PMING Distance: A Collaborative Semantic Proximity Measure

Franzoni, Valentina; Milani, Alfredo

doi:10.1109/wi-iat.2012.226

Cited by 36 publications

(20 citation statements)

References 12 publications

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“…Previous experiments [3] [37] show that PMING is one of the measures with the best overall performance for clustering and classification of concepts. Conf() (i.e.…”

Section: Web-based Proximitymentioning

confidence: 99%

“…Any search engine (e.g. Bing, Google, Yahoo) [13][14][15][16], and any web-based semantic proximity measure [3][5] (e.g. Confidence, PMI, NGD, PMING Distance), can be used in this approach, where normalized distances are 1-proximity:…”

Section: Web-based Proximitymentioning

confidence: 99%

“…PMING Distance (PMING) [3][37] consists of NGD and PMI locally normalized, with a correction factor of weight ρ, which depends on the differential of NGD and PMI.…”

Section: Web-based Proximitymentioning

confidence: 99%

See 2 more Smart Citations

Web-based similarity for emotion recognition in web objects

Biondi

Franzoni

et al. 2016

Proceedings of the 9th International Conference on Utility and Cloud Computing

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In this project we propose a new approach for emotion recognition using web-based similarity (e.g. confidence, PMI and PMING). We aim to extract basic emotions from short sentences with emotional content (e.g. news titles, tweets, captions), performing a web-based quantitative evaluation of semantic proximity between each word of the analyzed sentence and each emotion of a psychological model (e.g. Plutchik, Ekman, Lovheim). The phases of the extraction include: text preprocessing (tokenization, stop words, filtering), search engine automated query, HTML parsing of results (i.e. scraping), estimation of semantic proximity, ranking of emotions according to proximity measures. The main idea is that, since it is possible to generalize semantic similarity under the assumption that similar concepts co-occur in documents indexed in search engines, therefore also emotions can be generalized in the same way, through tags or terms that express them in a particular language, ranking emotions. Training results are compared to human evaluation, then additional comparative tests on results are performed, both for the global ranking correlation (e.g. Kendall, Spearman, Pearson) both for the evaluation of the emotion linked to each single word. Different from sentiment analysis, our approach works at a deeper level of abstraction, aiming at recognizing specific emotions and not only the positive/negative sentiment, in order to predict emotions as semantic data.

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“…Previous experiments [3] [37] show that PMING is one of the measures with the best overall performance for clustering and classification of concepts. Conf() (i.e.…”

Section: Web-based Proximitymentioning

confidence: 99%

Section: Web-based Proximitymentioning

confidence: 99%

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Web-based similarity for emotion recognition in web objects

Biondi

Franzoni

et al. 2016

Proceedings of the 9th International Conference on Utility and Cloud Computing

Self Cite

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“…In f inequality. [2] consi factor of weight ρ, which de two component measures ar based on the context of eval More formally, the PMIN f(x)≥f(y), as a function PMIN 1 and w2 are independent, positive if they are positiv hey are negatively correlated. quency data, PMI does not provide reliable results.…”

Section: Pointwise Mutual Informa Mutual Information (Mi) Is Variablesmentioning

confidence: 99%

“…In particular the use of a semantic proximity measure, the PMING distance [2,37], is proposed and experimented. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Collective Evolutionary Concept Distance Based Query Expansion for Effective Web Document Retrieval

Leung

Milani

et al. 2013

Lecture Notes in Computer Science

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In this work several semantic approaches to concept-based query expansion and\ud reranking schemes are studied and compared with different ontology-based\ud expansion methods in web document search and retrieval. In particular, we focus\ud on concept-based query expansion schemes, where, in order to effectively\ud increase the precision of web document retrieval and to decrease the users\ud browsing time, the main goal is to quickly provide users with the most suitable\ud query expansion. Two key tasks for query expansion in web document retrieval\ud are to find the expansion candidates, as the closest concepts in web document\ud domain, and to rank the expanded queries properly. The approach we propose aims\ud at improving the expansion phase for better web document retrieval and\ud precision. The basic idea is to measure the distance between candidate concepts\ud using the PMING distance, a collaborative semantic proximity measure, i.e. a\ud measure which can be computed by using statistical results from web search\ud engine. Experiments show that the proposed technique can provide users with\ud more satisfying expansion results and improve the quality of web document\ud retrieval

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Text Classification for Italian Proficiency Evaluation

Milani

Spina

Santucci

et al. 2019

Computational Science and Its Applications – ICCSA 2019

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NLP technologies and components have an increasing diffusion in mass analysis of text based dialogues, such as classifiers for sentiment polarity, trends clustering of online messages and hate speech detection. In this work we present the design and the implementation an automatic classification tool for the evaluation of the complexity of Italian texts as understood by a speaker of Italian as a second language. The classification is done within the Common European Framework of Reference for Languages (CEFR) which aims at classifying speakers language proficiency. Results of preliminary experiments on a data set of real texts, annotated by experts and used in actual CEFR exam sessions, show a strong ability of the proposed system to label texts with the correct language proficiency class and a great potential for its integration in learning tools, such systems supporting examiners in tests design and automatic evaluation of writing abilities.

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PMING Distance: A Collaborative Semantic Proximity Measure

Cited by 36 publications

References 12 publications

Web-based similarity for emotion recognition in web objects

Web-based similarity for emotion recognition in web objects

Collective Evolutionary Concept Distance Based Query Expansion for Effective Web Document Retrieval

Text Classification for Italian Proficiency Evaluation

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