Khmer Treebank Construction via Interactive Tree Visualization

Despite the fact that there are a number of researches working on Khmer Language in the field of Natural Language Processing along with some resources regarding words segmentation and POS Tagging, we still lack of high-level resources regarding syntax, Treebanks and grammars, for example. This paper illustrates the semi-automatic framework of constructing Khmer Treebank and the extraction of the Khmer grammar rules from a set of sentences taken from the Khmer grammar books. Initially, these sentences will be manually annotated and processed to generate a number of grammar rules with their probabilities once the Treebank is obtained. In our experiments, the annotated trees and the extracted grammar rules are analyzed in both quantitative and qualitative way. Finally, the results will be evaluated in three evaluation processes including Self-Consistency, 5-Fold Cross-Validation, Leave-One-Out Cross-Validation along with the three validation methods such as Precision, Recall, F1-Measure. According to the result of the three validations, Self-Consistency has shown the best result with more than 92%, followed by the Leave-One-Out Cross-Validation and 5-Fold Cross Validation with the average of 88% and 75% respectively. On the other hand, the crossing bracket data shows that Leave-One-Out Cross Validation holds the highest average with 96% while the other two are 85% and 89%, respectively

De l’arabe standard vers l’arabe dialectal :projection de corpus et ressourceslinguistiques en vue du traitementautomatique de l’oral dans les médiastunisiens

International audienceRÉSUMÉ. Dans ce travail, nous nous intéressons aux problèmes liés au traitement automatique de l'oral parlé dans les médias tunisiens. Cet oral se caractérise par l'emploi de l'alternance codique entre l'arabe standard moderne (MSA) et le dialecte tunisien (DT). L'objectif consiste à construire des ressources utiles pour apprendre des modèles de langage dédiés à des applications de reconnaissance automatique de la parole. Comme il s'agit d'une variante du MSA, nous décrivons dans cet article une démarche d'adaptation des ressources MSA vers le DT. Une première évaluation en termes de couverture lexicale et de perplexité est présentée. ABSTRACT. In this work, we focus on the problems of the automatic treatment of oral spoken in the Tunisian media. This oral is marked by the use of code-switching between the Modern Standard Arabic (MSA) and the Tunisian dialect (TD). Our goal is to build useful resources to learn language models that can be used in automatic speech recognition applications. As it is a variant of MSA, we describe in this paper an adjustment process of the MSA resources to the TD. A first evaluation in terms of lexical coverage and perplexity is presented

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)

Semi-supervised Thai Sentence Segmentation Using Local and Distant Word Representations

A sentence is typically treated as the minimal syntactic unit used to extract valuable information from long text. However, in written Thai, there are no explicit sentence markers. Some prior works use machine learning; however, a deep learning approach has never been employed. We propose a deep learning model for sentence segmentation that includes three main contributions. First, we integrate n-gram embedding as a local representation to capture word groups near sentence boundaries. Second, to focus on the keywords of dependent clauses, we combine the model with a distant representation obtained from self-attention modules. Finally, due to the scarcity of labeled data, for which annotation is difficult and time-consuming, we also investigate two techniques that allow us to utilize unlabeled data: Cross-View Training (CVT) as a semi-supervised learning technique, and a pre-trained language model (ELMo) to improve word representation. In the experiments, our model reduced the relative error by 7.4% and 18.5% compared with the baseline models on the Orchid and UGWC datasets, respectively. Ablation studies revealed that the main contributing factor was adopting n-gram features, which were further analyzed using the interpretation technique and indicated that the model utilizes the features in the same way that humans do

Lexical descriptions for Vietnamese language processing

Colloque avec actes et comité de lecture. internationale.International audienceOnly very recently have Vietnamese re-searchers begun to be involved in the do-main of Natural Language Processing. As there does not exist any published work in formal linguistics or any recognizable standard for Vietnamese word categories, the fundamental works in Vietnamese text analysis such as part-of-speech tagging, parsing, etc. are very difficult tasks for computer scientists. All necessary linguistic resources have to be built from scratch, and until now almost no re-sources are shared in public research. The aim of our project is to build a common linguistic database that is freely and easily exploitable for the automatic processing of Vietnamese. In this paper, we propose an extensible set of Vietnamese syntactic descriptions that can be used for tagset definition and corpus annotation. These descriptors are established in such a way to be a reference set proposal for Vietnamese in the context of ISO subcommit-tee TC37/SC4 (Language Resource Management)

Question answering using document tagging and question classification

viii, 139 leaves ; 29 cm.Question answering (QA) is a relatively new area of research. QA is retriecing answers to questions rather than information retrival systems (search engines), which retrieve documents. This means that question answering systems will possibly be the next generation of search engines. What is left to be done to allow QA to be the next generation of search engines? The answer is higher accuracy, which can be achieved by investigating methods of questions answering. I took the approach of designing a question answering system that is based on document tagging and question classification. Question classification extracts useful information from the question about how to answer the question. Document tagging extracts useful information from the documents, which will be used in finding the answer to the question. We used different available systems to tage the documents. Our system classifies the questions using manually developed rules. I also investigated different ways which can use both these methods to answer questions and found that our methods had a comparable accuracy to some systems that use deeper processing techniques. This thesis includes investigations into modules of a question answering system and gives insights into how to go about developing a question answering system based on document tagging and question classification. I also evaluated our current system with the questions from the TREC 2004 question answering track

Learning narrative structure from annotated folktales

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 97-100).Narrative structure is an ubiquitous and intriguing phenomenon. By virtue of structure we recognize the presence of Villainy or Revenge in a story, even if that word is not actually present in the text. Narrative structure is an anvil for forging new artificial intelligence and machine learning techniques, and is a window into abstraction and conceptual learning as well as into culture and its in influence on cognition. I advance our understanding of narrative structure by describing Analogical Story Merging (ASM), a new machine learning algorithm that can extract culturally-relevant plot patterns from sets of folktales. I demonstrate that ASM can learn a substantive portion of Vladimir Propp's in influential theory of the structure of folktale plots. The challenge was to take descriptions at one semantic level, namely, an event timeline as described in folktales, and abstract to the next higher level: structures such as Villainy, Stuggle- Victory, and Reward. ASM is based on Bayesian Model Merging, a technique for learning regular grammars. I demonstrate that, despite ASM's large search space, a carefully-tuned prior allows the algorithm to converge, and furthermore it reproduces Propp's categories with a chance-adjusted Rand index of 0.511 to 0.714. Three important categories are identied with F-measures above 0.8. The data are 15 Russian folktales, comprising 18,862 words, a subset of Propp's original tales. This subset was annotated for 18 aspects of meaning by 12 annotators using the Story Workbench, a general text-annotation tool I developed for this work. Each aspect was doubly-annotated and adjudicated at inter-annotator F-measures that cluster around 0.7 to 0.8. It is the largest, most deeply-annotated narrative corpus assembled to date. The work has significance far beyond folktales. First, it points the way toward important applications in many domains, including information retrieval, persuasion and negotiation, natural language understanding and generation, and computational creativity. Second, abstraction from natural language semantics is a skill that underlies many cognitive tasks, and so this work provides insight into those processes. Finally, the work opens the door to a computational understanding of cultural in influences on cognition and understanding cultural differences as captured in stories.by Mark Alan Finlayson.Ph.D

Unsupervised learning for text-to-speech synthesis

This thesis introduces a general method for incorporating the distributional analysis of textual and linguistic objects into text-to-speech (TTS) conversion systems. Conventional TTS conversion uses intermediate layers of representation to bridge the gap between text and speech. Collecting the annotated data needed to produce these intermediate layers is a far from trivial task, possibly prohibitively so for languages in which no such resources are in existence. Distributional analysis, in contrast, proceeds in an unsupervised manner, and so enables the creation of systems using textual data that are not annotated. The method therefore aids the building of systems for languages in which conventional linguistic resources are scarce, but is not restricted to these languages. The distributional analysis proposed here places the textual objects analysed in a continuous-valued space, rather than specifying a hard categorisation of those objects. This space is then partitioned during the training of acoustic models for synthesis, so that the models generalise over objects' surface forms in a way that is acoustically relevant. The method is applied to three levels of textual analysis: to the characterisation of sub-syllabic units, word units and utterances. Entire systems for three languages (English, Finnish and Romanian) are built with no reliance on manually labelled data or language-specific expertise. Results of a subjective evaluation are presented