Phrase-based Part-of-Speech Tagging

Finch, Andrew; Sumita, Eiichro

doi:10.1109/nlpke.2007.4368036

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…3.1. Therefore, the proposed model degenerates the work in [24] precisely when the segmentation information is not included in the target phrase.…”

Section: Related Workmentioning

confidence: 98%

“…The work related most closely to ours is [24], in which the authors proposed a phrase-based POS tagging model and applied it to English POS tagging, resulting in improvements over existing word-based models. However, our model advances the work presented in [24] in the following aspects: 1) we generalize their models further in that the phrase-based model is applied not only to the POS tagging problem from [24] but also to the segmentation and tagging problem, which is, more generally, a Korean morphological analysis task that consists of three sub-tasks, and 2) because of its generalized nature, our model includes two different types of language models (LMs): the morpheme and tag language models, whereas [24] used a tag LM alone.…”

Section: Related Workmentioning

confidence: 99%

“…Note that the underlying idea in [24] of using the phrases as the basic processing unit for an NLP task is the same as that of our work. Thus, our proposed framework is not intended to oppose the method used in [24] but rather to generalize it.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, our proposed framework is not intended to oppose the method used in [24] but rather to generalize it. To generalize the method of [24] for the segmentation and tagging problem, we redefine a target phrase as a segment that captures both the segmentation and tagging information, as mentioned in Sect. 3.1.…”

Section: Related Workmentioning

confidence: 99%

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Phrase-Based Statistical Model for Korean Morpheme Segmentation and POS Tagging

Kim

2018

IEICE Trans. Inf. & Syst.

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SUMMARY In this paper, we propose a novel phrase-based model for Korean morphological analysis by considering a phrase as the basic processing unit, which generalizes all the other existing processing units. The impetus for using phrases this way is largely motivated by the success of phrase-based statistical machine translation (SMT), which convincingly shows that the larger the processing unit, the better the performance. Experimental results using the SEJONG dataset show that the proposed phrasebased models outperform the morpheme-based models used as baselines. In particular, when combined with the conditional random field (CRF) model, our model leads to statistically significant improvements over the state-of-the-art CRF method.

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“…3.1. Therefore, the proposed model degenerates the work in [24] precisely when the segmentation information is not included in the target phrase.…”

Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Phrase-Based Statistical Model for Korean Morpheme Segmentation and POS Tagging

Kim

2018

IEICE Trans. Inf. & Syst.

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“…• Extrai as correlações semânticas entre as palavras por meio das conjunções "e" e "mas" usando parte de técnica-de-fala (Part-Of-Speech -POS) (FINCH, 2007) em mídias sociais;…”

Section: )unclassified

Análise de sentimentos e afetividade de textos extraídos das redes sociais.

Rosa¹

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Currently, users express their wishes and preferences in relation to an object, content or event through social networks; therefore analyze the sentiments of a person in the digital world about what surrounds the person has been increasingly used in order to know the preferences of this person. The study proposes new metrics of sentiments and affection, improving the sentiment analysis. The sentiment analysis metric associated with a corresponding correction factor for n-grams, tenses, expressions and personal characteristics such as age, gender and education is developed in this work. Negative, neutral and positive sentiments are extracted from social networks phrases. The sentences are ranked in positive, neutral or negative sentiment intensity or polarity by a new dictionary of words in Portuguese language and is extracted the sentiments. The calculation of sentiments has specific rules for verb tenses (present and past) and adverbs. The sentiments are extracted by means of adjectives, nouns, unigrams and associated words (bigrams and trigrams) that have a different meaning of single words. To validate the dictionary performance and new sentiments calculation mechanisms, the results are compared with an analysis tool of sentiments named of SentiStrength and are validated by subjective tests, with remote evaluators, with a technique named of crowdsourcing and machine learning. The study also analyzes the affection of sentences and proposes a metric called Brazillian Affective Metric (AFM-Br), that extracts emotions of anger, joy, sadness, surprise and disgust. The sentiment analysis solution and affection is applied in a music recommendation system, as a case study, which suggests content according to the emotional state of the person.

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