External Lexical Information for Multilingual Part-of-Speech Tagging

Morphosyntactic lexicons and word vector representations have both proven useful for improving the accuracy of statistical part-of-speech taggers. Here we compare the performances of four systems on datasets covering 16 languages, two of these systems being feature-based (MEMMs and CRFs) and two of them being neural-based (bi-LSTMs). We show that, on average, all four approaches perform similarly and reach state-of-the-art results. Yet better performances are obtained with our feature-based models on lexically richer datasets (e.g. for morphologically rich languages), whereas neural-based results are higher on datasets with less lexical variability (e.g. for English). These conclusions hold in particular for the MEMM models relying on our system MElt, which benefited from newly designed features. This shows that, under certain conditions, feature-based approaches enriched with morphosyntactic lexicons are competitive with respect to neural methods

Multilingual Text to Speech in embedded systems using RC8660

Most multilingual Test to Speech (TTS) systems are software applications which allow people with visual impairments or reading disabilities to listen the written material using computer. This paper describes an approach to make a multilingual TTS and embed it into the portable, low cost, and standalone embedded system to access and read electronic documents particularly in developing countries. There are several TTS such as Doubletalk, DECtalk, and Dolphin available in market, also there are some products using TTS such as Talking OCR, Bill Reader and Intel Reader, which are not affordable or multilingual. To design this system OMAP3530 an application processor board is considered as the hardware platform to process the language-independent parts of the application and RC8660 used as an integrated TTS processor

A Hybrid Algorithm for Recognizing the Position of Ezafe Constructions in Persian Texts

In the Persian language, an Ezafe construction is a linking element which joins the head of a phrase to its modifiers. The Ezafe in its simplest form is pronounced as –e, but generally not indicated in writing. Determining the position of an Ezafe is advantageous for disambiguating the boundary of the syntactic phrases which is a fundamental task in most natural language processing applications. This paper introduces a framework for combining genetic algorithms with rule-based models that brings the advantages of both approaches and overcomes their problems. This framework was used for recognizing the position of Ezafe constructions in Persian written texts. At the first stage, the rule-based model was applied to tag some tokens of an input sentence. Then, in the second stage, the search capabilities of the genetic algorithm were used to assign the Ezafe tag to untagged tokens using the previously captured training information. The proposed framework was evaluated on Peykareh corpus and it achieved 95.26 percent accuracy. Test results show that this proposed approach outperformed other approaches for recognizing the position of Ezafe constructions

A morphological lexicon for the persian language

We introduce PerLex, a large-coverage and freely-available morphological lexicon for the Persian language. We describe the main features of the Persian morphology, and the way we have represented it within the Alexina formalism, on which PerLex is based. We focus on the methodology we used for constructing lexical entries from various sources, as well as the problems related to typographic normalisation. The resulting lexicon shows a satisfying coverage on a reference corpus and should therefore be a good starting point for developing a syntactic lexicon for the Persian language. 1