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Likun Qiu

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3 papers

1 author row

AAAI Conference 2020 Conference Paper

Multi-Point Semantic Representation for Intent Classification

Jinghan Zhang
Yuxiao Ye
Yue Zhang
Likun Qiu
Bin Fu
Yang Li
Zhenglu Yang
Jian Sun

Detecting user intents from utterances is the basis of natural language understanding (NLU) task. To understand the meaning of utterances, some work focuses on fully representing utterances via semantic parsing in which annotation cost is labor-intentsive. While some researchers simply view this as intent classiﬁcation or frequently asked questions (FAQs) retrieval, they do not leverage the shared utterances among different intents. We propose a simple and novel multi-point semantic representation framework with relatively low annotation cost to leverage the ﬁne-grained factor information, decomposing queries into four factors, i. e. , topic, predicate, object/condition, query type. Besides, we propose a compositional intent bi-attention model under multi-task learning with three kinds of attention mechanisms among queries, labels and factors, which jointly combines coarse-grained intent and ﬁne-grained factor information. Extensive experiments show that our framework and model signiﬁcantly outperform several state-of-the-art approaches with an improvement of 1. 35%-2. 47% in terms of accuracy.

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AAAI Conference 2016 Conference Paper

Dependency Tree Representations of Predicate-Argument Structures

Likun Qiu
Yue Zhang
Meishan Zhang

We present a novel annotation framework for representing predicate-argument structures, which uses dependency trees to encode the syntactic and semantic roles of a sentence simultaneously. The main contribution is a semantic role transmission model, which eliminates the structural gap between syntax and shallow semantics, making them compatible. A Chinese semantic treebank was built under the proposed framework, and the ﬁrst release containing about 14K sentences is made freely available. The proposed framework enables semantic role labeling to be solved as a sequence labeling task, and experiments show that standard sequence labelers can give competitive performance on the new treebank compared with state-of-the-art graph structure models.

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AAAI Conference 2015 Conference Paper

Word Segmentation for Chinese Novels

Likun Qiu
Yue Zhang

Word segmentation is a necessary first step for automatic syntactic analysis of Chinese text. Chinese segmentation is highly accurate on news data, but the accuracies drop significantly on other domains, such as science and literature. For scientific domains, a significant portion of out-of-vocabulary words are domain-specific terms, and therefore lexicons can be used to improve segmentation significantly. For the literature domain, however, there is not a fixed set of domain terms. For example, each novel can contain a specific set of person, organization and location names. We investigate a method for automatically mining common noun entities for each novel using information extraction techniques, and use the resulting entities to improve a state-of-the-art segmentation model for the novel. In particular, we design a novel double-propagation algorithm that mines noun entities together with common contextual patterns, and use them as plug-in features to a model trained on the source domain. An advantage of our method is that no retraining for the segmentation model is needed for each novel, and hence it can be applied efficiently given the huge number of novels on the web. Results on five different novels show significantly improved accuracies, in particular for OOV words.

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