Steps for Creating two Persian Specialized Corpora

Currently, most linguistic studies benefit from valid linguistic data available at corpora. Compiling corpora is a common practice in linguistic research. The present study introduces two specialized corpora in Persian; a specialized corpus is used to study a particular type of language or language variety. For building such corpora, first, a set of texts were compiled based on pre-established criteria used in the sampling process (including the mode of the texts, type of the texts, domain of the texts, language/ language varieties of the texts and the date of the texts). The corpora are specialized because they include technical terms in information processing and management, librarianship, linguistics, computational linguistics, thesaurus building, managing, policy-making, natural language processing, information technology, information retrieval, ontology and other related interdisciplinary domains. After compiling data and Metadata, the texts were preprocessed (normalized and tokenized) and annotated (automated POS tagging); finally, the tags were manually checked. Each corpus includes more than four million words. Since not many specialized corpora are built in Persian, such corpora could be considered valuable resources for researchers interested in studying linguistic variations in Persian interdisciplinary texts.https://dorl.net/dor/20.1001.1.20088302.2022.20.4.14.

COMPUTATIONAL LINGUISTICS (Model Baru Kajian Linguistik dalam Perspektif Komputer)

This paper describes a new discipline in applied linguistics studies, computational linguistics. It’s a new model of applied linguistics which is influenced by computer technology. Computational linguistics is a discipline straddling applied linguistics and computer science that is concerned with the computer processing of natural languages on all levels of linguistic description. Traditionally, computational linguistics was usually performed by computer scientists who had specialized in the application of computers to the processing of a natural language. Computational linguists often work as members of interdisciplinary teams, including linguists (specifically trained in linguistics), language experts (persons with some level of ability in the languages relevant to a given project), and computer scientists. The several areas of computational linguistics study encompasses such practical applications as speech recognition systems, speech synthesis, automated voice response systems, web search engines, text editors, grammar checking, text to speech, corpus linguistics, machine translation, text data mining, and others. This paper presents the definition of computational linguistics, relation between language and computer, and area of computational linguistics studies

Break it Down for Me: A Study in Automated Lyric Annotation

Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understandable manner. However, in ALA the system must often include additional information to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task.Comment: To appear in Proceedings of EMNLP 201

Automated assessment of non-native learner essays: Investigating the role of linguistic features

Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in real life applications today. However, not much is known about which specific linguistic features are useful for prediction and how much of this is consistent across datasets. This article addresses that by exploring the role of various linguistic features in automatic essay scoring using two publicly available datasets of non-native English essays written in test taking scenarios. The linguistic properties are modeled by encoding lexical, syntactic, discourse and error types of learner language in the feature set. Predictive models are then developed using these features on both datasets and the most predictive features are compared. While the results show that the feature set used results in good predictive models with both datasets, the question "what are the most predictive features?" has a different answer for each dataset.Comment: Article accepted for publication at: International Journal of Artificial Intelligence in Education (IJAIED). To appear in early 2017 (journal url: http://www.springer.com/computer/ai/journal/40593

Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation

Automated metrics such as BLEU are widely used in the machine translation literature. They have also been used recently in the dialogue community for evaluating dialogue response generation. However, previous work in dialogue response generation has shown that these metrics do not correlate strongly with human judgment in the non task-oriented dialogue setting. Task-oriented dialogue responses are expressed on narrower domains and exhibit lower diversity. It is thus reasonable to think that these automated metrics would correlate well with human judgment in the task-oriented setting where the generation task consists of translating dialogue acts into a sentence. We conduct an empirical study to confirm whether this is the case. Our findings indicate that these automated metrics have stronger correlation with human judgments in the task-oriented setting compared to what has been observed in the non task-oriented setting. We also observe that these metrics correlate even better for datasets which provide multiple ground truth reference sentences. In addition, we show that some of the currently available corpora for task-oriented language generation can be solved with simple models and advocate for more challenging datasets