Measuring the semantic similarity of texts

Corley, Courtney D.; Mihalcea, Rada

doi:10.3115/1631862.1631865

Cited by 233 publications

(153 citation statements)

References 16 publications

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“…In line with many other researches (e.g., [5]), we determine these anchors using different similarity or relatedness models: the exact matching between tokens or lemmas, a similarity between tokens based on their edit distance, the derivationally related form relation and the verb entailment relation in WordNet, and, finally, a WordNet-based similarity [6]. Each of these detectors gives a different weight to the anchor: 0/1 for the first and the similarity value for all the others.…”

Section: Training Examples As Pairs Of Co-indexed Treessupporting

confidence: 64%

“…We compared three different kernels: (1) the kernel K l ((T , H ), (T , H )) = sim l (T , H ) × sim l (T , H ) which is based on the intra-pair lexical similarity sim l (T, H) described in [5]. (2) The kernel K l + K s that combines our kernel with the lexical-similarity-based kernel.…”

Section: Experimented Kernelsmentioning

confidence: 99%

“…-WordNet 2.0 [11] to extract both the verbs in entailment, Ent set, and the derivationally related words, Der set. -The wn::similarity package [12] to compute the Jiang&Conrath (J&C) distance [6] as in [5]. This is one of the best figure method which provides a similarity score in the [0, 1] interval.…”

Section: Experimental Settingsmentioning

confidence: 99%

See 2 more Smart Citations

Experimenting a “General Purpose” Textual Entailment Learner in AVE

Zanzotto

Moschitti

2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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Abstract. In this paper we present the use of a "general purpose" textual entailment recognizer in the Answer Validation Exercise (AVE) task. Our system is designed to learn entailment rules from annotated examples. Its main feature is the use of Support Vector Machines (SVMs) with kernel functions based on cross-pair similarity between entailment pairs. We experimented with our system using different training sets: RTE and AVE data sets. The comparative results show that entailment rules can be learned. Although, the high variability of the outcome prevents us to derive definitive conclusions, the results show that our approach is quite promising and improvable in the future.

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Section: Training Examples As Pairs Of Co-indexed Treessupporting

confidence: 64%

Section: Experimented Kernelsmentioning

confidence: 99%

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Experimenting a “General Purpose” Textual Entailment Learner in AVE

Zanzotto

Moschitti

2007

Evaluation of Multilingual and Multi-Modal Information Retrieval

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“…The open source project WordNet::Similarity 5 implements all of these measures, and was used to compute the similarity scores [20]. As the focus in this study is on the comparison of short segments of text, rather than individual words, the word similarity scores are combined using the technique developed by Corley and Mihalcea [6]. Since the OSM Wiki website holds about 1,900 concept definitions, the complete, symmetric similarity matrix for OSM concepts would contain about 1.8 million rankings.…”

Section: Discussionmentioning

confidence: 99%

The Similarity Jury: Combining Expert Judgements on Geographic Concepts

Ballatore

Wilson²,

Bertolotto

2012

Lecture Notes in Computer Science

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Abstract. A cognitively plausible measure of semantic similarity between geographic concepts is valuable across several areas, including geographic information retrieval, data mining, and ontology alignment. Semantic similarity measures are not intrinsically right or wrong, but obtain a certain degree of cognitive plausibility in the context of a given application. A similarity measure can therefore be seen as a domain expert summoned to judge the similarity of a pair of concepts according to her subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we first define the similarity jury as a panel of experts having to reach a decision on the semantic similarity of a set of geographic concepts. Second, we have conducted an evaluation of 8 WordNet-based semantic similarity measures on a subset of OpenStreetMap geographic concepts. This empirical evidence indicates that a jury tends to perform better than individual experts, but the best expert often outperforms the jury. In some cases, the jury obtains higher cognitive plausibility than its best expert.

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“…Anti-plagiarism techniques rely on the distributional hypothesis to detect suspiciously close citation patterns and similarities in writing styles across different text documents. More recently, the problem of paraphrase detection has become an active research area (Corley and Mihalcea 2005). For example, the sentence 'The Iraqi Foreign Minister warned of disastrous consequences if Turkey launched an invasion of Iraq' should be classified as a paraphrase of 'Iraq has warned that a Turkish incursion would have disastrous results' (Fernando and Stevenson 2008, p. 2).…”

Section: Text-to-text Semantic Similaritymentioning

confidence: 99%

Computing the semantic similarity of geographic terms using volunteered lexical definitions

Ballatore

Wilson

Bertolotto

2013

International Journal of Geographical Information Science

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Volunteered geographic information (VGI) is generated by heterogenous 'information communities' that co-operate to produce reusable units of geographic knowledge. A consensual lexicon is a key factor to enable this open production model. Lexical definitions help demarcate the boundaries of terms, forming a thin semantic ground on which knowledge can travel. In VGI, lexical definitions often appear to be inconsistent, circular, noisy and highly idiosyncratic. Computing the semantic similarity of these 'volunteered lexical definitions' has a wide range of applications in GIScience, including information retrieval, data mining and information integration. This article describes a knowledge-based approach to quantify the semantic similarity of lexical definitions. Grounded in the recursive intuition that similar terms are described using similar terms, the approach relies on paraphrase-detection techniques and the lexical database WordNet. The cognitive plausibility of the approach is evaluated in the context of the OpenStreetMap (OSM) Semantic Network, obtaining high correlation with human judgements. Guidelines are provided for the practical usage of the approach.

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Measuring the semantic similarity of texts

Cited by 233 publications

References 16 publications

Experimenting a “General Purpose” Textual Entailment Learner in AVE

Experimenting a “General Purpose” Textual Entailment Learner in AVE

The Similarity Jury: Combining Expert Judgements on Geographic Concepts

Computing the semantic similarity of geographic terms using volunteered lexical definitions

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