Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Digital Watermarking

Parsing unrestricted text is useful for many language technology applications but requires parsing methods that are both robust and efficient. MaltParser is a language-independent system for data-driven dependency parsing that can be used to induce a parser for a new language from a treebank sample in a simple yet flexible manner. Experimental evaluation confirms that MaltParser can achieve robust, efficient and accurate parsing for a wide range of languages without language-specific enhancements and with rather limited amounts of training data.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1351324906004505

MaltParser: A language-independent system for data-driven dependency parsing

Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This article provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to many applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.

A Survey of the Usages of Deep Learning for Natural Language Processing

The CoNLL-2012 shared task involved predicting coreference in three languages -- English, Chinese and Arabic -- using OntoNotes data. It was a follow-on to the English-only task organized in 2011. Until the creation of the OntoNotes corpus, resources in this subfield of language processing have tended to be limited to noun phrase coreference, often on a restricted set of entities, such as ACE entities. OntoNotes provides a large-scale corpus of general anaphoric coreference not restricted to noun phrases or to a specified set of entity types and covering multiple languages. OntoNotes also provides additional layers of integrated annotation, capturing additional shallow semantic structure. This paper briefly describes the OntoNotes annotation (coreference and other layers) and then describes the parameters of the shared task including the format, pre-processing information, evaluation criteria, and presents and discusses the results achieved by the participating systems. Being a task that has a complex evaluation history, and multiple evalation conditions, it has, in the past, been difficult to judge the improvement in new algorithms over previously reported results. Having a standard test set and evaluation parameters, all based on a resource that provides multiple integrated annotation layers (parses, semantic roles, word senses, named entities and coreference) that could support joint models, should help to energize ongoing research in the task of entity and event coreference.

CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes

We present several improvements to unlexicalized parsing with hierarchically state-split PCFGs. First, we present a novel coarse-to-fine method in which a grammar’s own hierarchical projections are used for incremental pruning, including a method for efficiently computing projections of a grammar without a treebank. In our experiments, hierarchical pruning greatly accelerates parsing with no loss in empirical accuracy. Second, we compare various inference procedures for state-split PCFGs from the standpoint of risk minimization, paying particular attention to their practical tradeoffs. Finally, we present multilingual experiments which show that parsing with hierarchical state-splitting is fast and accurate in multiple languages and domains, even without any language-specific tuning.

Improved Inference for Unlexicalized Parsing

With growing interest in Chinese Language Processing, numerous NLP tools (e.g., word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on corpora with different segmentation criteria, part-of-speech tagsets and bracketing guidelines, and therefore, comparisons are difficult. As a first step towards addressing this issue, we have been preparing a large bracketed corpus since late 1998. The first two installments of the corpus, 250 thousand words of data, fully segmented, POS-tagged and syntactically bracketed, have been released to the public via LDC (www.ldc.upenn.edu). In this paper, we discuss several Chinese linguistic issues and their implications for our treebanking efforts and how we address these issues when developing our annotation guidelines. We also describe our engineering strategies to improve speed while ensuring annotation quality.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/26220D5C308A1A65B1D0636AE9A9FC72/S135132490400364Xa.pdf/div-class-title-the-penn-chinese-treebank-phrase-structure-annotation-of-a-large-corpus-div.pdf

The Penn Chinese TreeBank: Phrase structure annotation of a large corpus

In this paper we address issues related to building a large-scale Chinese corpus. We try to answer four questions: (i) how to speed up annotation, (ii) how to maintain high annotation quality, (iii) for what purposes is the corpus applicable, and finally (iv) what future work we anticipate.

/pdf/building-a-large-scale-annotated-chinese-corpus-2mtrh6fkan.pdf

Building a large-scale annotated Chinese corpus

With growing interest in Chinese Language Processing, numerous NLP tools (e.g. word segmenters, part-of-speech taggers, and parsers) for Chinese have been developed all over the world. However, since no large-scale bracketed corpora are available to the public, these tools are trained on the corpora with different segmentation criteria, part-of-speech tagsets and bracketing guidelines, and therefore, comparisons are difficult. As a first step towards addressing this issue, we have been preparing a 100-thousand-word bracketed corpus since late 1998 and plan to release it to the public summer 2000. In this paper, we will address several challenges in building the corpus, namely, creating annotation guidelines, ensuring annotation accuracy and maintaining a high level of community involvement.

http://www.lrec-conf.org/proceedings/lrec2000/pdf/287.pdf

Developing Guidelines and Ensuring Consistency for Chinese Text Annotation

In this paper we report on our experiments on automatic Word Sense Disambiguation using a maximum entropy approach for both English and Chinese verbs. We compare the difficulty of the sense-tagging tasks in the two languages and investigate the types of contextual features that are useful for each language. Our experimental results suggest that while richer linguistic features are useful for English WSD, they may not be as beneficial for Chinese.

/pdf/simple-features-for-chinese-word-sense-disambiguation-29deggzizg.pdf

Simple features for Chinese word sense disambiguation

Corpora of phrase-structure-annotated text, or treebanks, are useful for supervised training of statistical models for natural language processing, as well as for corpus linguistics. Their primary drawback, however, is that they are very time-consuming to produce. To alleviate this problem, the standard approach is to make two passes over the text: first, parse the text automatically, then correct the parser output by hand.

Fu-Dong Chiou

Papers

The Penn Chinese TreeBank: Phrase structure annotation of a large corpus

Building a large-scale annotated Chinese corpus

Developing Guidelines and Ensuring Consistency for Chinese Text Annotation

Simple features for Chinese word sense disambiguation

Facilitating treebank annotation using a statistical parser