Learning Morphophonemic Processes without Underlying Representations and Explicit Rules

Traditional phonology presupposes abstract underlying representations (UR) and a set of rules to explain the phonological phenomena. There are, however, a number of questions that have been raised regarding this approach : Where do URs come from? How are rules found and related to each other? In the current study, a connectionist network was trained without the benefit of any UR and explicit rules. We hypothesized that rules would emerge as the generalizations the network abstracts in the process of learning to associate forms -(sequences of phonological segments comprising words) with meanings (of the words) and URs as a pattern on the hidden layer. Employing a simple recurrent network we ran a series of simulations on different types of morphophonemic processes. The results of the simulations show that this network is capable of learning morphophonemic processes without any URs and explicit rule

Transfer in a Connectionist Model of the Acquisition of Morphology

The morphological systems of natural languages are replete with examples of the same devices used for multiple purposes: (1) the same type of morphological process (for example, suffixation for both noun case and verb tense) and (2) identical morphemes (for example, the same suffix for English noun plural and possessive). These sorts of similarity would be expected to convey advantages on language learners in the form of transfer from one morphological category to another. Connectionist models of morphology acquisition have been faulted for their supposed inability to represent phonological similarity across morphological categories and hence to facilitate transfer. This paper describes a connectionist model of the acquisition of morphology which is shown to exhibit transfer of this type. The model treats the morphology acquisition problem as one of learning to map forms onto meanings and vice versa. As the network learns these mappings, it makes phonological generalizations which are embedded in connection weights. Since these weights are shared by different morphological categories, transfer is enabled. In a set of experiments with artificial stimuli, networks were trained first on one morphological task (e.g., tense) and then on a second (e.g., number). It is shown that in the context of suffixation, prefixation, and template rules, the second task is facilitated when the second category either makes use of the same forms or the same general process type (e.g., prefixation) as the first.Comment: 21 pages, uuencoded compressed Postscrip

A Semi-automatic and Low Cost Approach to Build Scalable Lemma-based Lexical Resources for Arabic Verbs

International audienceThis work presents a method that enables Arabic NLP community to build scalable lexical resources. The proposed method is low cost and efficient in time in addition to its scalability and extendibility. The latter is reflected in the ability for the method to be incremental in both aspects, processing resources and generating lexicons. Using a corpus; firstly, tokens are drawn from the corpus and lemmatized. Secondly, finite state transducers (FSTs) are generated semi-automatically. Finally, FSTsare used to produce all possible inflected verb forms with their full morphological features. Among the algorithm’s strength is its ability to generate transducers having 184 transitions, which is very cumbersome, if manually designed. The second strength is a new inflection scheme of Arabic verbs; this increases the efficiency of FST generation algorithm. The experimentation uses a representative corpus of Modern Standard Arabic. The number of semi-automatically generated transducers is 171. The resulting open lexical resources coverage is high. Our resources cover more than 70% Arabic verbs. The built resources contain 16,855 verb lemmas and 11,080,355 fully, partially and not vocalized verbal inflected forms. All these resources are being made public and currently used as an open package in the Unitex framework available under the LGPL license

Investigating Semantic Alignment in Character Learning of Chinese as a Foreign Language: The Use and Effect of the Imagery Based Encoding Strategy

For learners of Chinese as a foreign language (CFL), character learning is frustrating. This research postulated that this difficulty may mainly come from a lack of semantic understanding of character-denoted meanings. Language theories support that when a learner’s semantic meaning increases, the orthographic structures that represent the underlying meanings also improve. This study aimed to reveal CFL learners’ cognitive abilities and processes in visual-semantic learning of Chinese characters. Particularly, this study investigated the process by which English-speaking adolescent CFL learners, at the beginning to intermediate level, made mental images of character-denoted meanings to visually encode and retrieve character forms. Quantitative and qualitative data were gathered from image making questionnaires, writing, and reading tests, after learning characters in three commonly-used teaching methods (i.e., English, pictorial, and verbal). The data were analyzed based on a triangulation of the literature from Neuro-Semantic Language Learning Theory, scientific findings in cognitive psychology, and neuroscience. The study found that participants’ semantic abilities to understand character-denoted meanings emerged, but were still restricted in familiar orthographic forms. The use of the imagery strategy as a semantic ability predicted better performances, most evidently in writing; however, the ability in using the imagery strategy to learn characters was still underdeveloped, and needed to be supported with sufficient contextual information. Implications and further research in visual-semantic learning and teaching characters were suggested

Undergraduates’ interest towards learning genetics concepts through integrated stemproblem based learning approach

Scientific and innovative society can be produced by giving priorities in Science, Technology, Engineering, and Mathematics (STEM) as emphasized by Malaysian Higher Education Blueprint (2015-2025). STEM need to be implemented at higher education because universities need to produce competent graduates to support economy growth and sustainable development. Learning STEM through Problem Based Learning might allow the undergraduates to become more enthusiastic when problem-based instruction is incorporated with STEM by implementing teamwork and problem-solving techniques to engage the first-year undergraduates fully with the learning. This study was conducted to investigate whether Integrated STEM Problem Based Learning module could enhance and retain the interest towards genetics concepts among first-year undergraduates. Topics in genetics was considered difficult not only to teach but also to learn. In this research, to overcome the genetic concepts learning difficulties, genetic related topics were chosen to introduce STEM through problem-based learning approach, which might help first-year undergraduates to acquire deep genetic content knowledge. This is very vital for the first-year undergraduates, as the knowledge gained in their first semester will be applied in the upcoming courses in their entire undergraduates’ programs of study. A Pre-Experimental research design with one group-posttest design was applied. A total of 50 participants who are first-year undergraduates from Faculty of Biology from one of the public universities in Malaysia were involved. The Genetics Interest Questionnaire used to study if the STEM Problem Based Learning module could enhance and retain the interest towards genetics concepts. The research has proven that Integrated STEM through problem-based learning approach could enhance and retains the interest in learning genetics concepts among first-year undergraduates

Examining the life cycle of phonological processes: considerations for historical research

The  life  cycle  of  phonological  processes  (e.g.  Bermúdez‐Otero  2015)  provides  an  account  of  how  a  sound  change  might  develop  over  the  history  of  a language,  from its  beginnings in  the  pressures  of  speaking  and hearing, through its progress to a cognitively‐controlled process and  maturation into a categorical phenomenon, to its final resting‐place as a  lexical  or  morphological  pattern.  It  has  been  the  subject  of  increased  research  in  recent  times,  but has  faced  strikingly  few  challenges  to  its  diachronic aspects, notably its predictions of unidirectionality and cycle‐ based dialectal splits. Furthermore,  the cognitive mechanisms rooted in  morpheme‐based  learning  which  are  required  to  predict  domain narrowing  (phrase  >  word  >  stem)  rather  than  broadening  need  to be  tested through child (and adult) acquisition studies. This paper examines  how a historical phonologist might go about interrogating  the life‐cycle  model  using extensive  historical  data  spanning  several  centuries,  and  methodically  ascertaining what  the  model  predicts  in  order  to  know  what to look for. The paper concludes by briefly addressing some of the  many other  questions  raised  by  the  model  which  have  faced  comparatively  little investigation  given  the  purported  pervasiveness  of  the life cycle