Sina M andarin Alphabetical Words:A Web-driven Code-mixing Lexical Resource (2020)

Abstract: Mandarin Alphabetical Word (MAW) is one indispensable component of Modern Chinese that demonstrates unique code-mixing idiosyncrasies influenced by language exchanges. Yet, this interesting phenomenon has not been properly addressed and is mostly excluded from the Chinese language system. This paper addresses the core problem of MAW identification and proposes to construct a large collection of MAWs from Sina Weibo (SMAW) using an automatic web-based technique which includes rule-based identification, informatics-based extraction, as well as Baidu search engine validation. A collection of 16,207 qualified SMAWs are obtained using this technique along with an annotated corpus of more than 200,000 sentences for linguistic research and applicable inquiries.

## introduction mandarin alphabetic words (maws), also known as lettered words @xcite or code-mixing words @xcite , are usually formed by latin, greek, arabic alphabets in combination with chinese characters, e.g. "x-光/x射 线", x-ray. although pure alphabets (e.g. "nba") used in chinese context have also been regarded as maws in some previous work @xcite , they are more like switching-codes that retain the orthography and linguistic behaviors of the original language, instead of showing typical chinese lexical characteristics. it is noteworthy that maws shall be taken as a code-mixing phenomenon instead of code-switching as a maw is still a chinese word which is not switched into another language. therefore, in this work, maws refer to the combined type which encodes both alphabet(s) and chinese character(s) in one word, such as "a型", a-type, "po主", post owner, and "γ线", gamma ray. it is linguistically-interesting and applicablysignificant to investigate maws due to two main reasons. first, a maw maintains part of the chinese characteristics in morphology, phonology and orthography (e.g. "pk过", player killed, past tense). meanwhile, it also demonstrates some properties of the foreigner language (e.g. "维生素ing", supplementing vitamin, progressive)), providing a unique lexical resource for studying morphophonological idiosyncrasies of code-mixing words. second, maws serve as an indispensable part of people's daily vocabulary, especially under the rapid development of social media communication. yet, being out-liars of the chinese lexicon, they can cause problems to existing word segmentation/new word extraction tools that are trained on traditional words @xcite . consider the following example: e1: po主 主 主也不知道链接被吞了 (the post owner didn't know that the link has been hacked off) seg: po/主 主 主/也/不/知道/链接/被/吞/了 golden seg: po主 主 主/也/不/知道/链接/被/吞/了 the sentence in e1 (example 1) is segmented using stanford parser @xcite which fails to identify the word "po主", post owner and breaks it into two parts. the same type of error also occurs in other popular segmentation tools. although @xcite proposed a radical method of word segmentation to meet the challenge, using a concept of classifying a string of character-boundaries into either word-boundaries or non-word-boundaries, their work did not address the cases of code-mixing words, whose word boundaries can also fall on foreigner alphabets. some other methods mainly rely on unsupervised methods @xcite or simple statistical methods based on n-gram frequencies, with indices of collocation and co-occurrence @xcite @xcite . however, these works are mainly designed for new words of pure chinese characters, which are not applicable to maws. in this paper, we address the issue of maw identification and present the construction of the sina maw lexicon (smaw) (available at https://github.com/christainx/smaw ) using a fully automatic information extraction technique. the quality of the maws (accurateness and inter-rater agreement) are rated by three experts for system evaluation. compared to previous resources, this dataset provides an unprecedentedly large, balanced, and structured maws as well as a maw annotated corpus. with the availability of a comprehensive maws as a valuable chinese lexical resource as well as corpus resource, it shall benefit many chinese language processing tasks which need to deal with code-mixing, such as word segmentation and information extraction. ## related works the earliest maw was probably "x射 线/x-光", x-ray, which was officially documented in 1903 @xcite . for over 60 years, such words had been largely confined to technical and medical domains with very few lexicalized and registered terms in dictionaries. the authoritative xiandai hanyu cidian/xianhan ("现代汉语词典"), for instance, initiated a separate section to include 39 maw entries in 1996. this list has grown rapidly with each subsequent xianhan dictionary edition, reaching 239 entries by the 2012 edition. this in turn generated a flurry of related linguistic studies, which were mainly focused on lexicological and language policy issues @xcite . some works have dealt with the emergence of maws in light of globalization, placing them in a socio-cultural context @xcite , and a few are also interested in studying the morpho-lexical status of maws @xcite @xcite . in the age of internet and social media, the scale of maws, their extraction methods, and resources of maws have changed drastically since the last decade. for example, @xcite extracted a small set of maws with manual validation from the corpus of people's daily @xcite . @xcite extracted 93 maws (out of 1,053 new domain-specific terms) using a statistical approach with rule-based validation. recently, @xcite extracted over 1,157 maws from both the sinica corpus @xcite and the chinese gigaword corpus @xcite based on manually segmented maws in the corpora. although they have extracted 60,000 tokens with alphabetical letters. however, the list mainly includes pure alphabets those are indeed switching codes of other languages. in our study, these pure code-switching words are excluded according to our definition. their work has established a taxonomy of distributional patterns of alphabetical letters in maws and found that typical maws follow chinese modifier-modified (head) morphological rule and the most frequent and productive pattern is alphabetical letter+ mandarin character (ac), such as type b in the form of "b型". besides the above investigations, maws have not been identified in a systemic and automatic way. the problem of identifying maws can be generalized as an issue of new/unknown/out-ofvocabulary word extraction (code-mixing chinese words in particular) @xcite . a commonly adopted way of identifying a new word usually rely on word segmentation at the first step and then map the valid maws to an existing dictionary. those not mapped in the dictionary will be identified as new words. this is actually problematic for identifying maws (cf. example in section 1). in addition, previous studies mainly extract maws from manually segmented newspapers in pre-1990s @xcite . hence, the resources are domain-constrained and usage-outdated. ## construction of smaw to address the bias in previous works, we propose to collect an maw list using social-media text commonly commonly available on sina weibo platform (weibo for short, or micro-blogs), a near-natural context. weibo is one of the most popular social media platform in china with over 400 million active users on monthly basis. this platform becomes the enabler for generating tons of online data, which can serve as a huge web corpus. the raw dataset crawled from weibo consists of over 226 million posts (around 20 gigabytes data). on the other hand, as there are many debates among linguists about the definition of a maw @xcite @xcite @xcite , this work uses a datadriven statistical approach as well as leveraging on search engine hits to exclude pseudo-maws of low-vitality. details of the methodology are given in the next section. figure 1 depicts the framework of smaw construction. collecting the smaw dataset is carried out through a two stage process: candidate extraction and candidate filtering. in our system, candidate extraction uses an alphabet-anchored brute-force extraction of n-grams tokens which contains both alphabets and chinese. to eliminate as many false positive cases as possible, candidate filtering uses three methods to remove noisy candidates using (1) rule-based refinement, (2) informatics-based elimination, as well as (3) search engine validation. in rule-based refinement, a number of rules are selected as preliminary refinement for candidate filtering. these rules are easy implemented and fast in execution. then, in informatics-based elimination, pmi (point-wise mutual information) and entropy are calculated to select candidates of high co-occurrence rate and informative flexibility. using informatics-based methods can greatly help narrow down the scope of maw candidates and remove false positive cases. lastly, search engine based validation is adopted to filter out low-vitality terms based on user links. this intellectual agent provide use cases about a candidate word as extra evidence. details of these steps are described in the following subsections. ## results and evaluation in our system, every filtering method is executed sequentially. due to length limitation of this paper, we are giving the final selected parameters of our modules without showing the tuning process. the n-gram token window size of bruteforce method in candidate extraction is set to 5 because most new terms are not longer than 5 as a common practice. in candidate filtering, len _t hres and f req_t hres (detailed in ## conclusion and future work this work uses social media platform (sina weibo) and search engine (baidu) for collection and validation of code-mixing words to tackle the under-representation and identification problems of maws. the evaluation of the new sina maw dataset (smaw), proves the high performance (acc. = 0.82, k. = 0.78) of the proposed extraction method as well as the effectiveness our proposed candidate filtering techniques in terms of reducing number of noisy candidates. the contribution of this work is two-fold: it proposes an innovative method of leveraging the web for maw extraction without involvement of manual mediation, yet achieving promising performance in identifying out-of-vocabulary code-mixing words; it provides a unique maw dataset and corresponding corpus which are most updated, scaled, structured and comprehensive for supporting linguistic inquiries of code-mixing words, as well as for facilitating related nlp tasks. the preliminary analysis to the lexical and grammatical characteristics of smaws and the corpus imply the development of code-mixing words into being a more important and diversified component in the chinese lexicon. future work will continue the annotation of the lexicon and the corpus with information of domains, sources, active time, semantic classes, etc., and conduct deeper linguistic analyses for uncovering the phonological and morpho-lexical characteristics of code-mixing words.