A Formal Framework for Linguistic Annotation

`Linguistic annotation' covers any descriptive or analytic notations applied to raw language data. The basic data may be in the form of time functions -- audio, video and/or physiological recordings -- or it may be textual. The added notations may include transcriptions of all sorts (from phonetic features to discourse structures), part-of-speech and sense tagging, syntactic analysis, `named entity' identification, co-reference annotation, and so on. While there are several ongoing efforts to provide formats and tools for such annotations and to publish annotated linguistic databases, the lack of widely accepted standards is becoming a critical problem. Proposed standards, to the extent they exist, have focussed on file formats. This paper focuses instead on the logical structure of linguistic annotations. We survey a wide variety of existing annotation formats and demonstrate a common conceptual core, the annotation graph. This provides a formal framework for constructing, maintaining and searching linguistic annotations, while remaining consistent with many alternative data structures and file formats.Comment: 49 page

Windows into Sensory Integration and Rates in Language Processing: Insights from Signed and Spoken Languages

This dissertation explores the hypothesis that language processing proceeds in "windows" that correspond to representational units, where sensory signals are integrated according to time-scales that correspond to the rate of the input. To investigate universal mechanisms, a comparison of signed and spoken languages is necessary. Underlying the seemingly effortless process of language comprehension is the perceiver's knowledge about the rate at which linguistic form and meaning unfold in time and the ability to adapt to variations in the input. The vast body of work in this area has focused on speech perception, where the goal is to determine how linguistic information is recovered from acoustic signals. Testing some of these theories in the visual processing of American Sign Language (ASL) provides a unique opportunity to better understand how sign languages are processed and which aspects of speech perception models are in fact about language perception across modalities. The first part of the dissertation presents three psychophysical experiments investigating temporal integration windows in sign language perception by testing the intelligibility of locally time-reversed sentences. The findings demonstrate the contribution of modality for the time-scales of these windows, where signing is successively integrated over longer durations (~ 250-300 ms) than in speech (~ 50-60 ms), while also pointing to modality-independent mechanisms, where integration occurs in durations that correspond to the size of linguistic units. The second part of the dissertation focuses on production rates in sentences taken from natural conversations of English, Korean, and ASL. Data from word, sign, morpheme, and syllable rates suggest that while the rate of words and signs can vary from language to language, the relationship between the rate of syllables and morphemes is relatively consistent among these typologically diverse languages. The results from rates in ASL also complement the findings in perception experiments by confirming that time-scales at which phonological units fluctuate in production match the temporal integration windows in perception. These results are consistent with the hypothesis that there are modality-independent time pressures for language processing, and discussions provide a synthesis of converging findings from other domains of research and propose ideas for future investigations

Non-native listeners' recognition of high-variability speech using PRESTO

BACKGROUND: Natural variability in speech is a significant challenge to robust successful spoken word recognition. In everyday listening environments, listeners must quickly adapt and adjust to multiple sources of variability in both the signal and listening environments. High-variability speech may be particularly difficult to understand for non-native listeners, who have less experience with the second language (L2) phonological system and less detailed knowledge of sociolinguistic variation of the L2. PURPOSE: The purpose of this study was to investigate the effects of high-variability sentences on non-native speech recognition and to explore the underlying sources of individual differences in speech recognition abilities of non-native listeners. RESEARCH DESIGN: Participants completed two sentence recognition tasks involving high-variability and low-variability sentences. They also completed a battery of behavioral tasks and self-report questionnaires designed to assess their indexical processing skills, vocabulary knowledge, and several core neurocognitive abilities. STUDY SAMPLE: Native speakers of Mandarin (n = 25) living in the United States recruited from the Indiana University community participated in the current study. A native comparison group consisted of scores obtained from native speakers of English (n = 21) in the Indiana University community taken from an earlier study. DATA COLLECTION AND ANALYSIS: Speech recognition in high-variability listening conditions was assessed with a sentence recognition task using sentences from PRESTO (Perceptually Robust English Sentence Test Open-Set) mixed in 6-talker multitalker babble. Speech recognition in low-variability listening conditions was assessed using sentences from HINT (Hearing In Noise Test) mixed in 6-talker multitalker babble. Indexical processing skills were measured using a talker discrimination task, a gender discrimination task, and a forced-choice regional dialect categorization task. Vocabulary knowledge was assessed with the WordFam word familiarity test, and executive functioning was assessed with the BRIEF-A (Behavioral Rating Inventory of Executive Function - Adult Version) self-report questionnaire. Scores from the non-native listeners on behavioral tasks and self-report questionnaires were compared with scores obtained from native listeners tested in a previous study and were examined for individual differences. RESULTS: Non-native keyword recognition scores were significantly lower on PRESTO sentences than on HINT sentences. Non-native listeners' keyword recognition scores were also lower than native listeners' scores on both sentence recognition tasks. Differences in performance on the sentence recognition tasks between non-native and native listeners were larger on PRESTO than on HINT, although group differences varied by signal-to-noise ratio. The non-native and native groups also differed in the ability to categorize talkers by region of origin and in vocabulary knowledge. Individual non-native word recognition accuracy on PRESTO sentences in multitalker babble at more favorable signal-to-noise ratios was found to be related to several BRIEF-A subscales and composite scores. However, non-native performance on PRESTO was not related to regional dialect categorization, talker and gender discrimination, or vocabulary knowledge. CONCLUSIONS: High-variability sentences in multitalker babble were particularly challenging for non-native listeners. Difficulty under high-variability testing conditions was related to lack of experience with the L2, especially L2 sociolinguistic information, compared with native listeners. Individual differences among the non-native listeners were related to weaknesses in core neurocognitive abilities affecting behavioral control in everyday life

Phonologically Conditioned Allomorphy and UR Constraints

This dissertation provides a new model of the phonology-morphology interface, focusing on Phonologically Conditioned Allomorphy (PCA). In this model, UR selection occurs during the phonological component, and mappings between meanings and URs are encoded as violable constraints, called UR constraints (Boersma 2001; Pater et al. 2012). Ranking UR constraints captures many empirical generalizations about PCA, such as similarities between PCA and phonological alternations, the existence of defaults, and the interaction of PCA and phonological repairs (epenthesis, deletion, etc.). Since PCA follows from the ranking or weighting of constraints, patterns of PCA can be learned using existing learning algorithms, and modeling variation in PCA is straightforward. The main empirical conclusion is that some cases of PCA are driven by the general phonological grammar, and are not the result of subcategorization. This conclusion follows from three case studies: English a and an, French liaison, and English suffixes -(a)licious and -(a)thon. For each of these cases, PCA is closely integrated into the phonology of the language. The same ranking of constraints can be used to capture both alternations and PCA, and phonological repairs like epenthesis and deletion conspire with PCA to avoid marked structures. Each of these cases comes with novel data and analyses. For English a and an, the selection of the article interacts with h-deletion and ʔ-epenthesis, conspiring with both to avoid hiatus. In French liaison, the default allomorph is often unpredictable, and liaison in some words conspires with n-epenthesis to avoid hiatus. For English -(a)licious and -(a)thon, PCA avoids hiatus and stress clash, and the suffix -(a)licious conspires with the Rhythm Rule to avoid stress clashes. For each case study, I also present variable data, analyzed with UR constraints in Maximum Entropy Harmonic Grammar (Goldwater and Johnson 2003)

Developing Methods and Resources for Automated Processing of the African Language Igbo

Natural Language Processing (NLP) research is still in its infancy in Africa. Most of languages in Africa have few or zero NLP resources available, of which Igbo is among those at zero state. In this study, we develop NLP resources to support NLP-based research in the Igbo language. The springboard is the development of a new part-of-speech (POS) tagset for Igbo (IgbTS) based on a slight adaptation of the EAGLES guideline as a result of language internal features not recognized in EAGLES. The tagset consists of three granularities: fine-grain (85 tags), medium-grain (70 tags) and coarse-grain (15 tags). The medium-grained tagset is to strike a balance between the other two grains for practical purpose. Following this is the preprocessing of Igbo electronic texts through normalization and tokenization processes. The tokenizer is developed in this study using the tagset definition of a word token and the outcome is an Igbo corpus (IgbC) of about one million tokens. This IgbTS was applied to a part of the IgbC to produce the first Igbo tagged corpus (IgbTC). To investigate the effectiveness, validity and reproducibility of the IgbTS, an inter-annotation agreement (IAA) exercise was undertaken, which led to the revision of the IgbTS where necessary. A novel automatic method was developed to bootstrap a manual annotation process through exploitation of the by-products of this IAA exercise, to improve IgbTC. To further improve the quality of the IgbTC, a committee of taggers approach was adopted to propose erroneous instances on IgbTC for correction. A novel automatic method that uses knowledge of affixes to flag and correct all morphologically-inflected words in the IgbTC whose tags violate their status as not being morphologically-inflected was also developed and used. Experiments towards the development of an automatic POS tagging system for Igbo using IgbTC show good accuracy scores comparable to other languages that these taggers have been tested on, such as English. Accuracy on the words previously unseen during the taggers’ training (also called unknown words) is considerably low, and much lower on the unknown words that are morphologically-complex, which indicates difficulty in handling morphologically-complex words in Igbo. This was improved by adopting a morphological reconstruction method (a linguistically-informed segmentation into stems and affixes) that reformatted these morphologically-complex words into patterns learnable by machines. This enables taggers to use the knowledge of stems and associated affixes of these morphologically-complex words during the tagging process to predict their appropriate tags. Interestingly, this method outperforms other methods that existing taggers use in handling unknown words, and achieves an impressive increase for the accuracy of the morphologically-inflected unknown words and overall unknown words. These developments are the first NLP toolkit for the Igbo language and a step towards achieving the objective of Basic Language Resources Kits (BLARK) for the language. This IgboNLP toolkit will be made available for the NLP community and should encourage further research and development for the language

Multiple Media Correlation: Theory and Applications

This thesis introduces multiple media correlation, a new technology for the automatic alignment of multiple media objects such as text, audio, and video. This research began with the question: what can be learned when multiple multimedia components are analyzed simultaneously? Most ongoing research in computational multimedia has focused on queries, indexing, and retrieval within a single media type. Video is compressed and searched independently of audio, text is indexed without regard to temporal relationships it may have to other media data. Multiple media correlation provides a framework for locating and exploiting correlations between multiple, potentially heterogeneous, media streams. The goal is computed synchronization, the determination of temporal and spatial alignments that optimize a correlation function and indicate commonality and synchronization between media objects. The model also provides a basis for comparison of media in unrelated domains. There are many real-world applications for this technology, including speaker localization, musical score alignment, and degraded media realignment. Two applications, text-to-speech alignment and parallel text alignment, are described in detail with experimental validation. Text-to-speech alignment computes the alignment between a textual transcript and speech-based audio. The presented solutions are effective for a wide variety of content and are useful not only for retrieval of content, but in support of automatic captioning of movies and video. Parallel text alignment provides a tool for the comparison of alternative translations of the same document that is particularly useful to the classics scholar interested in comparing translation techniques or styles. The results presented in this thesis include (a) new media models more useful in analysis applications, (b) a theoretical model for multiple media correlation, (c) two practical application solutions that have wide-spread applicability, and (d) Xtrieve, a multimedia database retrieval system that demonstrates this new technology and demonstrates application of multiple media correlation to information retrieval. This thesis demonstrates that computed alignment of media objects is practical and can provide immediate solutions to many information retrieval and content presentation problems. It also introduces a new area for research in media data analysis

Developing Methods and Resources for Automated Processing of the African Language Igbo

Natural Language Processing (NLP) research is still in its infancy in Africa. Most of languages in Africa have few or zero NLP resources available, of which Igbo is among those at zero state. In this study, we develop NLP resources to support NLP-based research in the Igbo language. The springboard is the development of a new part-of-speech (POS) tagset for Igbo (IgbTS) based on a slight adaptation of the EAGLES guideline as a result of language internal features not recognized in EAGLES. The tagset consists of three granularities: fine-grain (85 tags), medium-grain (70 tags) and coarse-grain (15 tags). The medium-grained tagset is to strike a balance between the other two grains for practical purpose. Following this is the preprocessing of Igbo electronic texts through normalization and tokenization processes. The tokenizer is developed in this study using the tagset definition of a word token and the outcome is an Igbo corpus (IgbC) of about one million tokens. This IgbTS was applied to a part of the IgbC to produce the first Igbo tagged corpus (IgbTC). To investigate the effectiveness, validity and reproducibility of the IgbTS, an inter-annotation agreement (IAA) exercise was undertaken, which led to the revision of the IgbTS where necessary. A novel automatic method was developed to bootstrap a manual annotation process through exploitation of the by-products of this IAA exercise, to improve IgbTC. To further improve the quality of the IgbTC, a committee of taggers approach was adopted to propose erroneous instances on IgbTC for correction. A novel automatic method that uses knowledge of affixes to flag and correct all morphologically-inflected words in the IgbTC whose tags violate their status as not being morphologically-inflected was also developed and used. Experiments towards the development of an automatic POS tagging system for Igbo using IgbTC show good accuracy scores comparable to other languages that these taggers have been tested on, such as English. Accuracy on the words previously unseen during the taggers’ training (also called unknown words) is considerably low, and much lower on the unknown words that are morphologically-complex, which indicates difficulty in handling morphologically-complex words in Igbo. This was improved by adopting a morphological reconstruction method (a linguistically-informed segmentation into stems and affixes) that reformatted these morphologically-complex words into patterns learnable by machines. This enables taggers to use the knowledge of stems and associated affixes of these morphologically-complex words during the tagging process to predict their appropriate tags. Interestingly, this method outperforms other methods that existing taggers use in handling unknown words, and achieves an impressive increase for the accuracy of the morphologically-inflected unknown words and overall unknown words. These developments are the first NLP toolkit for the Igbo language and a step towards achieving the objective of Basic Language Resources Kits (BLARK) for the language. This IgboNLP toolkit will be made available for the NLP community and should encourage further research and development for the language