Shuhei Kondo


Coordination Boundary Identification without Labeled Data for Compound Terms Disambiguation
Yuya Sawada | Takashi Wada | Takayoshi Shibahara | Hiroki Teranishi | Shuhei Kondo | Hiroyuki Shindo | Taro Watanabe | Yuji Matsumoto
Proceedings of the 28th International Conference on Computational Linguistics

We propose a simple method for nominal coordination boundary identification. As the main strength of our method, it can identify the coordination boundaries without training on labeled data, and can be applied even if coordination structure annotations are not available. Our system employs pre-trained word embeddings to measure the similarities of words and detects the span of coordination, assuming that conjuncts share syntactic and semantic similarities. We demonstrate that our method yields good results in identifying coordinated noun phrases in the GENIA corpus and is comparable to a recent supervised method for the case when the coordinator conjoins simple noun phrases.


Construction of English MWE Dictionary and its Application to POS Tagging
Yutaro Shigeto | Ai Azuma | Sorami Hisamoto | Shuhei Kondo | Tomoya Kose | Keisuke Sakaguchi | Akifumi Yoshimoto | Frances Yung | Yuji Matsumoto
Proceedings of the 9th Workshop on Multiword Expressions

Hidden Markov Tree Model for Word Alignment
Shuhei Kondo | Kevin Duh | Yuji Matsumoto
Proceedings of the Eighth Workshop on Statistical Machine Translation

Efficient Stacked Dependency Parsing by Forest Reranking
Katsuhiko Hayashi | Shuhei Kondo | Yuji Matsumoto
Transactions of the Association for Computational Linguistics, Volume 1

This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discriminative reranker, using the 1-best tree output by the shift-reduce parser as guide features in addition to third-order graph-based features. To improve efficiency and accuracy, this paper also proposes a novel shift-reduce parser that eliminates the spurious ambiguity of arc-standard transition systems. Testing on the English Penn Treebank data, forest reranking gave a state-of-the-art unlabeled dependency accuracy of 93.12.


NAIST at the HOO 2012 Shared Task
Keisuke Sakaguchi | Yuta Hayashibe | Shuhei Kondo | Lis Kanashiro | Tomoya Mizumoto | Mamoru Komachi | Yuji Matsumoto
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP