Alessandro Moschitti


2020

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The Cascade Transformer: an Application for Efficient Answer Sentence Selection
Luca Soldaini | Alessandro Moschitti
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Large transformer-based language models have been shown to be very effective in many classification tasks. However, their computational complexity prevents their use in applications requiring the classification of a large set of candidates. While previous works have investigated approaches to reduce model size, relatively little attention has been paid to techniques to improve batch throughput during inference. In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers. Each ranker is used to prune a subset of candidates in a batch, thus dramatically increasing throughput at inference time. Partial encodings from the transformer model are shared among rerankers, providing further speed-up. When compared to a state-of-the-art transformer model, our approach reduces computation by 37% with almost no impact on accuracy, as measured on two English Question Answering datasets.

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A Study on Efficiency, Accuracy and Document Structure for Answer Sentence Selection
Daniele Bonadiman | Alessandro Moschitti
Proceedings of the 28th International Conference on Computational Linguistics

An essential task of most Question Answering (QA) systems is to re-rank the set of answer candidates, i.e., Answer Sentence Selection (AS2). These candidates are typically sentences either extracted from one or more documents preserving their natural order or retrieved by a search engine. Most state-of-the-art approaches to the task use huge neural models, such as BERT, or complex attentive architectures. In this paper, we argue that by exploiting the intrinsic structure of the original rank together with an effective word-relatedness encoder, we achieve the highest accuracy among the cost-efficient models, with two orders of magnitude fewer parameters than the current state of the art. Our model takes 9.5 seconds to train on the WikiQA dataset, i.e., very fast in comparison with the 18 minutes required by a standard BERT-base fine-tuning.

2019

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A Study of Latent Structured Prediction Approaches to Passage Reranking
Iryna Haponchyk | Alessandro Moschitti
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)

The structured output framework provides a helpful tool for learning to rank problems. In this paper, we propose a structured output approach which regards rankings as latent variables. Our approach addresses the complex optimization of Mean Average Precision (MAP) ranking metric. We provide an inference procedure to find the max-violating ranking based on the decomposition of the corresponding loss. The results of our experiments on WikiQA and TREC13 datasets show that our reranking based on structured prediction is a promising research direction.

2018

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Automatic Stance Detection Using End-to-End Memory Networks
Mitra Mohtarami | Ramy Baly | James Glass | Preslav Nakov | Lluís Màrquez | Alessandro Moschitti
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present an effective end-to-end memory network model that jointly (i) predicts whether a given document can be considered as relevant evidence for a given claim, and (ii) extracts snippets of evidence that can be used to reason about the factuality of the target claim. Our model combines the advantages of convolutional and recurrent neural networks as part of a memory network. We further introduce a similarity matrix at the inference level of the memory network in order to extract snippets of evidence for input claims more accurately. Our experiments on a public benchmark dataset, FakeNewsChallenge, demonstrate the effectiveness of our approach.

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Integrating Stance Detection and Fact Checking in a Unified Corpus
Ramy Baly | Mitra Mohtarami | James Glass | Lluís Màrquez | Alessandro Moschitti | Preslav Nakov
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e.g., news websites, social media, etc.), determining the stance of each document with respect to the claim, and finally making a prediction about the claim’s factuality by aggregating the strength of the stances, while taking the reliability of the source into account. Moreover, a fact checking system should be able to explain its decision by providing relevant extracts (rationales) from the documents. Yet, this setup is not directly supported by existing datasets, which treat fact checking, document retrieval, source credibility, stance detection and rationale extraction as independent tasks. In this paper, we support the interdependencies between these tasks as annotations in the same corpus. We implement this setup on an Arabic fact checking corpus, the first of its kind.

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Adversarial Domain Adaptation for Duplicate Question Detection
Darsh Shah | Tao Lei | Alessandro Moschitti | Salvatore Romeo | Preslav Nakov
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.

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Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection
Massimo Nicosia | Alessandro Moschitti
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

State-of-the-art networks that model relations between two pieces of text often use complex architectures and attention. In this paper, instead of focusing on architecture engineering, we take advantage of small amounts of labelled data that model semantic phenomena in text to encode matching features directly in the word representations. This greatly boosts the accuracy of our reference network, while keeping the model simple and fast to train. Our approach also beats a tree kernel model that uses similar input encodings, and neural models which use advanced attention and compare-aggregate mechanisms.

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Cross-Pair Text Representations for Answer Sentence Selection
Kateryna Tymoshenko | Alessandro Moschitti
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

High-level semantics tasks, e.g., paraphrasing, textual entailment or question answering, involve modeling of text pairs. Before the emergence of neural networks, this has been mostly performed using intra-pair features, which incorporate similarity scores or rewrite rules computed between the members within the same pair. In this paper, we compute scalar products between vectors representing similarity between members of different pairs, in place of simply using a single vector for each pair. This allows us to obtain a representation specific to any pair of pairs, which delivers the state of the art in answer sentence selection. Most importantly, our approach can outperform much more complex algorithms based on neural networks.

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Supervised Clustering of Questions into Intents for Dialog System Applications
Iryna Haponchyk | Antonio Uva | Seunghak Yu | Olga Uryupina | Alessandro Moschitti
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Modern automated dialog systems require complex dialog managers able to deal with user intent triggered by high-level semantic questions. In this paper, we propose a model for automatically clustering questions into user intents to help the design tasks. Since questions are short texts, uncovering their semantics to group them together can be very challenging. We approach the problem by using powerful semantic classifiers from question duplicate/matching research along with a novel idea of supervised clustering methods based on structured output. We test our approach on two intent clustering corpora, showing an impressive improvement over previous methods for two languages/domains.

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Learning to Progressively Recognize New Named Entities with Sequence to Sequence Models
Lingzhen Chen | Alessandro Moschitti
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we propose to use a sequence to sequence model for Named Entity Recognition (NER) and we explore the effectiveness of such model in a progressive NER setting – a Transfer Learning (TL) setting. We train an initial model on source data and transfer it to a model that can recognize new NE categories in the target data during a subsequent step, when the source data is no longer available. Our solution consists in: (i) to reshape and re-parametrize the output layer of the first learned model to enable the recognition of new NEs; (ii) to leave the rest of the architecture unchanged, such that it is initialized with parameters transferred from the initial model; and (iii) to fine tune the network on the target data. Most importantly, we design a new NER approach based on sequence to sequence (Seq2Seq) models, which can intuitively work better in our progressive setting. We compare our approach with a Bidirectional LSTM, which is a strong neural NER model. Our experiments show that the Seq2Seq model performs very well on the standard NER setting and it is more robust in the progressive setting. Our approach can recognize previously unseen NE categories while preserving the knowledge of the seen data.

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Injecting Relational Structural Representation in Neural Networks for Question Similarity
Antonio Uva | Daniele Bonadiman | Alessandro Moschitti
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Effectively using full syntactic parsing information in Neural Networks (NNs) for solving relational tasks, e.g., question similarity, is still an open problem. In this paper, we propose to inject structural representations in NNs by (i) learning a model with Tree Kernels (TKs) on relatively few pairs of questions (few thousands) as gold standard (GS) training data is typically scarce, (ii) predicting labels on a very large corpus of question pairs, and (iii) pre-training NNs on such large corpus. The results on Quora and SemEval question similarity datasets show that NNs using our approach can learn more accurate models, especially after fine tuning on GS.

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A Flexible, Efficient and Accurate Framework for Community Question Answering Pipelines
Salvatore Romeo | Giovanni Da San Martino | Alberto Barrón-Cedeño | Alessandro Moschitti
Proceedings of ACL 2018, System Demonstrations

Although deep neural networks have been proving to be excellent tools to deliver state-of-the-art results, when data is scarce and the tackled tasks involve complex semantic inference, deep linguistic processing and traditional structure-based approaches, such as tree kernel methods, are an alternative solution. Community Question Answering is a research area that benefits from deep linguistic analysis to improve the experience of the community of forum users. In this paper, we present a UIMA framework to distribute the computation of cQA tasks over computer clusters such that traditional systems can scale to large datasets and deliver fast processing.

2017

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Don’t understand a measure? Learn it: Structured Prediction for Coreference Resolution optimizing its measures
Iryna Haponchyk | Alessandro Moschitti
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

An interesting aspect of structured prediction is the evaluation of an output structure against the gold standard. Especially in the loss-augmented setting, the need of finding the max-violating constraint has severely limited the expressivity of effective loss functions. In this paper, we trade off exact computation for enabling the use and study of more complex loss functions for coreference resolution. Most interestingly, we show that such functions can be (i) automatically learned also from controversial but commonly accepted coreference measures, e.g., MELA, and (ii) successfully used in learning algorithms. The accurate model comparison on the standard CoNLL-2012 setting shows the benefit of more expressive loss functions.

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Self-Crowdsourcing Training for Relation Extraction
Azad Abad | Moin Nabi | Alessandro Moschitti
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

In this paper we introduce a self-training strategy for crowdsourcing. The training examples are automatically selected to train the crowd workers. Our experimental results show an impact of 5% Improvement in terms of F1 for relation extraction task, compared to the method based on distant supervision.

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RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations
Kateryna Tymoshenko | Alessandro Moschitti | Massimo Nicosia | Aliaksei Severyn
Proceedings of ACL 2017, System Demonstrations

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Collaborative Partitioning for Coreference Resolution
Olga Uryupina | Alessandro Moschitti
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

This paper presents a collaborative partitioning algorithm—a novel ensemble-based approach to coreference resolution. Starting from the all-singleton partition, we search for a solution close to the ensemble’s outputs in terms of a task-specific similarity measure. Our approach assumes a loose integration of individual components of the ensemble and can therefore combine arbitrary coreference resolvers, regardless of their models. Our experiments on the CoNLL dataset show that collaborative partitioning yields results superior to those attained by the individual components, for ensembles of both strong and weak systems. Moreover, by applying the collaborative partitioning algorithm on top of three state-of-the-art resolvers, we obtain the best coreference performance reported so far in the literature (MELA v08 score of 64.47).

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Learning Contextual Embeddings for Structural Semantic Similarity using Categorical Information
Massimo Nicosia | Alessandro Moschitti
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Tree kernels (TKs) and neural networks are two effective approaches for automatic feature engineering. In this paper, we combine them by modeling context word similarity in semantic TKs. This way, the latter can operate subtree matching by applying neural-based similarity on tree lexical nodes. We study how to learn representations for the words in context such that TKs can exploit more focused information. We found that neural embeddings produced by current methods do not provide a suitable contextual similarity. Thus, we define a new approach based on a Siamese Network, which produces word representations while learning a binary text similarity. We set the latter considering examples in the same category as similar. The experiments on question and sentiment classification show that our semantic TK highly improves previous results.

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A Practical Perspective on Latent Structured Prediction for Coreference Resolution
Iryna Haponchyk | Alessandro Moschitti
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Latent structured prediction theory proposes powerful methods such as Latent Structural SVM (LSSVM), which can potentially be very appealing for coreference resolution (CR). In contrast, only small work is available, mainly targeting the latent structured perceptron (LSP). In this paper, we carried out a practical study comparing for the first time online learning with LSSVM. We analyze the intricacies that may have made initial attempts to use LSSVM fail, i.e., a huge training time and much lower accuracy produced by Kruskal’s spanning tree algorithm. In this respect, we also propose a new effective feature selection approach for improving system efficiency. The results show that LSP, if correctly parameterized, produces the same performance as LSSVM, being much more efficient.

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Effective shared representations with Multitask Learning for Community Question Answering
Daniele Bonadiman | Antonio Uva | Alessandro Moschitti
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

An important asset of using Deep Neural Networks (DNNs) for text applications is their ability to automatically engineering features. Unfortunately, DNNs usually require a lot of training data, especially for highly semantic tasks such as community Question Answering (cQA). In this paper, we tackle the problem of data scarcity by learning the target DNN together with two auxiliary tasks in a multitask learning setting. We exploit the strong semantic connection between selection of comments relevant to (i) new questions and (ii) forum questions. This enables a global representation for comments, new and previous questions. The experiments of our model on a SemEval challenge dataset for cQA show a 20% of relative improvement over standard DNNs.

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SemEval-2017 Task 3: Community Question Answering
Preslav Nakov | Doris Hoogeveen | Lluís Màrquez | Alessandro Moschitti | Hamdy Mubarak | Timothy Baldwin | Karin Verspoor
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

We describe SemEval–2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016: (A) Question–Comment Similarity, (B) Question–Question Similarity, (C) Question–External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A–D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A–C.

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KeLP at SemEval-2017 Task 3: Learning Pairwise Patterns in Community Question Answering
Simone Filice | Giovanni Da San Martino | Alessandro Moschitti
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the KeLP system participating in the SemEval-2017 community Question Answering (cQA) task. The system is a refinement of the kernel-based sentence pair modeling we proposed for the previous year challenge. It is implemented within the Kernel-based Learning Platform called KeLP, from which we inherit the team’s name. Our primary submission ranked first in subtask A, and third in subtasks B and C, being the only systems appearing in the top-3 ranking for all the English subtasks. This shows that the proposed framework, which has minor variations among the three subtasks, is extremely flexible and effective in tackling learning tasks defined on sentence pairs.

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Building Chatbots from Forum Data: Model Selection Using Question Answering Metrics
Martin Boyanov | Preslav Nakov | Alessandro Moschitti | Giovanni Da San Martino | Ivan Koychev
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

We propose to use question answering (QA) data from Web forums to train chat-bots from scratch, i.e., without dialog data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train seq2seq models in a more efficient way. We further improve the parameter optimization using a new model selection strategy based on QA measures. Finally, we propose to use extrinsic evaluation with respect to a QA task as an automatic evaluation method for chatbot systems. The evaluation shows that the model achieves a MAP of 63.5% on the extrinsic task. Moreover, our manual evaluation demonstrates that the model can answer correctly 49.5% of the questions when they are similar in style to how questions are asked in the forum, and 47.3% of the questions, when they are more conversational in style.

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Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model
Kateryna Tymoshenko | Daniele Bonadiman | Alessandro Moschitti
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Recent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vector Machines (SVMs) is promising as it can exploit both the syntactic patterns captured by TKs and the embeddings learned by CNNs. However, the embeddings are constructed according to a classification function, which is not directly exploitable in the preference ranking algorithm of SVMs. In this work, we propose a new hybrid approach combining preference ranking applied to TKs and pointwise ranking applied to CNNs. We show that our approach produces better results on two well-known and rather different datasets: WikiQA for answer sentence selection and SemEval cQA for comment selection in Community Question Answering.

2016

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Learning to Recognize Ancillary Information for Automatic Paraphrase Identification
Simone Filice | Alessandro Moschitti
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Convolutional Neural Networks vs. Convolution Kernels: Feature Engineering for Answer Sentence Reranking
Kateryna Tymoshenko | Daniele Bonadiman | Alessandro Moschitti
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Semi-supervised Question Retrieval with Gated Convolutions
Tao Lei | Hrishikesh Joshi | Regina Barzilay | Tommi Jaakkola | Kateryna Tymoshenko | Alessandro Moschitti | Lluís Màrquez
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Neural Attention for Learning to Rank Questions in Community Question Answering
Salvatore Romeo | Giovanni Da San Martino | Alberto Barrón-Cedeño | Alessandro Moschitti | Yonatan Belinkov | Wei-Ning Hsu | Yu Zhang | Mitra Mohtarami | James Glass
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply Long Short-Term Memory networks with an attention mechanism, which can select important parts of text for the task of similar question retrieval from community Question Answering (cQA) forums. In particular, we use the attention weights for both selecting entire sentences and their subparts, i.e., word/chunk, from shallow syntactic trees. More interestingly, we apply tree kernels to the filtered text representations, thus exploiting the implicit features of the subtree space for learning question reranking. Our results show that the attention-based pruning allows for achieving the top position in the cQA challenge of SemEval 2016, with a relatively large gap from the other participants while greatly decreasing running time.

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Selecting Sentences versus Selecting Tree Constituents for Automatic Question Ranking
Alberto Barrón-Cedeño | Giovanni Da San Martino | Salvatore Romeo | Alessandro Moschitti
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Community question answering (cQA) websites are focused on users who query questions onto an online forum, expecting for other users to provide them answers or suggestions. Unlike other social media, the length of the posted queries has no limits and queries tend to be multi-sentence elaborations combining context, actual questions, and irrelevant information. We approach the problem of question ranking: given a user’s new question, to retrieve those previously-posted questions which could be equivalent, or highly relevant. This could prevent the posting of nearly-duplicate questions and provide the user with instantaneous answers. For the first time in cQA, we address the selection of relevant text —both at sentence- and at constituent-level— for parse tree-based representations. Our supervised models for text selection boost the performance of a tree kernel-based machine learning model, allowing it to overtake the current state of the art on a recently released cQA evaluation framework.

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An Interactive System for Exploring Community Question Answering Forums
Enamul Hoque | Shafiq Joty | Lluís Màrquez | Alberto Barrón-Cedeño | Giovanni Da San Martino | Alessandro Moschitti | Preslav Nakov | Salvatore Romeo | Giuseppe Carenini
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

We present an interactive system to provide effective and efficient search capabilities in Community Question Answering (cQA) forums. The system integrates state-of-the-art technology for answer search with a Web-based user interface specifically tailored to support the cQA forum readers. The answer search module automatically finds relevant answers for a new question by exploring related questions and the comments within their threads. The graphical user interface presents the search results and supports the exploration of related information. The system is running live at http://www.qatarliving.com/betasearch/.

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LiMoSINe Pipeline: Multilingual UIMA-based NLP Platform
Olga Uryupina | Barbara Plank | Gianni Barlacchi | Francisco J. Valverde Albacete | Manos Tsagkias | Antonio Uva | Alessandro Moschitti
Proceedings of ACL-2016 System Demonstrations

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SemEval-2016 Task 3: Community Question Answering
Preslav Nakov | Lluís Màrquez | Alessandro Moschitti | Walid Magdy | Hamdy Mubarak | Abed Alhakim Freihat | Jim Glass | Bilal Randeree
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora
Alberto Barrón-Cedeño | Daniele Bonadiman | Giovanni Da San Martino | Shafiq Joty | Alessandro Moschitti | Fahad Al Obaidli | Salvatore Romeo | Kateryna Tymoshenko | Antonio Uva
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers
Simone Filice | Danilo Croce | Alessandro Moschitti | Roberto Basili
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Taking the best from the Crowd:Learning Question Passage Classification from Noisy Data
Azad Abad | Alessandro Moschitti
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

2015

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Global Thread-level Inference for Comment Classification in Community Question Answering
Shafiq Joty | Alberto Barrón-Cedeño | Giovanni Da San Martino | Simone Filice | Lluís Màrquez | Alessandro Moschitti | Preslav Nakov
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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High-Order Low-Rank Tensors for Semantic Role Labeling
Tao Lei | Yuan Zhang | Lluís Màrquez | Alessandro Moschitti | Regina Barzilay
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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On the Automatic Learning of Sentiment Lexicons
Aliaksei Severyn | Alessandro Moschitti
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Structural Representations for Learning Relations between Pairs of Texts
Simone Filice | Giovanni Da San Martino | Alessandro Moschitti
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Distributional Neural Networks for Automatic Resolution of Crossword Puzzles
Aliaksei Severyn | Massimo Nicosia | Gianni Barlacchi | Alessandro Moschitti
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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Thread-Level Information for Comment Classification in Community Question Answering
Alberto Barrón-Cedeño | Simone Filice | Giovanni Da San Martino | Shafiq Joty | Lluís Màrquez | Preslav Nakov | Alessandro Moschitti
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

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SACRY: Syntax-based Automatic Crossword puzzle Resolution sYstem
Alessandro Moschitti | Massimo Nicosia | Gianni Barlacchi
Proceedings of ACL-IJCNLP 2015 System Demonstrations

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A State-of-the-Art Mention-Pair Model for Coreference Resolution
Olga Uryupina | Alessandro Moschitti
Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

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QCRI: Answer Selection for Community Question Answering - Experiments for Arabic and English
Massimo Nicosia | Simone Filice | Alberto Barrón-Cedeño | Iman Saleh | Hamdy Mubarak | Wei Gao | Preslav Nakov | Giovanni Da San Martino | Alessandro Moschitti | Kareem Darwish | Lluís Màrquez | Shafiq Joty | Walid Magdy
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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SemEval-2015 Task 3: Answer Selection in Community Question Answering
Preslav Nakov | Lluís Màrquez | Walid Magdy | Alessandro Moschitti | Jim Glass | Bilal Randeree
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification
Aliaksei Severyn | Alessandro Moschitti
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

2014

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Opinion Mining on YouTube
Aliaksei Severyn | Alessandro Moschitti | Olga Uryupina | Barbara Plank | Katja Filippova
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Encoding Semantic Resources in Syntactic Structures for Passage Reranking
Kateryna Tymoshenko | Alessandro Moschitti | Aliaksei Severyn
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Learning to Rank Answer Candidates for Automatic Resolution of Crossword Puzzles
Gianni Barlacchi | Massimo Nicosia | Alessandro Moschitti
Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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A Study of using Syntactic and Semantic Structures for Concept Segmentation and Labeling
Iman Saleh | Scott Cyphers | Jim Glass | Shafiq Joty | Lluís Màrquez | Alessandro Moschitti | Preslav Nakov
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Alessandro Moschitti | Bo Pang | Walter Daelemans
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Learning to Differentiate Better from Worse Translations
Francisco Guzmán | Shafiq Joty | Lluís Màrquez | Alessandro Moschitti | Preslav Nakov | Massimo Nicosia
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Semantic Kernels for Semantic Parsing
Iman Saleh | Alessandro Moschitti | Preslav Nakov | Lluís Màrquez | Shafiq Joty
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Discriminative Reranking of Discourse Parses Using Tree Kernels
Shafiq Joty | Alessandro Moschitti
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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SenTube: A Corpus for Sentiment Analysis on YouTube Social Media
Olga Uryupina | Barbara Plank | Aliaksei Severyn | Agata Rotondi | Alessandro Moschitti
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper we present SenTube -- a dataset of user-generated comments on YouTube videos annotated for information content and sentiment polarity. It contains annotations that allow to develop classifiers for several important NLP tasks: (i) sentiment analysis, (ii) text categorization (relatedness of a comment to video and/or product), (iii) spam detection, and (iv) prediction of comment informativeness. The SenTube corpus favors the development of research on indexing and searching YouTube videos exploiting information derived from comments. The corpus will cover several languages: at the moment, we focus on English and Italian, with Spanish and Dutch parts scheduled for the later stages of the project. For all the languages, we collect videos for the same set of products, thus offering possibilities for multi- and cross-lingual experiments. The paper provides annotation guidelines, corpus statistics and annotator agreement details.

2013

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Automatic Feature Engineering for Answer Selection and Extraction
Aliaksei Severyn | Alessandro Moschitti
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Multilingual Mention Detection for Coreference Resolution
Olga Uryupina | Alessandro Moschitti
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Long-Distance Time-Event Relation Extraction
Alessandro Moschitti | Siddharth Patwardhan | Chris Welty
Proceedings of the Sixth International Joint Conference on Natural Language Processing

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Learning Adaptable Patterns for Passage Reranking
Aliaksei Severyn | Massimo Nicosia | Alessandro Moschitti
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

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Towards Robust Linguistic Analysis using OntoNotes
Sameer Pradhan | Alessandro Moschitti | Nianwen Xue | Hwee Tou Ng | Anders Björkelund | Olga Uryupina | Yuchen Zhang | Zhi Zhong
Proceedings of the Seventeenth Conference on Computational Natural Language Learning

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Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction
Barbara Plank | Alessandro Moschitti
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Semantic Textual Similarity with Structural Representations
Aliaksei Severyn | Massimo Nicosia | Alessandro Moschitti
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Relational Features in Fine-Grained Opinion Analysis
Richard Johansson | Alessandro Moschitti
Computational Linguistics, Volume 39, Issue 3 - September 2013

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iKernels-Core: Tree Kernel Learning for Textual Similarity
Aliaksei Severyn | Massimo Nicosia | Alessandro Moschitti
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2012

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Verb Classification using Distributional Similarity in Syntactic and Semantic Structures
Danilo Croce | Alessandro Moschitti | Roberto Basili | Martha Palmer
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Modeling Topic Dependencies in Hierarchical Text Categorization
Alessandro Moschitti | Qi Ju | Richard Johansson
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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State-of-the-Art Kernels for Natural Language Processing
Alessandro Moschitti
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

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Joint Conference on EMNLP and CoNLL - Shared Task
Sameer Pradhan | Alessandro Moschitti | Nianwen Xue
Joint Conference on EMNLP and CoNLL - Shared Task

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CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes
Sameer Pradhan | Alessandro Moschitti | Nianwen Xue | Olga Uryupina | Yuchen Zhang
Joint Conference on EMNLP and CoNLL - Shared Task

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BART goes multilingual: The UniTN / Essex submission to the CoNLL-2012 Shared Task
Olga Uryupina | Alessandro Moschitti | Massimo Poesio
Joint Conference on EMNLP and CoNLL - Shared Task

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Translating Questions to SQL Queries with Generative Parsers Discriminatively Reranked
Alessandra Giordani | Alessandro Moschitti
Proceedings of COLING 2012: Posters

2011

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Using Syntactic and Semantic Structural Kernels for Classifying Definition Questions in Jeopardy!
Alessandro Moschitti | Jennifer Chu-Carroll | Siddharth Patwardhan | James Fan | Giuseppe Riccardi
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Structured Lexical Similarity via Convolution Kernels on Dependency Trees
Danilo Croce | Alessandro Moschitti | Roberto Basili
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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A Study on Dependency Tree Kernels for Automatic Extraction of Protein-Protein Interaction
Faisal Md. Chowdhury | Alberto Lavelli | Alessandro Moschitti
Proceedings of BioNLP 2011 Workshop

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Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing
Irina Matveeva | Alessandro Moschitti | Lluís Màrquez | Fabio Massimo Zanzotto
Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing

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Joint Distant and Direct Supervision for Relation Extraction
Truc-Vien T. Nguyen | Alessandro Moschitti
Proceedings of 5th International Joint Conference on Natural Language Processing

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Extracting Opinion Expressions and Their Polarities – Exploration of Pipelines and Joint Models
Richard Johansson | Alessandro Moschitti
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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End-to-End Relation Extraction Using Distant Supervision from External Semantic Repositories
Truc-Vien T. Nguyen | Alessandro Moschitti
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

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Book Reviews: Semantic Role Labeling by Martha Palmer, Daniel Gildea and Nianwen Xue
Alessandro Moschitti
Computational Linguistics, Volume 37, Issue 3 - September 2011

2010

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Syntactic/Semantic Structures for Textual Entailment Recognition
Yashar Mehdad | Alessandro Moschitti | Fabio Massimo Zanzotto
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Proceedings of TextGraphs-5 - 2010 Workshop on Graph-based Methods for Natural Language Processing
Carmen Banea | Alessandro Moschitti | Swapna Somasundaran | Fabio Massimo Zanzotto
Proceedings of TextGraphs-5 - 2010 Workshop on Graph-based Methods for Natural Language Processing

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Syntactic and Semantic Structure for Opinion Expression Detection
Richard Johansson | Alessandro Moschitti
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

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On Reverse Feature Engineering of Syntactic Tree Kernels
Daniele Pighin | Alessandro Moschitti
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

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Reranking Models in Fine-grained Opinion Analysis
Richard Johansson | Alessandro Moschitti
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

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Kernel-based Reranking for Named-Entity Extraction
Truc-Vien T. Nguyen | Alessandro Moschitti | Giuseppe Riccardi
Coling 2010: Posters

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Coling 2010: Kernel Engineering for Fast and Easy Design of Natural Language Applications–Tutorial notes
Alessandro Moschitti
Coling 2010: Kernel Engineering for Fast and Easy Design of Natural Language Applications–Tutorial notes

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Kernel Engineering for Fast and Easy Design of Natural Language Applications
Alessandro Moschitti
Coling 2010: Kernel Engineering for Fast and Easy Design of Natural Language Applications–Tutorial notes

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A Flexible Representation of Heterogeneous Annotation Data
Richard Johansson | Alessandro Moschitti
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper describes a new flexible representation for the annotation of complex structures of metadata over heterogeneous data collections containing text and other types of media such as images or audio files. We argue that existing frameworks are not suitable for this purpose, most importantly because they do not easily generalize to multi-document and multimodal corpora, and because they often require the use of particular software frameworks. In the paper, we define a data model to represent such structured data over multimodal collections. Furthermore, we define a surface realization of the data structure as a simple and readable XML format. We present two examples of annotation tasks to illustrate how the representation and format work for complex structures involving multimodal annotation and cross-document links. The representation described here has been used in a large-scale project focusing on the annotation of a wide range of information ― from low-level features to high-level semantics ― in a multimodal data collection containing both text and images.

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A Comprehensive Resource to Evaluate Complex Open Domain Question Answering
Silvia Quarteroni | Alessandro Moschitti
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Complex Question Answering is a discipline that involves a deep understanding of question/answer relations, such as those characterizing definition and procedural questions and their answers. To contribute to the improvement of this technology, we deliver two question and answer corpora for complex questions, WEB-QA and TREC-QA, extracted by the same Question Answering system, YourQA, from the Web and from the AQUAINT-6 data collection respectively. We believe that such corpora can be useful resources to address a type of QA that is far from being efficiently solved. WEB-QA and TREC-QA are available in two formats: judgment files and training/testing files. Judgment files contain a ranked list of candidate answers to TREC-10 complex questions, extracted using YourQA as a baseline system and manually labelled according to a Likert scale from 1 (completely incorrect) to 5 (totally correct). Training and testing files contain learning instances compatible with SVM-light; these are useful for experimenting with shallow and complex structural features such as parse trees and semantic role labels. Our experiments with the above corpora have allowed to prove that structured information representation is useful to improve the accuracy of complex QA systems and to re-rank answers.

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Corpora for Automatically Learning to Map Natural Language Questions into SQL Queries
Alessandra Giordani | Alessandro Moschitti
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Automatically translating natural language into machine-readable instructions is one of major interesting and challenging tasks in Natural Language (NL) Processing. This problem can be addressed by using machine learning algorithms to generate a function that find mappings between natural language and programming language semantics. For this purpose suitable annotated and structured data are required. In this paper, we describe our method to construct and semi-automatically annotate these kinds of data, consisting of pairs of NL questions and SQL queries. Additionally, we describe two different datasets obtained by applying our annotation method to two well-known corpora, GeoQueries and RestQueries. Since we believe that syntactic levels are important, we also generate and make available relational pairs represented by means of their syntactic trees whose lexical content has been generalized. We validate the quality of our corpora by experimenting with them and our machine learning models to derive automatic NL/SQL translators. Our promising results suggest that our corpora can be effectively used to carry out research in the field of natural language interface to database.

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A General Purpose FrameNet-based Shallow Semantic Parser
Bonaventura Coppola | Alessandro Moschitti
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

In this paper we present a new FrameNet-based Shallow Semantic Parser. Shallow Semantic Parsing has been a popular Natural Language Processing task since the 2004 and 2005 CoNLL Shared Task editions on Semantic Role Labeling, which were based on the PropBank lexical-semantic resource. Nonetheless, efforts in extending such task to the FrameNet setting have been constrained by practical software engineering issues. We hereby analyze these issues, identify desirable requirements for a practical parsing framework, and show the results of our software implementation. In particular, we attempt at meeting requirements arising from both a) the need of a flexible environment supporting current ongoing research, and b) the willingness of providing an effective platform supporting preliminary application prototypes in the field. After introducing the task of FrameNet-based Shallow Semantic Parsing, we sketch the system processing workflow and summarize a set of successful experimental results, directing the reader to previous published papers for extended experiment descriptions and wider discussion of the achieved results.

2009

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Reverse Engineering of Tree Kernel Feature Spaces
Daniele Pighin | Alessandro Moschitti
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Re-Ranking Models Based-on Small Training Data for Spoken Language Understanding
Marco Dinarelli | Alessandro Moschitti | Giuseppe Riccardi
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Convolution Kernels on Constituent, Dependency and Sequential Structures for Relation Extraction
Truc-Vien T. Nguyen | Alessandro Moschitti | Giuseppe Riccardi
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Annotating Spoken Dialogs: From Speech Segments to Dialog Acts and Frame Semantics
Marco Dinarelli | Silvia Quarteroni | Sara Tonelli | Alessandro Moschitti | Giuseppe Riccardi
Proceedings of SRSL 2009, the 2nd Workshop on Semantic Representation of Spoken Language

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Efficient Linearization of Tree Kernel Functions
Daniele Pighin | Alessandro Moschitti
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

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Re-Ranking Models for Spoken Language Understanding
Marco Dinarelli | Alessandro Moschitti | Giuseppe Riccardi
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Syntactic and Semantic Kernels for Short Text Pair Categorization
Alessandro Moschitti
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

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Shallow Semantic Parsing for Spoken Language Understanding
Bonaventura Coppola | Alessandro Moschitti | Giuseppe Riccardi
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Semantic Role Labeling Systems for Arabic using Kernel Methods
Mona Diab | Alessandro Moschitti | Daniele Pighin
Proceedings of ACL-08: HLT

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Kernels on Linguistic Structures for Answer Extraction
Alessandro Moschitti | Silvia Quarteroni
Proceedings of ACL-08: HLT, Short Papers

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BART: A Modular Toolkit for Coreference Resolution
Yannick Versley | Simone Paolo Ponzetto | Massimo Poesio | Vladimir Eidelman | Alan Jern | Jason Smith | Xiaofeng Yang | Alessandro Moschitti
Proceedings of the ACL-08: HLT Demo Session

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BART: A modular toolkit for coreference resolution
Yannick Versley | Simone Ponzetto | Massimo Poesio | Vladimir Eidelman | Alan Jern | Jason Smith | Xiaofeng Yang | Alessandro Moschitti
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Developing a full coreference system able to run all the way from raw text to semantic interpretation is a considerable engineering effort. Accordingly, there is very limited availability of off-the shelf tools for researchers whose interests are not primarily in coreference or others who want to concentrate on a specific aspect of the problem. We present BART, a highly modular toolkit for developing coreference applications. In the Johns Hopkins workshop on using lexical and encyclopedic knowledge for entity disambiguation, the toolkit was used to extend a reimplementation of Soon et al.’s proposal with a variety of additional syntactic and knowledge-based features, and experiment with alternative resolution processes, preprocessing tools, and classifiers. BART has been released as open source software and is available from http://www.sfs.uni-tuebingen.de/~versley/BART

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Tree Kernels for Semantic Role Labeling
Alessandro Moschitti | Daniele Pighin | Roberto Basili
Computational Linguistics, Volume 34, Number 2, June 2008 - Special Issue on Semantic Role Labeling

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Encoding Tree Pair-Based Graphs in Learning Algorithms: The Textual Entailment Recognition Case
Alessandro Moschitti | Fabio Massimo Zanzotto
Coling 2008: Proceedings of the 3rd Textgraphs workshop on Graph-based Algorithms for Natural Language Processing

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Coreference Systems Based on Kernels Methods
Yannick Versley | Alessandro Moschitti | Massimo Poesio | Xiaofeng Yang
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Shallow Semantic in Fast Textual Entailment Rule Learners
Fabio Massimo Zanzotto | Marco Pennacchiotti | Alessandro Moschitti
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

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CUNIT: A Semantic Role Labeling System for Modern Standard Arabic
Mona Diab | Alessandro Moschitti | Daniele Pighin
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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RTV: Tree Kernels for Thematic Role Classification
Daniele Pighin | Alessandro Moschitti | Roberto Basili
Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007)

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Exploiting Syntactic and Shallow Semantic Kernels for Question Answer Classification
Alessandro Moschitti | Silvia Quarteroni | Roberto Basili | Suresh Manandhar
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Automatic Learning of Textual Entailments with Cross-Pair Similarities
Fabio Massimo Zanzotto | Alessandro Moschitti
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Semantic Role Labeling via FrameNet, VerbNet and PropBank
Ana-Maria Giuglea | Alessandro Moschitti
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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A Tree Kernel approach to Question and Answer Classification in Question Answering Systems
Alessandro Moschitti | Roberto Basili
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

A critical step in Question Answering design is the definition of the models for question focus identification and answer extraction. In case of factoid questions, we can use a question classifier (trained according to a target taxonomy) and a named entity recognizer. Unfortunately, this latter cannot be applied to generate answers related to non-factoid questions. In this paper, we tackle such problem by designing classifiers of non-factoid answers. As the feature design for this learning task is very complex, we take advantage of tree kernels to generate large feature set from the syntactic parse trees of passages relevant to the target question. Such kernels encode syntactic and lexical information in Support Vector Machines which can decide if a sentence focuses on a target taxonomy subject. The experiments with SVMs on the TREC 10 dataset show that our approach is an interesting future research.

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Tree Kernel Engineering in Semantic Role Labeling Systems
Alessandro Moschitti | Daniele Pighin | Roberto Basili
Proceedings of the Workshop on Learning Structured Information in Natural Language Applications

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Towards Free-text Semantic Parsing: A Unified Framework Based on FrameNet, VerbNet and PropBank
Ana-Maria Giuglea | Alessandro Moschitti
Proceedings of the Workshop on Learning Structured Information in Natural Language Applications

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Semantic Role Labeling via Tree Kernel Joint Inference
Alessandro Moschitti | Daniele Pighin | Roberto Basili
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

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Similarity between Pairs of Co-indexed Trees for Textual Entailment Recognition
Fabio Massimo Zanzotto | Alessandro Moschitti
Proceedings of TextGraphs: the First Workshop on Graph Based Methods for Natural Language Processing

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Making Tree Kernels Practical for Natural Language Learning
Alessandro Moschitti
11th Conference of the European Chapter of the Association for Computational Linguistics

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Syntactic Kernels for Natural Language Learning: the Semantic Role Labeling Case
Alessandro Moschitti
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers

2005

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Engineering of Syntactic Features for Shallow Semantic Parsing
Alessandro Moschitti | Bonaventura Coppola | Daniele Pighin | Roberto Basili
Proceedings of the ACL Workshop on Feature Engineering for Machine Learning in Natural Language Processing

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Effective use of WordNet Semantics via Kernel-Based Learning
Roberto Basili | Marco Cammisa | Alessandro Moschitti
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

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Hierarchical Semantic Role Labeling
Alessandro Moschitti | Ana-Maria Giuglea | Bonaventura Coppola | Roberto Basili
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

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Verb Subcategorization Kernels for Automatic Semantic Labeling
Alessandro Moschitti | Roberto Basili
Proceedings of the ACL-SIGLEX Workshop on Deep Lexical Acquisition

2004

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A Study on Convolution Kernels for Shallow Statistic Parsing
Alessandro Moschitti
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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Semantic parsing based on FrameNet
Cosmin Adrian Bejan | Alessandro Moschitti | Paul Morărescu | Gabriel Nicolae | Sanda Harabagiu
Proceedings of SENSEVAL-3, the Third International Workshop on the Evaluation of Systems for the Semantic Analysis of Text

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A Semantic Kernel for Predicate Argument Classification
Alessandro Moschitti | Cosmin Adrian Bejan
Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004

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Intentions, Implicatures and Processing of Complex Questions
Sanda Harabagiu | Steven Maiorano | Alessandro Moschitti | Cosmin Bejan
Proceedings of the Workshop on Pragmatics of Question Answering at HLT-NAACL 2004

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A Novel Approach to Focus Identification in Question/Answering Systems
Alessandro Moschitti | Sanda Harabagiu
Proceedings of the Workshop on Pragmatics of Question Answering at HLT-NAACL 2004

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