A Study on Implementation of Southern-Min Taiwanese Tone Sandhi System

PACLIC 19 / Taipei, taiwan / December 1-3, 200

In and Out: Senses and Meaning Extension of Mandarin Spatial Terms nei and wai

PACLIC 19 / Taipei, taiwan / December 1-3, 200

People in the State of the Union: Viewing Social Change through the Eyes of Presidents

PACLIC 19 / Taipei, taiwan / December 1-3, 200

MARKET Metaphors: Chinese, English and Malay

PACLIC 19 / Taipei, taiwan / December 1-3, 200

XNLRDF, an Open Source Natural Language Resource Description Framework

PACLIC 19 / Taipei, taiwan / December 1-3, 200

Automatic Acquisition of Knowledge About Multiword Predicates

PACLIC 19 / Taipei, taiwan / December 1-3, 200

Towards case-based parsing : are chunks reliable indicators for syntax trees?

This paper presents an approach to the question whether it is possible to construct a parser based on ideas from case-based reasoning. Such a parser would employ a partial analysis of the input sentence to select a (nearly) complete syntax tree and then adapt this tree to the input sentence. The experiments performed on German data from the Tüba-D/Z treebank and the KaRoPars partial parser show that a wide range of levels of generality can be reached, depending on which types of information are used to determine the similarity between input sentence and training sentences. The results are such that it is possible to construct a case-based parser. The optimal setting out of those presented here need to be determined empirically

Multi-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations

As a key task of question answering, question retrieval has attracted much attention from the communities of academia and industry. Previous solutions mainly focus on the translation model, topic model, and deep learning techniques. Distinct from the previous solutions, we propose to construct fine-grained semantic representations of a question by a learned importance score assigned to each keyword, so that we can achieve a fine-grained question matching solution with these semantic representations of different lengths. Accordingly, we propose a multi-view semantic matching model by reusing the important keywords in multiple semantic representations. As a key of constructing fine-grained semantic representations, we are the first to use a cross-task weakly supervised extraction model that applies question-question labelled signals to supervise the keyword extraction process (i.e. to learn the keyword importance). The extraction model integrates the deep semantic representation and lexical matching information with statistical features to estimate the importance of keywords. We conduct extensive experiments on three public datasets and the experimental results show that our proposed model significantly outperforms the state-of-the-art solutions.Comment: 10 page