High accuracy for automated translation and information retrieval calls for linguistic annotations at various language levels. The plethora of informal internet content sparked the demand for porting state-of-art natural language processing (NLP) applications to new social media as well as diverse language adaptation. Effort launched by the BOLT (Broad Operational Language Translation) program at DARPA (Defense Advanced Research Projects Agency) successfully addressed the internet information with enhanced NLP systems. BOLT aims for automated translation and linguistic analysis for informal genres of text and speech in online and in-person communication. As a part of this program, the Linguistic Data Consortium (LDC) developed valuable linguistic resources in support of the training and evaluation of such new technologies. This paper focuses on methodologies, infrastructure, and procedure for developing linguistic annotation at various language levels, including Treebank (TB), word alignment (WA), PropBank (PB), and co-reference (CoRef). Inspired by the OntoNotes approach with adaptations to the tasks to reflect the goals and scope of the BOLT project, this effort has introduced more annotation types of informal and free-style genres in English, Chinese and Egyptian Arabic. The corpus produced is by far the largest multi-lingual, multi-level and multi-genre annotation corpus of informal text and speech.

This paper describes the parallel development of an Egyptian Arabic Treebank and a morphological analyzer for Egyptian Arabic (CALIMA). By the very nature of Egyptian Arabic, the data collected is informal, for example Discussion Forum text, which we use for the treebank discussed here. In addition, Egyptian Arabic, like other Arabic dialects, is sufficiently different from Modern Standard Arabic (MSA) that tools and techniques developed for MSA cannot be simply transferred over to work on Egyptian Arabic work. In particular, a morphological analyzer for Egyptian Arabic is needed to mediate between the written text and the segmented, vocalized form used for the syntactic trees. This led to the necessity of a feedback loop between the treebank team and the analyzer team, as improvements in each area were fed to the other. Therefore, by necessity, there needed to be close cooperation between the annotation team and the tool development team, which was to their mutual benefit. Collaboration on this type of challenge, where tools and resources are limited, proved to be remarkably synergistic and opens the way to further fruitful work on Arabic dialects.

Parallel aligned treebanks (PAT) are linguistic corpora annotated with morphological and syntactic structures that are aligned at sentence as well as sub-sentence levels. They are valuable resources for improving machine translation (MT) quality. Recently, there has been an increasing demand for such data, especially for divergent language pairs. The Linguistic Data Consortium (LDC) and its academic partners have been developing Arabic-English and Chinese-English PATs for several years. This paper describes the PAT corpus creation effort for the program GALE (Global Autonomous Language Exploitation) and introduces the potential issues of scaling up this PAT effort for the program BOLT (Broad Operational Language Translation). Based on existing infrastructures and in the light of current annotation process, challenges and approaches, we are exploring new methodologies to address emerging challenges in constructing PATs, including data volume bottlenecks, dialect issues of Arabic languages, and new genre features related to rapidly changing social media. Preliminary experimental results are presented to show the feasibility of the approaches proposed.

The Linguistic Data Consortium and Georgetown University Press are collaborating to create updated editions of bilingual diction- aries that had originally been published in the 1960's for English-speaking learners of Moroccan, Syrian and Iraqi Arabic. In their first editions, these dictionaries used ad hoc Latin-alphabet orthography for each colloquial Arabic dialect, but adopted some proper- ties of Arabic-based writing (collation order of Arabic headwords, clitic attachment to word forms in example phrases); despite their common features, there are notable differences among the three books that impede comparisons across the dialects, as well as com- parisons of each dialect to Modern Standard Arabic. In updating these volumes, we use both Arabic script and International Pho- netic Alphabet orthographies; the former provides a common basis for word recognition across dialects, while the latter provides dialect-specific pronunciations. Our goal is to preserve the full content of the original publications, supplement the Arabic headword inventory with new usages, and produce a uniform lexicon structure expressible via the Lexical Markup Framework (LMF, ISO 24613). To this end, we developed a relational database schema that applies consistently to each dialect, and HTTP-based tools for searching, editing, workflow, review and inventory management.

Treebanking a large corpus of relatively structured speech transcribed from various Arabic Broadcast News (BN) sources has allowed us to begin to address the many challenges of annotating and parsing a speech corpus in Arabic. The now completed Arabic Treebank BN corpus consists of 432,976 source tokens (517,080 tree tokens) in 120 files of manually transcribed news broadcasts. Because news broadcasts are predominantly scripted, most of the transcribed speech is in Modern Standard Arabic (MSA). As such, the lexical and syntactic structures are very similar to the MSA in written newswire data. However, because this is spoken news, cross-linguistic speech effects such as restarts, fillers, hesitations, and repetitions are common. There is also a certain amount of dialect data present in the BN corpus, from on-the-street interviews and similar informal contexts. In this paper, we describe the finished corpus and focus on some of the necessary additions to our annotation guidelines, along with some of the technical challenges of a treebanked speech corpus and an initial parsing evaluation for this data. This corpus will be available to the community in 2012 as an LDC publication.

The Arabic Treebank (ATB) Project at the Linguistic Data Consortium (LDC) has embarked on a large corpus of Broadcast News (BN) transcriptions, and this has led to a number of new challenges for the data processing and annotation procedures that were originally developed for Arabic newswire text (ATB1, ATB2 and ATB3). The corpus requirements currently posed by the DARPA GALE Program, including English translation of Arabic BN transcripts, word-level alignment of Arabic and English data, and creation of a corresponding English Treebank, place significant new constraints on ATB corpus creation, and require careful coordination among a wide assortment of concurrent activities and participants. Nonetheless, in spite of the new challenges posed by BN data, the ATB’s newly improved pipeline and revised annotation guidelines for newswire have proven to be robust enough that very few changes were necessary to account for the new genre of data. This paper presents the points where some adaptation has been necessary, and the overall pipeline as used in the production of BN ATB data.

Complications arise for standoff annotation when the annotation is not on the source text itself, but on a more abstract representation. This is particularly the case in a language such as Arabic with morphological and orthographic challenges, and we discuss various aspects of these issues in the context of the Arabic Treebank. The Standard Arabic Morphological Analyzer (SAMA) is closely integrated into the annotation workflow, as the basis for the abstraction between the explicit source text and the more abstract token representation. However, this integration with SAMA gives rise to various problems for the annotation workflow and for maintaining the link between the Treebank and SAMA. In this paper we discuss how we have overcome these problems with consistent and more precise categorization of all of the tokens for their relationship with SAMA. We also discuss how we have improved the creation of several distinct alternative forms of the tokens used in the syntactic trees. As a result, the Treebank provides a resource relating the different forms of the same underlying token with varying degrees of vocalization, in terms of how they relate (1) to each other, (2) to the syntactic structure, and (3) to the morphological analyzer.

The Arabic Treebank (ATB), released by the Linguistic Data Consortium, contains multiple annotation files for each source file, due in part to the role of diacritic inclusion in the annotation process. The data is made available in both “vocalized” and “unvocalized” forms, with and without the diacritic marks, respectively. Much parsing work with the ATB has used the unvocalized form, on the basis that it more closely represents the “real-world” situation. We point out some problems with this usage of the unvocalized data and explain why the unvocalized form does not in fact represent “real-world” data. This is due to some aspects of the treebank annotation that to our knowledge have never before been published.

The Arabic Treebank team at the Linguistic Data Consortium has significantly revised and enhanced its annotation guidelines and procedure over the past year. Improvements were made to both the morphological and syntactic annotation guidelines, and annotators were trained in the new guidelines, focusing on areas of low inter-annotator agreement. The revised guidelines are now being applied in annotation production, and the combination of the revised guidelines and a period of intensive annotator training has raised inter-annotator agreement f-measure scores already and has also improved parsing results.

In this paper, we present the details of creating a pilot Arabic proposition bank (Propbank). Propbanks exist for both English and Chinese. However the morphological and syntactic expression of linguistic phenomena in Arabic yields a very different type of process in creating an Arabic propbank. Hence, we highlight those characteristics of Arabic that make creating a propbank for the language a different challenge compared to the creation of an English Propbank.We believe that many of the lessons learned in dealing with Arabic could generalise to other languages that exhibit equally rich morphology and relatively free word order.

In Arabic speech communities, there is a diglossic gap between written/formal Modern Standard Arabic (MSA) and spoken/casual colloquial dialectal Arabic (DA): the common spoken language has no standard representation in written form, while the language observed in texts has limited occurrence in speech. Hence the task of developing language resources to describe and model DA speech involves extra work to establish conventions for orthography and grammatical analysis. We describe work being done at the LDC to develop lexicons for DA, comprising pronunciation, morphology and part-of-speech labeling for word forms in recorded speech. Components of the approach are: (a) a two-layer transcription, providing a consonant-skeleton form and a pronunciation form; (b) manual annotation of morphology, part-of-speech and English gloss, followed by development of automatic word parsers modeled on the Buckwalter Morphological Analyzer for MSA; (c) customized user interfaces and supporting tools for all stages of annotation; and (d) a relational database for storing, emending and publishing the transcription corpus as well as the lexicon.

In this paper, we describe the methodological procedures and issues that emerged from the development of a pilot Levantine Arabic Treebank (LATB) at the Linguistic Data Consortium (LDC) and its use at the Johns Hopkins University (JHU) Center for Language and Speech Processing workshop on Parsing Arabic Dialects (PAD). This pilot, consisting of morphological and syntactic annotation of approximately 26,000 words of Levantine Arabic conversational telephone speech, was developed under severe time constraints; hence the LDC team drew on their experience in treebanking Modern Standard Arabic (MSA) text. The resulting Levantine dialect treebanked corpus was used by the PAD team to develop and evaluate parsers for Levantine dialect texts. The parsers were trained on MSA resources and adapted using dialect-MSA lexical resources (some developed especially for this task) and existing linguistic knowledge about syntactic differences between MSA and dialect. The use of the LATB for development and evaluation of syntactic parsers allowed the PAD team to provide feedbasck to the LDC treebank developers. In this paper, we describe the creation of resources for this corpus, as well as transformations on the corpus to eliminate speech effects and lessen the gap between our pre-existing MSA resources and the new dialectal corpus

Linguistic Data Consortium has recently embarked on an effort to create integrated linguistic resources and related infrastructure for language exploitation technologies within the DARPA GALE (Global Autonomous Language Exploitation) Program. GALE targets an end-to-end system consisting of three major engines: Transcription, Translation and Distillation. Multilingual speech or text from a variety of genres is taken as input and English text is given as output, with information of interest presented in an integrated and consolidated fashion to the end user. GALE's goals require a quantum leap in the performance of human language technology, while also demanding solutions that are more intelligent, more robust, more adaptable, more efficient and more integrated. LDC has responded to this challenge with a comprehensive approach to linguistic resource development designed to support GALE's research and evaluation needs and to provide lasting resources for the larger Human Language Technology community.

Arabic diacritization (referred to sometimes as vocalization or vowelling), defined as the full or partial representation of short vowels, shadda (consonantal length or germination), tanween (nunation or definiteness), and hamza (the glottal stop and its support letters), is still largely understudied in the current NLP literature. In this paper, the lack of diacritics in standard Arabic texts is presented as a major challenge to most Arabic natural language processing tasks, including parsing. Recent studies (Messaoudi, et al. 2004; Vergyri & Kirchhoff 2004; Zitouni, et al. 2006 and Maamouri, et al. forthcoming) about the place and impact of diacritization in text-based NLP research are presented along with an analysis of the weight of the missing diacritics on Treebank morphological and syntactic analyses and the impact on parser development.