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The GOD model
Alfio Massimiliano Gliozzo
Demonstrations

2005

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Investigating Unsupervised Learning for Text Categorization Bootstrapping
Alfio Gliozzo | Carlo Strapparava | Ido Dagan
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

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Domain Kernels for Word Sense Disambiguation
Alfio Gliozzo | Claudio Giuliano | Carlo Strapparava
Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05)

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Domain Kernels for Text Categorization
Alfio Gliozzo | Carlo Strapparava
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

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Cross Language Text Categorization by Acquiring Multilingual Domain Models from Comparable Corpora
Alfio Gliozzo | Carlo Strapparava
Proceedings of the ACL Workshop on Building and Using Parallel Texts

2004

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Pattern abstraction and term similarity for Word Sense Disambiguation: IRST at Senseval-3
Carlo Strapparava | Alfio Gliozzo | Claudio Giuliano
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

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Unsupervised Domain Relevance Estimation for Word Sense Disambiguation
Alfio Gliozzo | Bernardo Magnini | Carlo Strapparava
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

2001

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Using Domain Information for Word Sense Disambiguation
Bernardo Magnini | Carlo Strapparava | Giovanni Pezzulo | Alfio Gliozzo
Proceedings of SENSEVAL-2 Second International Workshop on Evaluating Word Sense Disambiguation Systems