On SemEval-2010 Japanese WSD Task
スポンサーリンク
概要
- 論文の詳細を見る
An overview of the SemEval-2 Japanese WSD task is presented. The new characteristics of our task are (1) the task will use the first balanced Japanese sense-tagged corpus, and (2) the task will take into account not only the instances that have a sense in the given set but also the instances that have a sense that cannot be found in the set. It is a lexical sample task, and word senses are defined according to a Japanese dictionary, the Iwanami Kokugo Jiten. This dictionary and a training corpus were distributed to participants. The number of target words was 50, with 22 nouns, 23 verbs, and 5 adjectives. Fifty instances of each target word were provided, consisting of a total of 2,500 instances for the evaluation. Nine systems from four organizations participated in the task.
- Information and Media Technologies 編集運営会議の論文
著者
-
Okumura Manabu
Precision And Intelligence Laboratory Tokyo Institute Of Technology
-
Shirai Kiyoaki
School Of Information Science Japan Advanced Institute Of Science And Technology
-
YOKONO Hikaru
Precision and Intelligence Laboratory, Tokyo Institute of Technology
-
Yokono Hikaru
Precision And Intelligence Laboratory Tokyo Institute Of Technology
-
Komiya Kanako
Institute Of Engineering Tokyo University Of Agriculture And Technology
関連論文
- Active Learning with Partially Annotated Sequence
- Semi-Supervised Learning to Classify Evaluative Expressions from Labeled and Unlabeled Texts(Knowledge, Information and Creativity Support System)
- Analysis of Eye Movements and Linguistic Boundaries in a Text for the Investigation of Japanese Reading Processes
- Collecting Object-attribute Noun Pairs and Constructing Concept Graphs for the Argument of Adjectives from Japanese N1-Adj-N2 Constructions
- On SemEval-2010 Japanese WSD task ([SemEval-2日本語タスクを中心とする日本語語義曖昧性解消])
- Active Learning with Subsequence Sampling Strategy for Sequence Labeling Tasks
- Active Learning with Subsequence Sampling Strategy for Sequence Labeling Tasks
- Study on Supervised Learning of Vietnamese Word Sense Disambiguation Classifiers
- Query Snowball: A Co-occurrence-based Approach to Multi-document Summarization for Question Answering
- Query Snowball: A Co-occurrence-based Approach to Multi-document Summarization for Question Answering
- An Efficient Algorithm for Unsupervised Word Segmentation with Branching Entropy and MDL
- On SemEval-2010 Japanese WSD Task