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- 678 creator feiyu-xu.
- 678 creator xiwen-cheng.
- 678 type InProceedings.
- 678 label "Fine-grained Opinion Topic and Polarity Identification".
- 678 sameAs 678.
- 678 abstract "This paper presents OMINE, an opinion mining system which aims to identify concepts such as products and their attributes, and analyze their corresponding polarities. Our work pioneers at linking extracted topic terms with domain-specific concepts. Compared with previous work, taking advantage of ontological techniques, OMINE achieves 10% higher recall with the same level precision on the topic extraction task. In addition, making use of opinion patterns for sentiment analysis, OMINE improves the performance of the backup system (NGram) around 6% for positive reviews and 8% for negative ones.".
- 678 hasAuthorList authorList.
- 678 hasTopic Linguistics.
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- 678 keyword "Acquisition, Machine Learning".
- 678 keyword "Information Extraction, Information Retrieval".
- 678 keyword "Text mining".
- 678 title "Fine-grained Opinion Topic and Polarity Identification".
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- 679 creator jean-tavernier.
- 679 creator michelle-vanni.
- 679 creator rosa-cowan.
- 679 type InProceedings.
- 679 label "Holy Moses! Leveraging Existing Tools and Resources for Entity Translation".
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- 679 abstract "Recently, there has been an emphasis on creating shared resources for natural language processing applications. This has resulted in the development of high-quality tools and data, which can then be leveraged by the research community as components for novel systems. In this paper, we reuse an open source machine translation framework to create an Arabic-to-English entity translation system. The system first translates known entity mentions using a standard phrase-based statistical machine translation framework, which is then reused to perform name transliteration on unknown mentions. In order to transliterate names more accurately, we introduce an algorithm to augment a names database with name origin and frequency information from existing data resources. Origin information is used to learn name origin classifiers and origin-specific transliteration models, while frequency information is used to select amongst n-best transliteration candidates. This work demonstrates the feasibility and benefit of adapting such data resources and shows how off-the-shelf tools and data resources can be repurposed to rapidly create a system outside their original domain.".
- 679 hasAuthorList authorList.
- 679 hasTopic Linguistics.
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- 679 keyword "Information Extraction, Information Retrieval".
- 679 keyword "Machine Translation, SpeechToSpeech Translation".
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- 679 title "Holy Moses! Leveraging Existing Tools and Resources for Entity Translation".
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- 68 creator chen-liu.
- 68 creator lynette-melnar.
- 68 type InProceedings.
- 68 label "Borrowing Language Resources for Development of Automatic Speech Recognition for Low- and Middle-Density Languages".
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- 68 abstract "In this paper we describe an approach that both creates crosslingual acoustic monophone model sets for speech recognition tasks and objectively predicts their performance without target-language speech data or acoustic measurement techniques. This strategy is based on a series of linguistic metrics characterizing the articulatory phonetic and phonological distances of target-language phonemes from source-language phonemes. We term these algorithms the Combined Phonetic and Phonological Crosslingual Distance (CPP-CD) metric and the Combined Phonetic and Phonological Crosslingual Prediction (CPP-CP) metric. The particular motivations for this project are the current unavailability and often prohibitively high production cost of speech databases for many strategically important low- and middle-density languages. First, we describe the CPP-CD approach and compare the performance of CPP-CD-specified models to both native language models and crosslingual models selected by the Bhattacharyya acoustic-model distance metric in automatic speech recognition (ASR) experiments. Results confirm that the CPP-CD approach nearly matches those achieved by the acoustic distance metric. We then test the CPP-CP algorithm on the CPP-CD models by comparing the CPP-CP scores to the recognition phoneme error rates. Based on this comparison, we conclude that the CPP-CP algorithm is a reliable indicator of crosslingual model performance in speech recognition tasks.".
- 68 hasAuthorList authorList.
- 68 hasTopic Linguistics.
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- 68 keyword "Corpus (creation, annotation, etc.)".
- 68 keyword "Multilinguality".
- 68 keyword "Speech recognition and understanding".
- 68 title "Borrowing Language Resources for Development of Automatic Speech Recognition for Low- and Middle-Density Languages".
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