Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis
This paper presents a joint model for morphological and dependency analysis based on automatically acquired lexical knowledge. This model takes advantage of rich lexical knowledge to simultaneously resolve word segmentation, POS, and dependency ambiguities. In our experiments on Japanese, we show the effectiveness of our joint model over conventional pipeline models.
Code (0)
등록된 구현이 없습니다.
Tasks
LemmatizationMorphological AnalysisPOSSegmentationSimilar Papers 제목 키워드 기반
Cross-lingual Linking of Automatically Constructed Frames and FrameNet
A semantic frame is a conceptual structure describing an event, relation, or object along with its participants. Several semantic frame resources have been manually elaborated, and there has been much interest in the pos…
Cross-Lingual Word EmbeddingsWord EmbeddingsBERT-Based Simplification of Japanese Sentence-Ending Predicates in Descriptive Text
Japanese sentence-ending predicates intricately combine content words and functional elements, such as aspect, modality, and honorifics; this can often hinder the understanding of language learners and children. Conventi…
DescriptiveLexical SimplificationSentenceAutomatically Extracting Variant-Normalization Pairs for Japanese Text Normalization
Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing. We present a method for automatical…
Machine TranslationMorphological AnalysisText NormalizationWord Complexity Estimation for Japanese Lexical Simplification
We introduce three language resources for Japanese lexical simplification: 1) a large-scale word complexity lexicon, 2) the first synonym lexicon for converting complex words to simpler ones, and 3) the first toolkit for…
BenchmarkingLexical SimplificationA Large Scale Database of Strongly-related Events in Japanese
The knowledge about the relation between events is quite useful for coreference resolution, anaphora resolution, and several NLP applications such as dialogue system. This paper presents a large scale database of strongl…
Common Sense Reasoningcoreference-resolutionCoreference ResolutionEvent Extraction