The Chinese pronunciation system offers two characteristics that distinguish it from other languages: deep phonemic orthography and intonation variations. We are the first to argue that these two important properties can play a major role in Chinese sentiment analysis. Particularly, we propose two effective features to encode phonetic information. Next, we develop a Disambiguate Intonation for Sentiment Analysis (DISA) network using a reinforcement network. It functions as disambiguating intonations for each Chinese character (pinyin). Thus, a precise phonetic representation of Chinese is learned. Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text. Experimental results on five different Chinese sentiment analysis datasets show that the inclusion of phonetic features significantly and consistently improves the performance of textual and visual representations and outshines the state-of-the-art Chinese character level representations.
Chinese text differs from English text for two key aspects: it does not have word segmentations and it has a characteristic of compositionality due to its pictogram nature. Based on the former aspect, word segmentation tools are always employed before text representation, such as ICTCLAS @cite_45 , THULAC @cite_19 , Jieba github.com fxsjy jieba and so forth. Based on the latter aspect, several works had focused on the use of sub-word components (such as characters and radicals) to improve word embeddings. @cite_57 proposed decomposition of Chinese words into characters and presented a character-enhanced word embedding model (CWE). @cite_34 @cite_31 had decomposed Chinese characters to radicals and developed a radical-enhanced Chinese character embedding. In @cite_53 , pure radical based embeddings were trained for short-text categorization, Chinese word segmentation and web search ranking. @cite_37 extend the pure radical embedding by introducing multi-granularity Chinese word embeddings.
@cite_37 @cite_53 @cite_57 @cite_19 @cite_45 @cite_31 @cite_34
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Languages using Chinese characters are mostly processed at word level. Inspired by recent success of deep learning, we delve deeper to character and radical levels for Chinese language processing. We propose a new deep learning technique, called “radical embedding”, with justifications based on Chinese linguistics, and validate its feasibility and utility through a set of three experiments: two in-house standard experiments on short-text categorization (STC) and Chinese word segmentation (CWS), and one in-field experiment on search ranking. We show that radical embedding achieves comparable, and sometimes even better, results than competing methods. Most word embedding methods take a word as a basic unit and learn embeddings according to words' external contexts, ignoring the internal structures of words. However, in some languages such as Chinese, a word is usually composed of several characters and contains rich internal information. The semantic meaning of a word is also related to the meanings of its composing characters. Hence, we take Chinese for example, and present a character-enhanced word embedding model (CWE). In order to address the issues of character ambiguity and non-compositional words, we propose multiple prototype character embeddings and an effective word selection method. We evaluate the effectiveness of CWE on word relatedness computation and analogical reasoning. The results show that CWE outperforms other baseline methods which ignore internal character information. The codes and data can be accessed from https: github.com Leonard-Xu CWE. This document presents the results from Inst. of Computing Tech., CAS in the ACL SIGHAN-sponsored First International Chinese Word Segmentation Bake-off. The authors introduce the unified HHMM-based frame of our Chinese lexical analyzer ICTCLAS and explain the operation of the six tracks. Then provide the evaluation results and give more analysis. Evaluation on ICTCLAS shows that its performance is competitive. Compared with other system, ICTCLAS has ranked top both in CTB and PK closed track. In PK open track, it ranks second position. ICTCLAS BIG5 version was transformed from GB version only in two days; however, it achieved well in two BIG5 closed tracks. Through the first bakeoff, we could learn more about the development in Chinese word segmentation and become more confident on our HHMM-based approach. At the same time, we really find our problems during the evaluation. The bakeoff is interesting and helpful. Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this work, we innovatively develop two component-enhanced Chinese character embedding models and their bigram extensions. Distinguished from English word embeddings, our models explore the compositions of Chinese characters, which often serve as semantic indictors inherently. The evaluations on both word similarity and text classification demonstrate the effectiveness of our models. In this paper, we present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role for modelling character semantics as characters with the same radical usually have similar semantic meaning and grammatical usage. However, most existing character (or word) embedding learning algorithms typically only model the syntactic contexts but ignore the radical information. As a result, they do not explicitly capture the inner semantic connections of characters via radical into the embedding space of characters. To solve this problem, we propose to incorporate the radical information for enhancing the Chinese character embedding. We present a dedicated neural architecture with a hybrid loss function, and integrate the radical information through softmax upon each character. To verify the effectiveness of the learned character embedding, we apply it on Chinese word segmentation. Experiment results on two benchmark datasets show that, our radical-enhanced method outperforms two widely-used context-based embedding learning algorithms.