C. Porchietto La Stampa Torino 30.05.09
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La Russa “salva” la caserma Cernaia
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Unfortunately I do not have enough context from the given document to generate an accurate 3 sentence summary. The document contains random characters and does not convey any clear ideas or information that could be summarized.
Verb based manipuri sentiment analysis
This document describes a verb-based sentiment analysis of Manipuri language documents using conditional random fields for part-of-speech tagging and a manually annotated verb lexicon to determine sentiment polarity. The system was tested on 550 letters to newspaper editors, achieving an average recall of 72.1%, precision of 78.14%, and F-measure of 75% for sentiment classification. The authors conclude the work is an initial effort in sentiment analysis for the highly agglutinative Manipuri language and more methods are needed to improve accuracy.
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Unfortunately I do not have enough context from the given document to generate an accurate 3 sentence summary. The document contains random characters and does not convey any clear ideas or information that could be summarized.
Verb based manipuri sentiment analysis
This document describes a verb-based sentiment analysis of Manipuri language documents using conditional random fields for part-of-speech tagging and a manually annotated verb lexicon to determine sentiment polarity. The system was tested on 550 letters to newspaper editors, achieving an average recall of 72.1%, precision of 78.14%, and F-measure of 75% for sentiment classification. The authors conclude the work is an initial effort in sentiment analysis for the highly agglutinative Manipuri language and more methods are needed to improve accuracy.
HANDLING UNKNOWN WORDS IN NAMED ENTITY RECOGNITION USING TRANSLITERATION
The document discusses handling unknown words in named entity recognition using transliteration. It proposes an approach where named entities in training data are transliterated into other languages and stored in transliteration files. During testing, if an unknown entity is encountered, it is checked against the transliteration files and assigned the corresponding tag if found. The approach is shown to achieve 95.8% recall, 96.3% precision and 96.04% F-measure on a multilingual named entity recognition task handling words from English, Hindi, Marathi, Punjabi and Urdu. Performance metrics for named entity recognition systems such as precision, recall and F-measure are also discussed.
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El documento lista diferentes tipos de música y los artistas asociados con cada uno. También incluye el número de discos vendidos por cada artista y el número total de personas que escuchan cada tipo de música. En total se vendieron 11051 discos y 63 personas escuchan los diferentes tipos de música.
CLUSTERING WEB SEARCH RESULTS FOR EFFECTIVE ARABIC LANGUAGE BROWSING
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Document excel
El documento lista diferentes tipos de música y los artistas asociados con cada uno. También incluye el número de discos vendidos por cada artista y el número total de personas que escuchan cada tipo de música. En total se vendieron 11051 discos y 63 personas escuchan los diferentes tipos de música.
CLUSTERING WEB SEARCH RESULTS FOR EFFECTIVE ARABIC LANGUAGE BROWSING
The process of browsing Search Results is one of the major problems with traditional Web search engines
for English, European, and any other languages generally, and for Arabic Language particularly. This
process is absolutely time consuming and the browsing style seems to be unattractive. Organizing Web
search results into clusters facilitates users quick browsing through search results. Traditional clustering
techniques (data-centric clustering algorithms) are inadequate since they don't generate clusters with
highly readable names or cluster labels. To solve this problem, Description-centric algorithms such as
Suffix Tree Clustering (STC) algorithm have been introduced and used successfully and extensively with
different adapted versions for English, European, and Chinese Languages. However, till the day of writing
this paper, in our knowledge, STC algorithm has been never applied for Arabic Web Snippets Search
Results Clustering.
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HANDLING UNKNOWN WORDS IN NAMED ENTITY RECOGNITION USING TRANSLITERATION
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