Cognitive Information Processing

Contains research objectives.National Institutes of Health (Grant 5 POI GM14940-06)National Institutes of Health (Grant 1 P01 GM19428-01)National Science Foundation (Grant GK-33736X)Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DAAB07-71-C-0300Grant from the Associated PressPeter Bent Brigham Hospital Purchase Orders F64088Peter Bent Brigham Hospital Purchase Orders F6580

An Investigation of Reading Development Through Sensitivity to Sublexical Units

The present dissertation provides a novel perspective to the study of reading, focusing on sensitivity to sublexical units across reading development. Work towards this thesis has been conducted at SISSA and Macquarie University. The first study is an eye tracking experiment on natural reading, with 140 developing readers and 33 adult participants, who silently read multiline passages from story books in Italian. A developmental database of eye tracking during natural reading was created, filling a gap in the literature. We replicated well-documented developmental trends of reading behavior (e.g., reading rate and skipping rate increasing with age) and effects of word length and frequency on eye tracking measures. The second study, in collaboration with Dr Jon Carr, is a methodological paper presenting algorithms for accuracy enhancement of eye tracking recordings in multiline reading. Using the above-mentioned dataset and computational simulations, we assessed the performance of several algorithms (including two novel methods that we proposed) on the correction of vertical drift, the progressive displacement of fixation registrations on the vertical axis over time. We provided guidance for eye tracking researchers in the application of these methods, and one of the novel algorithms (based on Dynamic Time Warping) proved particularly promising in realigning fixations, especially in child recordings. This manuscript has recently been accepted for publication in Behavior Research Methods. In the third study, I examined sensitivity to statistical regularities in letter co-occurrence throughout reading development, by analysing the effects of n-gram frequency metrics on eye-tracking measures. To this end, the EyeReadIt eye-tracking corpus (presented in the first study) was used. Our results suggest that n-gram frequency effects (in particular related to maximum/average frequency metrics) are present even in developing readers, suggesting that sensitivity to sublexical orthographic regularities in reading is present as soon as the developing reading system can pick it up \u2013 in the case of this study, as early as in third grade. The results bear relevant implications for extant theories of learning to read, which largely overlook the contribution of statistical learning to reading acquisition. The fourth study is a magnetoencephalography experiment conducted at Macquarie University, in collaboration with Dr Lisi Beyersmann, Prof Paul Sowman, and Prof Anne Castles, on 28 adults and 17 children (5th and 6th grade). We investigated selective neural responses to morphemes at different stages of reading development, using Fast Periodic Visual Stimulation (FPVS) combined with an oddball design. Participants were presented with rapid sequences (6 Hz) of pseudoword combinations of stem/nonstem and suffix/nonsuffix components. Interleaved in this stream, oddball stimuli appeared periodically every 5 items (1.2 Hz) and were specifically designed to examine stem or suffix detection (e.g., stem+suffix oddballs, such as softity, were embedded in a sequence of nonstem+suffix base items, such as terpity). We predicted that neural responses at the oddball stimulation frequency (1.2 Hz) would reflect the detection of morphemes in the oddball stimuli. Sensor-level analysis revealed a selective response in a left occipito-temporal region of interest when the oddball stimuli were fully decomposable pseudowords. This response emerged for adults and children alike, showing that automatic morpheme identification occurs at relatively early stages of reading development, in line with major accounts of morphological decomposition. Critically, these findings also suggest that morpheme identification is modulated by the context in which the morphemes appear

Towards Robust Word Embeddings for Noisy Texts

[Abstract] Research on word embeddings has mainly focused on improving their performance on standard corpora, disregarding the difficulties posed by noisy texts in the form of tweets and other types of non-standard writing from social media. In this work, we propose a simple extension to the skipgram model in which we introduce the concept of bridge-words, which are artificial words added to the model to strengthen the similarity between standard words and their noisy variants. Our new embeddings outperform baseline models on noisy texts on a wide range of evaluation tasks, both intrinsic and extrinsic, while retaining a good performance on standard texts. To the best of our knowledge, this is the first explicit approach at dealing with these types of noisy texts at the word embedding level that goes beyond the support for out-of-vocabulary words.Ministerio de Economía, Industria y Competitividad. MINECO; TIN2017-85160-C2-2-RMinisterio de Economía, Industria y Competitividad. MINECO; TIN2017-85160-C2-1-REuropean Social Fund. ESF; BES-2015-073768Xunta de Galicia; ED431D 2017/12Xunta de Galicia; ED431B 2017/01Xunta de Galicia; ED431C 2020/11Xunta de Galicia; ED431G/0

Probabilistic Modelling of Morphologically Rich Languages

This thesis investigates how the sub-structure of words can be accounted for in probabilistic models of language. Such models play an important role in natural language processing tasks such as translation or speech recognition, but often rely on the simplistic assumption that words are opaque symbols. This assumption does not fit morphologically complex language well, where words can have rich internal structure and sub-word elements are shared across distinct word forms. Our approach is to encode basic notions of morphology into the assumptions of three different types of language models, with the intention that leveraging shared sub-word structure can improve model performance and help overcome data sparsity that arises from morphological processes. In the context of n-gram language modelling, we formulate a new Bayesian model that relies on the decomposition of compound words to attain better smoothing, and we develop a new distributed language model that learns vector representations of morphemes and leverages them to link together morphologically related words. In both cases, we show that accounting for word sub-structure improves the models' intrinsic performance and provides benefits when applied to other tasks, including machine translation. We then shift the focus beyond the modelling of word sequences and consider models that automatically learn what the sub-word elements of a given language are, given an unannotated list of words. We formulate a novel model that can learn discontiguous morphemes in addition to the more conventional contiguous morphemes that most previous models are limited to. This approach is demonstrated on Semitic languages, and we find that modelling discontiguous sub-word structures leads to improvements in the task of segmenting words into their contiguous morphemes.Comment: DPhil thesis, University of Oxford, submitted and accepted 2014. http://ora.ox.ac.uk/objects/uuid:8df7324f-d3b8-47a1-8b0b-3a6feb5f45c

Lexical cohesion analysis for topic segmentation, summarization and keyphrase extraction

Cataloged from PDF version of article.When we express some idea or story, it is inevitable to use words that are semantically related to each other. When this phenomena is exploited from the aspect of words in the language, it is possible to infer the level of semantic relationship between words by observing their distribution and use in discourse. From the aspect of discourse it is possible to model the structure of the document by observing the changes in the lexical cohesion in order to attack high level natural language processing tasks. In this research lexical cohesion is investigated from both of these aspects by first building methods for measuring semantic relatedness of word pairs and then using these methods in the tasks of topic segmentation, summarization and keyphrase extraction. Measuring semantic relatedness of words requires prior knowledge about the words. Two different knowledge-bases are investigated in this research. The first knowledge base is a manually built network of semantic relationships, while the second relies on the distributional patterns in raw text corpora. In order to discover which method is effective in lexical cohesion analysis, a comprehensive comparison of state-of-the art methods in semantic relatedness is made. For topic segmentation different methods using some form of lexical cohesion are present in the literature. While some of these confine the relationships only to word repetition or strong semantic relationships like synonymy, no other work uses the semantic relatedness measures that can be calculated for any two word pairs in the vocabulary. Our experiments suggest that topic segmentation performance improves methods using both classical relationships and word repetition. Furthermore, the experiments compare the performance of different semantic relatedness methods in a high level task. The detected topic segments are used in summarization, and achieves better results compared to a lexical chains based method that uses WordNet. Finally, the use of lexical cohesion analysis in keyphrase extraction is investigated. Previous research shows that keyphrases are useful tools in document retrieval and navigation. While these point to a relation between keyphrases and document retrieval performance, no other work uses this relationship to identify keyphrases of a given document. We aim to establish a link between the problems of query performance prediction (QPP) and keyphrase extraction. To this end, features used in QPP are evaluated in keyphrase extraction using a Naive Bayes classifier. Our experiments indicate that these features improve the effectiveness of keyphrase extraction in documents of different length. More importantly, commonly used features of frequency and first position in text perform poorly on shorter documents, whereas QPP features are more robust and achieve better results.Ercan, GönençPh.D

Cognitive Information Processing

Contains research objectives, summary of research and reports on four research projects.National Institutes of Health (Grant 5 PO1 GM14940-05)National Institutes of Health (Grant 3 POI1 GM15006-03S2)Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DAAB07-71-C-0300World Health Organization (Grant R/00348)Grant from the Associated Pres

Ontology Enrichment from Free-text Clinical Documents: A Comparison of Alternative Approaches

While the biomedical informatics community widely acknowledges the utility of domain ontologies, there remain many barriers to their effective use. One important requirement of domain ontologies is that they achieve a high degree of coverage of the domain concepts and concept relationships. However, the development of these ontologies is typically a manual, time-consuming, and often error-prone process. Limited resources result in missing concepts and relationships, as well as difficulty in updating the ontology as domain knowledge changes. Methodologies developed in the fields of Natural Language Processing (NLP), Information Extraction (IE), Information Retrieval (IR), and Machine Learning (ML) provide techniques for automating the enrichment of ontology from free-text documents. In this dissertation, I extended these methodologies into biomedical ontology development. First, I reviewed existing methodologies and systems developed in the fields of NLP, IR, and IE, and discussed how existing methods can benefit the development of biomedical ontologies. This previously unconducted review was published in the Journal of Biomedical Informatics. Second, I compared the effectiveness of three methods from two different approaches, the symbolic (the Hearst method) and the statistical (the Church and Lin methods), using clinical free-text documents. Third, I developed a methodological framework for Ontology Learning (OL) evaluation and comparison. This framework permits evaluation of the two types of OL approaches that include three OL methods. The significance of this work is as follows: 1) The results from the comparative study showed the potential of these methods for biomedical ontology enrichment. For the two targeted domains (NCIT and RadLex), the Hearst method revealed an average of 21% and 11% new concept acceptance rates, respectively. The Lin method produced a 74% acceptance rate for NCIT; the Church method, 53%. As a result of this study (published in the Journal of Methods of Information in Medicine), many suggested candidates have been incorporated into the NCIT; 2) The evaluation framework is flexible and general enough that it can analyze the performance of ontology enrichment methods for many domains, thus expediting the process of automation and minimizing the likelihood that key concepts and relationships would be missed as domain knowledge evolves

A SENSORY-MOTOR LINGUISTIC FRAMEWORK FOR HUMAN ACTIVITY UNDERSTANDING

We empirically discovered that the space of human actions has a linguistic structure. This is a sensory-motor space consisting of the evolution of joint angles of the human body in movement. The space of human activity has its own phonemes, morphemes, and sentences. We present a Human Activity Language (HAL) for symbolic non-arbitrary representation of sensory and motor information of human activity. This language was learned from large amounts of motion capture data. Kinetology, the phonology of human movement, finds basic primitives for human motion (segmentation) and associates them with symbols (symbolization). This way, kinetology provides a symbolic representation for human movement that allows synthesis, analysis, and symbolic manipulation. We introduce a kinetological system and propose five basic principles on which such a system should be based: compactness, view-invariance, reproducibility, selectivity, and reconstructivity. We demonstrate the kinetological properties of our sensory-motor primitives. Further evaluation is accomplished with experiments on compression and decompression of motion data. The morphology of a human action relates to the inference of essential parts of movement (morpho-kinetology) and its structure (morpho-syntax). To learn morphemes and their structure, we present a grammatical inference methodology and introduce a parallel learning algorithm to induce a grammar system representing a single action. The algorithm infers components of the grammar system as a subset of essential actuators, a CFG grammar for the language of each component representing the motion pattern performed in a single actuator, and synchronization rules modeling coordination among actuators. The syntax of human activities involves the construction of sentences using action morphemes. A sentence may range from a single action morpheme (nuclear syntax) to a sequence of sets of morphemes. A single morpheme is decomposed into analogs of lexical categories: nouns, adjectives, verbs, and adverbs. The sets of morphemes represent simultaneous actions (parallel syntax) and a sequence of movements is related to the concatenation of activities (sequential syntax). We demonstrate this linguistic framework on real motion capture data from a large scale database containing around 200 different actions corresponding to English verbs associated with voluntary meaningful observable movement