A machine learning approach to textual entailment recognition

Zanzotto, Fabio Massimo; Pennacchiotti, Marco; Moschitti, Alessandro

doi:10.1017/s1351324909990143

Cited by 54 publications

(21 citation statements)

References 26 publications

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“…However, their methods lack the use of important relational information between a question and a candidate answer, which is essential to learn accurate relational patterns. In this respect, a solution based on enumerating relational links was given in [55,56] for the textual entailment task but it is computationally too expensive for the large dataset of QA. Some faster versions were provided in [32,54], which may be worth to try.…”

Section: Related Workmentioning

confidence: 99%

Building structures from classifiers for passage reranking

Severyn

Nicosia

Moschitti

2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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This paper shows that learning to rank models can be applied to automatically learn complex patterns, such as relational semantic structures occurring in questions and their answer passages. This is achieved by providing the learning algorithm with a tree representation derived from the syntactic trees of questions and passages connected by relational tags, where the latter are again provided by the means of automatic classifiers, i.e., question and focus classifiers and Named Entity Recognizers. This way effective structural relational patterns are implicitly encoded in the representation and can be automatically utilized by powerful machine learning models such as kernel methods.We conduct an extensive experimental evaluation of our models on well-known benchmarks from the question answer (QA) track of TREC challenges. The comparison with stateof-the-art systems and BM25 show a relative improvement in MAP of more than 14% and 45%, respectively. Further comparison on the task restricted to the answer sentence reranking shows an improvement in MAP of more than 8% over the state of the art.

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Section: Related Workmentioning

confidence: 99%

Building structures from classifiers for passage reranking

Severyn

Nicosia

Moschitti

2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

Self Cite

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“…Other approaches map the two texts to a vector space model, where each word is mapped to strongly co-occurring words in the corpus (Mitchell & Lapata, 2008), and then similarity measures over those vectors are applied. Some measure syntactic similarity by applying graph similarity measure on the syntactic dependency graphs of the two texts (Zanzotto, Pennacchiotti, & Moschitti, 2009). Similarly, other methods measure the semantic distance similarity between the words in text (Haghighi, 2005), usually exploiting other resources such as WordNet as well.…”

Section: Textual Entailment Recognitionmentioning

confidence: 99%

Learning to Predict from Textual Data

Radinsky¹,

Davidovich²,

Markovitch³

2012

jair

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Given a current news event, we tackle the problem of generating plausible predictions of future events it might cause. We present a new methodology for modeling and predicting such future news events using machine learning and data mining techniques. Our Pundit algorithm generalizes examples of causality pairs to infer a causality predictor. To obtain precisely labeled causality examples, we mine 150 years of news articles and apply semantic natural language modeling techniques to headlines containing certain predefined causality patterns. For generalization, the model uses a vast number of world knowledge ontologies. Empirical evaluation on real news articles shows that our Pundit algorithm performs as well as non-expert humans

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“…The problem of determining the similarity between texts has been widely studied for a variety of problems and a range of techniques have been developed [Seo and Croft 2008;Bendersky and Croft 2009;Mohler and Mihalcea 2009;Zanzotto et al 2009]. However, these have only limited applications to CH data.…”

Section: Introductionmentioning

confidence: 99%

Computing similarity between items in a digital library of cultural heritage

Αλέτρας

Stevenson

Clough

2012

J. Comput. Cult. Herit.

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Large amounts of cultural heritage content have now been digitized and are available in digital libraries. However, these are often unstructured and difficult to navigate. Automatic techniques for identifying similar items in these collections could be used to improve navigation since it would allow items that are implicitly connected to be linked together and allow sets of similar items to be clustered. Europeana is a large digital library containing more than 20 million digital objects from a set of cultural heritage providers throughout Europe. The diverse nature of this collection means that the items do not have standard metadata to assist navigation.A range of methods for computing the similarity between pairs of texts are applied to metadata records in Europeana in order to estimate the similarity between items. Various methods for computing similarity have been proposed and can be classified into two main approaches: (1) knowledge-based, which make use of external knowledge sources and (2) corpus-based approaches, which rely on analyzing the frequency distributions of words in documents. Both techniques are evaluated against manual judgements obtained for this study and a multiple-choice test created from manually generated categories in cultural heritage collections. We find that a combination of corpus and knowledge-based approaches provide the best results in both experiments. ACM Reference Format:Aletras, N., Stevenson, M., and Clough, P. 2012. Computing similarity between items in a digital library of cultural heritage. ACM J. Comput. Cult.

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A machine learning approach to textual entailment recognition

Cited by 54 publications

References 26 publications

Building structures from classifiers for passage reranking

Building structures from classifiers for passage reranking

Learning to Predict from Textual Data

Computing similarity between items in a digital library of cultural heritage

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