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Chinese LIWC Lexicon Expansion Via Hierarchical Classification of Word

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Chinese LIWC Lexicon Expansion Via Hierarchical Classification of Word The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) Chinese LIWC Lexicon Expansion via Hierarchical Classification of Word Embeddings with Sememe Attention Xiangkai Zeng,∗‡ Cheng Yang,§ Cunchao Tu,§ Zhiyuan Liu,†§¶ Maosong Sun§¶ ‡School of Computer Science and Engineering, Beihang University, Beijing, China §Department of Computer Science and Technology, State Key Lab on Intelligent Technology and Systems, National Lab for Information Science and Technology, Tsinghua University, Beijing, China ¶Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou, China Abstract lyze Chinese text. Fortunately, Chinese LIWC (Huang et al. Linguistic Inquiry and Word Count (LIWC) is a word count- 2012) has been released to fill the vacancy. In this paper, we ing software tool which has been used for quantitative text mainly focus on Chinese LIWC and use LIWC to stand for analysis in many fields. Due to its success and popularity, Chinese LIWC if not otherwise specified. the core lexicon has been translated into Chinese and many While LIWC has been used in a variety of fields, its lexi- other languages. However, the lexicon only contains several con only contains less than 7,000 words. This is insufficient thousand of words, which is deficient compared with the because according to (Li and others 2008), there are at least number of common words in Chinese. Current approaches 56,008 common words in Chinese. Moreover, LIWC lexi- often require manually expanding the lexicon, but it often con does not consider the emerging words and phrases on takes too much time and requires linguistic experts to ex- the Internet. Therefore, it is reasonable and necessary to ex- tend the lexicon. To address this issue, we propose to ex- pand the LIWC lexicon so that it is more accurate and com- pand the LIWC lexicon automatically. Specifically, we con- prehensive for scientific research. One way to expand LIWC sider it as a hierarchical classification problem and utilize lexicon is to annotate the new words manually. However, it the Sequence-to-Sequence model to classify words in the is too time-consuming and often requires language expertise lexicon. Moreover, we use the sememe information with to add new words. Hence, we propose to automatically ex- the attention mechanism to capture the exact meanings of pand LIWC lexicon. To the best of our knowledge, we are a word, so that we can expand a more precise and com- the first to expand LIWC lexicon automatically. prehensive lexicon. The experimental results show that our Automatically LIWC lexicon expansion faces the prob- model has a better understanding of word meanings with lems of polysemy and indistinctness. Polysemy means the help of sememes and achieves significant and consis- words and phrases have multiple meanings and are thereby tent improvements compared with the state-of-the-art meth- classified into multiple irrelevant categories. Indistinctness ods. The source code of this paper can be obtained from means many categories in LIWC are fine-grained, and thus https://github.com/thunlp/Auto CLIWC. making it more difficult to distinguish them. One important feature in LIWC lexicon is that categories Introduction form a tree structure hierarchy. Therefore, hierarchical clas- sification algorithms such as Hierarchical SVM (Support Linguistic Inquiry and Word Count (Pennebaker, Booth, and Vector Machine) (Chen, Crawford, and Ghosh 2004), can be Francis 2007, LIWC) has been widely used for comput- easily applied to LIWC lexicon expansion. However, these erized text analysis in social science. LIWC groups words methods are often too generic, without considering the pol- into manually-defined coarse-to-fine grained categories, and ysemy property of words and LIWC lexicon and indistinct- it was originally developed to address content analytic is- ness property of LIWC categories. sues in experimental psychology. Nowadays, there is an in- To address these issues, we propose to incorporate exter- creasing number of applications across fields such as com- nal annotated word information when expanding the lexicon. putational linguistics (Grimmer and Stewart 2013), demo- In linguistics, each word has one or more senses, and each graphics (Newman et al. 2008), health diagnostics (Bucci sense consists of one or more sememes, which are defined as and Maskit 2005), social relationship (Kacewicz et al. 2014), the smallest semantic language unit of meaning (Bloomfield etc. 1926). In this paper, we use HowNet (Dong and Dong 2003) Chinese is the most spoken language in the world (Lewis where words are annotated with relevant sememes. For pol- et al. 2009), but we cannot use the original LIWC to ana- ysemy problem, sememes can explicitly express different ∗This work was done when the first author was visiting Ts- meanings of a word and make it possible to assign multi- inghua University. ple labels. For indistinctness problem, sememe is also help- †Corresponding author: Zhiyuan Liu ([email protected]). ful in differentiating fine-grained categories since we have Copyright c 2018, Association for the Advancement of Artificial more detailed semantic information about the word mean- Intelligence (www.aaai.org). All rights reserved. ings. Moreover, sememes should be given different weights 5650 when classifying words into different categories. Thus, we annotation form a tree hierarchy, we can adopt hierarchi- propose to use attention mechanism to better utilize sememe cal classification methods for automatically LIWC expan- information. The experimental results show that our model sion. As the first effort on LIWC lexicon expansion, we be- achieves significant improvements as compared to the state- lieve that the works in hierarchical classification problems of-the-art methods. are more correlated to LIWC expansion and more suitable The contributions of this paper can be summarized as fol- for baseline comparisons. lows: For hierarchical classification, (Silla Jr and Freitas 2011) summarized a variety of them from different fields and cat- • To the best of our knowledge, our model is the first at- egorized them into five approaches. Flat Classifier (Barbedo tempt to expand LIWC lexicon automatically. and Lopes 2007) is the simplest one to deal with hierarchical • To address the polysemy and indistinctness problems, we classification problems. In this approach, the classifier com- propose to use sememe information to distinguish mean- pletely ignores the hierarchy and predicts only classes at the ings among words. leaf nodes. Local Classifier Per Node (Fagni and Sebastiani 2007) consists of training one binary classifier for class. Lo- • To better utilize sememe information, we use attention cal Classifier Per Parent Node (Silla and Freitas 2009) is the mechanism to assign different weights to sememes when approach where, for each parent node in the class hierar- classifying each word. chy, a multiclass classifier is trained to distinguish among • In the experiments, we show that our model outperforms its child nodes. Similar to Local Classifier Per Parent Node the state-of-the-art methods significantly. approach, Local Classifier Per Level (Clare and King 2003) trains one multiclass classifier, but at each level instead of each node. The last one is Global Classifier (Kiritchenko et Related Work al. 2006), where a single classification model is trained to In this section, we first introduce some previous works based take into account the hierarchy as a whole. on LIWC and then describe recent research in the hierarchi- In recent years, there have been some attempts to use neu- cal classification. Lastly, we briefly mention studies about ral networks for hierarchical classification. (Cerri, Barros, HowNet. and De Carvalho 2014) trained the multilayer perceptron The original English version of LIWC lexicon is one of level by level, and used the predictions of the neural network the most well-known dictionaries in quantitative text anal- as inputs for the next neural network associated with the next ysis. It was first released in the early 1990s and has been hierarchical level. (Karn, Waltinger, and Schutze¨ 2017) pro- updated several times, with the latest version released in posed to use RNN encoder-decoder for entity mention clas- 2015 (Pennebaker et al. 2015). Over these years, there have sification, which is a problem with a hierarchical class struc- emerged numerous scientific findings across different fields ture. The encoder-decoder performs classification by gen- with the help of the original lexicon. (Mehl et al. 2007) erating paths in the hierarchy from top node to leaf nodes. found that women and men both spoke about 16,000 words However, due to the polysemy and indistinctness problems, per day, dispelling the myth of female talkativeness. (Bucci these methods are not suitable for LIWC expansion. There- and Maskit 2007) showed that word counting approach tends fore, we propose to incorporate sememe information. to be less biased than clinician’s self-reports in therapeutic During these years, people manually annotate sememes improvements. (Rohrbaugh et al. 2008) found that the use of and build many linguistic knowledge bases. HowNet (Dong we indicated relationship closeness and was even predictive and Dong 2003) is a typical knowledge base with annotated of heart failure improvements. (Schwartz et al. 2013) ana- sememes, and it has far more words than LIWC. Therefore, lyzed personality, gender, and age in social media using an we can use it directly to help expand the LIWC lexicon. open-vocabulary approach. Due to the success and the im- HowNet has also been widely used in sentiment analysis portance of the original LIWC, (Huang et al. 2012) manually (Fu et al. 2013) and word representation learning (Niu et developed the first Chinese LIWC, and there is an increas- al. 2017). ing number of applications (Gao et al. 2013; Yu et al. 2016; Li et al. 2014) based on it. However, as a manually anno- Problem Formalization tated dictionary, this lexicon only contains less than 7,000 In this section, we first give an illustrative example of words words, which is a serious limitation compared to the number and categories in LIWC lexicon.
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