An integrated architecture for shallow and deep processing

We present an architecture for the integration of shallow and deep NLP components which is aimed at flexible combination of different language technologies for a range of practical current and future applications. In particular, we describe the integration of a high-level HPSG parsing system with different high-performance shallow components, ranging from named entity recognition to chunk parsing and shallow clause recognition. The NLP components enrich a representation of natural language text with layers of new XML meta-information using a single shared data structure, called the text chart. We describe details of the integration methods, and show how information extraction and language checking applications for realworld German text benefit from a deep grammatical analysis

Statistical parsing of noun phrase structure

Noun phrases (NPs) are a crucial part of natural language, exhibiting in many cases an extremely complex structure. However, NP structure is largely ignored by the statistical parsing field, as the most widely-used corpus is not annotated with it. This lack of gold-standard data has restricted all previous efforts to parse NPs, making it impossible to perform the supervised experiments that have achieved high performance in so many Natural Language Processing (NLP) tasks. We comprehensively solve this problem by manually annotating NP structure for the entire Wall Street Journal section of the Penn Treebank. The inter-annotator agreement scores that we attain refute the belief that the task is too difficult, and demonstrate that consistent NP annotation is possible. Our gold-standard NP data is now available and will be useful for all parsers. We present three statistical methods for parsing NP structure. Firstly, we apply the Collins (2003) model, and find that its recovery of NP structure is significantly worse than its overall performance. Through much experimentation, we determine that this is not a result of the special base-NP model used by the parser, but primarily caused by a lack of lexical information. Secondly, we construct a wide-coverage, large-scale NP Bracketing system, applying a supervised model to achieve excellent results. Our Penn Treebank data set, which is orders of magnitude larger than those used previously, makes this possible for the first time. We then implement and experiment with a wide variety of features in order to determine an optimal model. Having achieved this, we use the NP Bracketing system to reanalyse NPs outputted by the Collins (2003) parser. Our post-processor outperforms this state-of-the-art parser. For our third model, we convert the NP data to CCGbank (Hockenmaier and Steedman, 2007), a corpus that uses the Combinatory Categorial Grammar (CCG) formalism. We experiment with a CCG parser and again, implement features that improve performance. We also evaluate the CCG parser against the Briscoe and Carroll (2006) reannotation of DepBank (King et al., 2003), another corpus that annotates NP structure. This supplies further evidence that parser performance is increased by improving the representation of NP structure. Finally, the error analysis we carry out on the CCG data shows that again, a lack of lexicalisation causes difficulties for the parser. We find that NPs are particularly reliant on this lexical information, due to their exceptional productivity and the reduced explicitness present in modifier sequences. Our results show that NP parsing is a significantly harder task than parsing in general. This thesis comprehensively analyses the NP parsing task. Our contributions allow wide-coverage, large-scale NP parsers to be constructed for the first time, and motivate further NP parsing research for the future. The results of our work can provide significant benefits for many NLP tasks, as the crucial information contained in NP structure is now available for all downstream systems

Recovering capitalization and punctuation marks for automatic speech recognition: case study for Portuguese broadcast news

The following material presents a study about recovering punctuation marks, and capitalization information from European Portuguese broadcast news speech transcriptions. Different approaches were tested for capitalization, both generative and discriminative, using: finite state transducers automatically built from language models; and maximum entropy models. Several resources were used, including lexica, written newspaper corpora and speech transcriptions. Finite state transducers produced the best results for written newspaper corpora, but the maximum entropy approach also proved to be a good choice, suitable for the capitalization of speech transcriptions, and allowing straightforward on-the-fly capitalization. Evaluation results are presented both for written newspaper corpora and for broadcast news speech transcriptions. The frequency of each punctuation mark in BN speech transcriptions was analyzed for three different languages: English, Spanish and Portuguese. The punctuation task was performed using a maximum entropy modeling approach, which combines different types of information both lexical and acoustic. The contribution of each feature was analyzed individually and separated results for each focus condition are given, making it possible to analyze the performance differences between planned and spontaneous speech. All results were evaluated on speech transcriptions of a Portuguese broadcast news corpus. The benefits of enriching speech recognition with punctuation and capitalization are shown in an example, illustrating the effects of described experiments into spoken texts.info:eu-repo/semantics/acceptedVersio

An Arabic CCG approach for determining constituent types from Arabic Treebank

AbstractConverting a treebank into a CCGbank opens the respective language to the sophisticated tools developed for Combinatory Categorial Grammar (CCG) and enriches cross-linguistic development. The conversion is primarily a three-step process: determining constituents’ types, binarization, and category conversion. Usually, this process involves a preprocessing step to the Treebank of choice for correcting brackets and normalizing tags for any changes that were introduced during the manual annotation, as well as extracting morpho-syntactic information that is necessary for determining constituents’ types. In this article, we describe the required preprocessing step on the Arabic Treebank, as well as how to determine Arabic constituents’ types. We conducted an experiment on parts 1 and 2 of the Penn Arabic Treebank (PATB) aimed at converting the PATB into an Arabic CCGbank. The performance of our algorithm when applied to ATB1v2.0 & ATB2v2.0 was 99% identification of head nodes and 100% coverage over the Treebank data

Treebanking in VIT: from Phrase Structure to Dependency Representation

In this chapter, we are dealing with treebanks and their applications. We describe VIT (Venice Italian Treebank), focusing on the syntactic-semantic features of the treebank that are partly dependent on the adopted tagset, partly on the reference linguistic theory, and, lastly - as in every treebank - on the chosen language: Italian. By discussing examples taken from treebanks available in other languages, we show the theoretical and practical differences and motivations that underlie our approach. Finally, we discuss the quantitative analysis of the data of our treebank and compare them to other treebanks. In general, we try to substantiate the claim that treebanking grammars or parsers strongly depend on the chosen treebank; and eventually this process seems to depend both on factors such as the adopted linguistic frame-work for structural description and, ultimately, the described language

Overview of the SPMRL 2013 Shared Task: A Cross-Framework Evaluation of Parsing Morphologically Rich Languages

International audienceThis paper reports on the first shared task on statistical parsing of morphologically rich lan- guages (MRLs). The task features data sets from nine languages, each available both in constituency and dependency annotation. We report on the preparation of the data sets, on the proposed parsing scenarios, and on the eval- uation metrics for parsing MRLs given dif- ferent representation types. We present and analyze parsing results obtained by the task participants, and then provide an analysis and comparison of the parsers across languages and frameworks, reported for gold input as well as more realistic parsing scenarios