Treebank-based acquisition of a Chinese lexical-functional grammar

Scaling wide-coverage, constraint-based grammars such as Lexical-Functional Grammars (LFG) (Kaplan and Bresnan, 1982; Bresnan, 2001) or Head-Driven Phrase Structure Grammars (HPSG) (Pollard and Sag, 1994) from fragments to naturally occurring unrestricted text is knowledge-intensive, time-consuming and (often prohibitively) expensive. A number of researchers have recently presented methods to automatically acquire wide-coverage, probabilistic constraint-based grammatical resources from treebanks (Cahill et al., 2002, Cahill et al., 2003; Cahill et al., 2004; Miyao et al., 2003; Miyao et al., 2004; Hockenmaier and Steedman, 2002; Hockenmaier, 2003), addressing the knowledge acquisition bottleneck in constraint-based grammar development. Research to date has concentrated on English and German. In this paper we report on an experiment to induce wide-coverage, probabilistic LFG grammatical and lexical resources for Chinese from the Penn Chinese Treebank (CTB) (Xue et al., 2002) based on an automatic f-structure annotation algorithm. Currently 96.751% of the CTB trees receive a single, covering and connected f-structure, 0.112% do not receive an f-structure due to feature clashes, while 3.137% are associated with multiple f-structure fragments. From the f-structure-annotated CTB we extract a total of 12975 lexical entries with 20 distinct subcategorisation frame types. Of these 3436 are verbal entries with a total of 11 different frame types. We extract a number of PCFG-based LFG approximations. Currently our best automatically induced grammars achieve an f-score of 81.57% against the trees in unseen articles 301-325; 86.06% f-score (all grammatical functions) and 73.98% (preds-only) against the dependencies derived from the f-structures automatically generated for the original trees in 301-325 and 82.79% (all grammatical functions) and 67.74% (preds-only) against the dependencies derived from the manually annotated gold-standard f-structures for 50 trees randomly selected from articles 301-325

Treebank-Based Acquisition of a Chinese Lexical-Functional Grammar

Scaling wide-coverage, constraint-based grammars such as Lexical-Functional Grammars (LFG) (Kaplan and Bresnan, 1982; Bresnan, 2001) or Head-Driven Phrase Structure Grammars (HPSG) (Pollard and Sag, 1994) from fragments to naturally occurring unrestricted text is knowledge-intensive, time-consuming and (often prohibitively) expensive. A number of researchers have recently presented methods to automatically acquire wide-coverage, probabilistic constraint-based grammatical resources from treebanks (Cahill et al., 2002, Cahill et al., 2003; Cahill et al., 2004; Miyao et al., 2003; Miyao et al., 2004; Hockenmaier and Steedman, 2002; Hockenmaier, 2003), addressing the knowledge acquisition bottleneck in constraint-based grammar development. Research to date has concentrated on English and German. In this paper we report on an experiment to induce wide-coverage, probabilistic LFG grammatical and lexical resources for Chinese from the Penn Chinese Treebank (CTB) (Xue et al., 2002) based on an automatic f-structure annotation algorithm. Currently 96.751% of the CTB trees receive a single, covering and connected f-structure, 0.112% do not receive an fstructure due to feature clashes, while 3.137% are associated with multiple f-structure fragments. From the f-structure-annotated CTB we extract a total of 12975 lexical entries with 20 distinct subcategorisation frame types. Of these 3436 are verbal entries with a total of 11 different frame types. We extract a number of PCFG-based LFG approximations. Currently our best automatically induced grammars achieve an f-score of 81.57% against the trees in unseen articles 301-325; 86.06% f-score (all grammatical functions) and 73.98% (preds-only) against the dependencies derived from the f-structures automatically generated for the original trees in 301-325 and 82.79% (all grammatical functions) and 67.74% (preds-only) against the dependencies derived from the manually annotated gold-standard f-structures for 50 trees randomly selected from articles 301-325

Pengembangan tata bahasa baku bahasa Indonesia (TBBI) daring terpadu

Badan Pengembangan dan Pembinaan Bahasa (Badan Bahasa) di bawah naungan Kementerian Pendidikan dan Kebudayaan Republik Indonesia, sebagai instansi pemerintah yang ditugaskan untuk menangani masalah kebahasaan dan kesastraan di Indonesia, menerbitkan berbagai produk kebahasaan. Dua produk yang sering dimanfaatkan para pemelajar bahasa Indonesia adalah Kamus Besar Bahasa Indonesia (KBBI) dan Tata Bahasa Baku Bahasa Indonesia (TBBI). KBBI terbaru edisi kelima (Amalia 2016) diluncurkan pada tahun 2016 dalam tiga versi: cetak, daring, dan luring (Moeljadi et al. 2017). Sejak diluncurkan pada 28 Oktober 2016, KBBI Daring mendapat sambutan hangat masyarakat, baik dari dalam maupun luar negeri. KBBI Daring memudahkan pemelajar bahasa Indonesia dan masyarakat umum menggunakan kamus pada era digital ini. Hal yang serupa dapat dilakukan untuk TBBI. Makalah ini membahas tahap awal pengembangan pangkalan data dan laman TBBI Daring Terpadu dengan menggunakan tata bahasa komputasional bahasa Indonesia INDRA (Indonesian Resource Grammar) (Moeljadi et al. 2015) yang dikembangkan dengan metode rekayasa tata bahasa dengan mengacu pada buku-buku referensi tata bahasa baku bahasa Indonesia, terutama TBBI (Alwi et al. 2014) dan Indonesian Reference Grammar (Sneddon et al. 2010). TBBI Daring Terpadu akan memuat aturan-aturan tata bahasa bahasa Indonesia baku, dipadukan dengan leksikon dan contoh-contoh dari korpus bahasa Indonesia baku yang telah dianotasi secara sintaksis dan semantis. Penulis berharap TBBI Daring Terpadu dapat menjadi acuan utama tata bahasa baku bahasa Indonesia yang dapat diakses dengan mudah oleh para penggunanya, misalnya pemelajar Bahasa Indonesia bagi Penutur Asing (BIPA), dan dapat memperkaya KBBI Daring dalam penggolongan kelas kata yang lebih spesifik, serta mendorong kemajuan bidang linguistik komputasional dan pemrosesan bahasa alami bahasa Indonesia, misalnya dalam penerjemahan mesin dan pengembangan sistem pemeriksaan gramatika dan leksikon bahasa Indonesia baku

Treebank-based acquisition of Chinese LFG resources for parsing and generation

This thesis describes a treebank-based approach to automatically acquire robust,wide-coverage Lexical-Functional Grammar (LFG) resources for Chinese parsing and generation, which is part of a larger project on the rapid construction of deep, large-scale, constraint-based, multilingual grammatical resources. I present an application-oriented LFG analysis for Chinese core linguistic phenomena and (in cooperation with PARC) develop a gold-standard dependency-bank of Chinese f-structures for evaluation. Based on the Penn Chinese Treebank, I design and implement two architectures for inducing Chinese LFG resources, one annotation-based and the other dependency conversion-based. I then apply the f-structure acquisition algorithm together with external, state-of-the-art parsers to parsing new text into "proto" f-structures. In order to convert "proto" f-structures into "proper" f-structures or deep dependencies, I present a novel Non-Local Dependency (NLD) recovery algorithm using subcategorisation frames and f-structure paths linking antecedents and traces in NLDs extracted from the automatically-built LFG f-structure treebank. Based on the grammars extracted from the f-structure annotated treebank, I develop a PCFG-based chart generator and a new n-gram based pure dependency generator to realise Chinese sentences from LFG f-structures. The work reported in this thesis is the first effort to scale treebank-based, probabilistic Chinese LFG resources from proof-of-concept research to unrestricted, real text. Although this thesis concentrates on Chinese and LFG, many of the methodologies, e.g. the acquisition of predicate-argument structures, NLD resolution and the PCFG- and dependency n-gram-based generation models, are largely language and formalism independent and should generalise to diverse languages as well as to labelled bilexical dependency representations other than LFG

Head finalization reordering for Chinese-to-Japanese machine translation.” in

Abstract In Statistical Machine Translation, reordering rules have proved useful in extracting bilingual phrases and in decoding during translation between languages that are structurally different. Linguistically motivated rules have been incorporated into Chineseto-Englis

Handbook of Lexical Functional Grammar

Lexical Functional Grammar (LFG) is a nontransformational theory of linguistic structure, first developed in the 1970s by Joan Bresnan and Ronald M. Kaplan, which assumes that language is best described and modeled by parallel structures representing different facets of linguistic organization and information, related by means of functional correspondences. This volume has five parts. Part I, Overview and Introduction, provides an introduction to core syntactic concepts and representations. Part II, Grammatical Phenomena, reviews LFG work on a range of grammatical phenomena or constructions. Part III, Grammatical modules and interfaces, provides an overview of LFG work on semantics, argument structure, prosody, information structure, and morphology. Part IV, Linguistic disciplines, reviews LFG work in the disciplines of historical linguistics, learnability, psycholinguistics, and second language learning. Part V, Formal and computational issues and applications, provides an overview of computational and formal properties of the theory, implementations, and computational work on parsing, translation, grammar induction, and treebanks. Part VI, Language families and regions, reviews LFG work on languages spoken in particular geographical areas or in particular language families. The final section, Comparing LFG with other linguistic theories, discusses LFG work in relation to other theoretical approaches

Automatic Classification of Verb-Direction Constructions In Mandarin Chinese

Processing Multi-Word Expressions (MWEs) presents a challenge for Natural Language Processing (NLP) systems (Sag et al. 2002). In Mandarin Chinese, there are different kinds of MWEs, such as compounding constructions and serial verb constructions containing multiple predicates (Chao 1968). In this project, I will focus on parsing the semantics of a family of constructions called Verb-Direction Constructions (VDCs) in Chinese. Similar to English Verb-Particle Constructions, VDCs include a verb of precondition followed by a directional verb (e.g., na chu (lit &lsquo;take exit&rsquo;) &lsquo;take out&rsquo;). VDC functions include Self-Motion, Caused-Motion, Aspect, Discourse-Connective, and Evidential, among others (Liu et al. 1998). Achieving native speakers&rsquo; interpretation of a language in machine learning systems can support different applications. Inspired by the framework of Sign-Based Construction Grammar (Sag 2012, Michaelis 2009, 2013) as well as Conceptual Metaphor Theory (Lakoff &amp; Johnson 1980, 1998), I conducted three classification tasks. In the first task, I designed a VDC taxonomy, which categorizes distinct functions of VDCs, such as event structures (both causative and non-causative) that involve the movement of an entity through space to a final location. Two versions of the taxonomy were developed and learned. The annotation guideline was mainly based on an analysis of Frame Semantics (Baker, Fillmore, &amp; Lowe 1998) for different VDC events. In the second task, VDCs were annotated as metaphoric and literal expressions, and metaphor detection was performed. The third task makes preliminary steps aimed at detecting the coerced use of VDCs, in which VDCs alter the canonical argument structures of verbs (Goldberg 1995, 1999). This research makes two primary contributions. First, it establishes linguistic analyses of the VDC properties in question, including taxonomies of event types, metaphorical mappings, and coercion, most of which directly support the VDC classification tasks. Second, based on the linguistically motivated categories, it develops an automated method for semantic classification of VDC constructions, surpassing the scope of classification resources previously devised within Chinese NLP (Xue et al. 2000; Xue &amp; Palmer 2009; Huang et al. 2010; Lu &amp; Wang 2017). The system developed potentially supports other NLP applications, such as machine translation, event detection, metaphor processing, and word sense disambiguation.</p

Modeling information structure in a cross-linguistic perspective

This study makes substantial contributions to both the theoretical and computational treatment of information structure, with a specific focus on creating natural language processing applications such as multilingual machine translation systems. The present study first provides cross-linguistic findings in regards to information structure meanings and markings. Building upon such findings, the current model represents information structure within the HPSG/MRS framework using Individual Constraints. The primary goal of the present study is to create a multilingual grammar model of information structure for the LinGO Grammar Matrix system. The present study explores the construction of a grammar library for creating customized grammar incorporating information structure and illustrates how the information structure-based model improves performance of transfer-based machine translation

Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP

Transfer learning, particularly approaches that combine multi-task learning with pre-trained contextualized embeddings and fine-tuning, have advanced the field of Natural Language Processing tremendously in recent years. In this paper we present MaChAmp, a toolkit for easy fine-tuning of contextualized embeddings in multi-task settings. The benefits of MaChAmp are its flexible configuration options, and the support of a variety of natural language processing tasks in a uniform toolkit, from text classification and sequence labeling to dependency parsing, masked language modeling, and text generation.Comment: https://machamp-nlp.github.io