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检索条件"任意字段=6th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2017"

共 117 条记录，以下是21-30 订阅

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A Deep Convolutional Neural Model for Character-Based chinese Word Segmentation 6th

A Deep Convolutional Neural Model for Character-Based Chines...

引用

6th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Xie, Zhipeng Hu, Junfeng Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai Peoples R China

ISBN: (纸本)9783319736181;9783319736174

this paper proposes a deep convolutional neural model for character-based chinese word segmentation. It first constructs position embeddings to encode unigram and bigram features that are directly related to single positions in input sentence, and then adaptively builds up hierarchical position representations with a deep convolutional net. In addition, a multi-task learning strategy is used to further enhance this deep neural model by treating multiple supervised CWS datasets as different tasks. Experimental results have shown that our neural model outperforms the existing neural ones, and the model equipped with multitask learning has successfully achieved state-of-the-art F-score performance for standard benchmarks: 0.964 on PKU dataset and 0.978 on MSR dataset.

关键词： Benchmarking

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Effective Semantic Relationship Classification of Context-Free chinese Words with Simple Surface and Embedding Features 6th

Effective Semantic Relationship Classification of Context-Fr...

引用

6th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Zhou, Yunxiao Lan, Man Wu, Yuanbin East China Normal Univ Sch Comp Sci & Software Engn Shanghai 200062 Peoples R China Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China

ISBN: (纸本)9783319736181;9783319736174

this paper describes the system we submitted to Task 1, i.e., chinese Word Semantic Relation Classification, in nlpcc 2017. Given a pair of context-free chinese words, this task is to predict the semantic relationships of them among four categories: Synonym, Antonym, Hyponym and Meronym. We design and investigate several surface features and embedding features containing word level and character level embeddings together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.

关键词： Semantic relation classification Context-free chinese words Surface and embedding features Supervised machine learning

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Modeling Indicative Context for Statistical Machine Translation 6th

Modeling Indicative Context for Statistical Machine Translat...

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6th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Wu, Shuangzhi Zhang, Dongdong Liu, Shujie Zhou, Ming Harbin Inst Technol Harbin Heilongjiang Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9783319736181;9783319736174

Contextual information is very important to select the appropriate phrases in statistical machine translation (SMT). the selection of different target phrases is sensitive to different parts of source contexts. Previous approaches based on either local contexts or global contexts neglect impacts of different contexts and are not always effective to disambiguate translation candidates. As a matter of fact, the indicative contexts are expected to play more important roles for disambiguation. In this paper, we propose to leverage the indicative contexts for translation disambiguation. Our model assigns phrase pairs confidence scores based on different source contexts which are then intergraded into the SMT log-linear model to help select translation candidates. Experimental results show that our proposed method significantly improves translation performance on the NIST chinese-to-English translation tasks compared with the state-of-the-art SMT baseline.

关键词： Computer aided language translation

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Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information 6th

Jointly Modeling Intent Identification and Slot Filling with...

引用

6th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Wen, Liyun Wang, Xiaojie Dong, Zhenjiang Chen, Hong Beijing Univ Posts & Telecommun Beijing Peoples R China ZTE Corp Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783319736181;9783319736174

Intent classification and slot filling are two critical subtasks of natural language understanding (NLU) in task-oriented dialogue systems. Previous work has made use of either hierarchical or contextual information when jointly modeling intent classification and slot filling, proving that either of them is helpful for joint models. this paper proposes a cluster of joint models to encode both types of information at the same time. Experimental results on different datasets show that the proposed models outperform joint models without either hierarchical or contextual information. Besides, finding the balance between two loss functions of two subtasks is important to achieve best overall performances.

关键词： Joint models Contextual and hierarchical information natural language understanding

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Active Learning for chinese Word Segmentation on Judgements 6th

Active Learning for Chinese Word Segmentation on Judgements

引用

6th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Yan, Qian Wang, Limin Li, Shoushan Liu, Huan Zhou, Guodong Soochow Univ Sch Comp Sci & Technol Nat Language Proc Lab Suzhou Peoples R China

ISBN: (纸本)9783319736181;9783319736174

this paper aims to perform the task of chinese Word Segmentation on judgements. For this task, the main challenge is the lack of the annotated corpus. To alleviate this challenge, this paper proposes an active learning approach. Specifically, on the basis of a few initial annotated samples, a new active learning approach is proposed to annotate some informative characters, and then select the context around these characters for annotation. In the active learning approach, it not only considers the uncertainty of the sample, but also leverages the redundancy of the sample for the selection of informative characters. Furthermore, this paper adopts the local annotation strategy, which select a substrings around the informative characters rather than the whole sentences and thus could also reduce the annotation. the empirical study demonstrates that the proposed approach effectively reduces the annotation cost and performances better than other baseline sample selection strategies under the same scale of annotation.

关键词： chinese word segmentation Active learning Judgements

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A Semantic Representation Enhancement Method for chinese News Headline Classification 6th

A Semantic Representation Enhancement Method for Chinese New...

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6th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Yin, Zhongbo Tang, Jintao Ru, Chengsen Luo, Wei Luo, Zhunchen Ma, Xiaolei China Def Sci & Technol Informat Ctr Beijing 100142 Peoples R China Natl Univ Def Technol Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9783319736181;9783319736174

Recently there has been an increasing research interest in short text such as news headline. Due to the inherent sparsity of short text, the current text classification methods perform badly when applied to the classification of news headlines. To overcome this problem, a novel method which enhances the semantic representation of headlines is proposed in this paper. Firstly, we add some keywords extracted from the most similar news to expand the word features. Secondly, we use the corpus in news domain to pre-train the word embedding so as to enhance the word representation. Moreover, Fasttext classifier, which uses a liner method to classify text with fast speed and high accuracy, is adopted for news headline classification. On the task for chinese news headline categorization in nlpcc2017, the proposed method achieved 83.1% of the F-measure, which got the first rank in 33 teams.

关键词： Semantic representation enhancement Short text classification News headline Word embedding

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Exploring the Impact of Linguistic Features for chinese Readability Assessment 6th

Exploring the Impact of Linguistic Features for Chinese Read...

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6th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Qiu, Xinying Deng, Kebin Qiu, Likun Wang, Xin Guangdong Univ Foreign Studies Guangzhou Guangdong Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China Ludong Univ Yantan Peoples R China Southwestern Univ Finance & Econ Chengdu Sichuan Peoples R China

ISBN: (纸本)9783319736181;9783319736174

Readability assessment plays an important role in selecting proper reading materials for language learners, and is applicable for many NLP tasks such as text simplification and document summarization. In this study, we designed 100 factors to systematically evaluate the impact of four levels of linguistic features (shallow, POS, syntactic, discourse) on predicting text difficulty for L1 chinese learners. We further selected 22 significant features with regression. Our experiment results show that the 100-feature model and the 22-feature model both achieve the same predictive accuracies as the BOW baseline for the majority of the text difficulty levels, and significantly better than baseline for the others. Using 18 out of the 22 features, we derived one of the first readability formulas for contemporary simplified chinese language.

关键词： Automatic Readability Assessment Feature selection

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Domain-Specific chinese Word Segmentation with Document-Level Optimization 6th

Domain-Specific Chinese Word Segmentation with Document-Leve...

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6th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Yan, Qian Shen, Chenlin Li, Shoushan Xia, Fen Du, Zekai Soochow Univ Sch Comp Sci & Technol Nat Language Proc Lab Suzhou Peoples R China Beijing Wisdom Uranium Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9783319736181;9783319736174

Previous studies normally formulate chinese word segmentation as a character sequence labeling task and optimize the solution in sentence-level. In this paper, we address chinese word segmentation as a document-level optimization problem. First, we apply a state-of-the-art approach, i.e., long short-term memory (LSTM), to perform character classification;then, we propose a global objective function on the basis of character classification and achieve global optimization via Integer Linear Programming (ILP). Specifically, we propose several kinds of global constrains in ILP to capture various segmentation knowledge, such as segmentation consistency and domain-specific regulations, to achieve document-level optimization, besides label transition knowledge to achieve sentence-level optimization. Empirical studies demonstrate the effectiveness of the proposed approach to domain-specific chinese word segmentation.

关键词： chinese word segmentation Document-level LSTM ILP

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Improved Compare-Aggregate Model for chinese Document-Based Question Answering 6th

Improved Compare-Aggregate Model for Chinese Document-Based ...

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6th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Wang, Ziliang Bian, Weijie Li, Si Chen, Guang Lin, Zhiqing Beijing Univ Posts & Telecommun Pattern Recognit & Intelligent Syst Xitucheng Rd 10 Beijing 100876 Peoples R China

ISBN: (纸本)9783319736181;9783319736174

Document-based question answering (DBQA) is a sub-task in question answering. It aims to measure the matching relation between questions and answers, which can be regarded as sentence matching problem. In this paper, we introduce a Compare-Aggregate architecture to handle the word-level comparison and aggregation. To deal with the noisy information in traditional attention mechanism, the k-top attention mechanism is proposed to filter out irrelevant words. Subsequently, we propose a combined model to merge matching relation learned by Compare-Aggregate model with shallow features to generate the final matching score. We evaluate our model on chinese Document-based Question Answering (DBQA) task. the experimental results show the effectiveness of our proposed improved methods. And our final combined model achieves second place result on the DBQA task of nlpcc-ICCPOL 2017 Shared Task. the paper provides the technical details of the proposed algorithm.

关键词： Question answering Compare-Aggregate architecture Deep learning Hybrid features Sentence matching

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Stochastic language Generation Using Situated PCFGs 4th

Stochastic Language Generation Using Situated PCFGs

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4th ccf international conference on natural language processing and chinese computing (nlpcc)

作者： Yuan, Caixia Wang, Xiaojie Zhong, Ziming Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

ISBN: (纸本)9783319252070;9783319252063

this paper presents a purely data-driven approach for generating natural language (NL) expressions from its corresponding semantic representations. Our aim is to exploit a parsing paradigm for natural language generation (NLG) task, which first encodes semantic representations with a situated probabilistic context-free grammar (PCFG), then decodes and yields natural sentences at the leaves of the optimal parsing tree. We deployed our system in two different domains, one is response generation for a chinese spoken dialogue system, and the other is instruction generation for a virtual environment in English language, obtaining results comparable to state-of-the-art systems both in terms of BLEU scores and human evaluation.

关键词： natural language generation Meaning representation Situated PCFG

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