Towards a flexible open-source software library for multi-layered scholarly textual studies: An Arabic case study dealing with semi-automatic language processing

This paper presents both the general model and a case study of the Computational and Collaborative Philology Library (CoPhiLib), an ongoing initiative underway at the Institute for Computational Linguistics (ILC) of the National Research Council (CNR), Pisa, Italy. The library, designed and organized as a reusable, abstract and open-source software component, aims at solving the needs of multi-lingual and cross-lingual analysis by exposing common Application Programming Interfaces (APIs). The core modules, coded by the Java programming language, constitute the groundwork of a Web platform designed to deal with textual scholarly needs. The Web application, implemented according to the Java Enterprise specifications, focuses on multi-layered analysis for the study of literary documents and related multimedia sources. This ambitious challenge seeks to obtain the management of textual resources, on the one hand by abstracting from current language, on the other hand by decoupling from the specific requirements of single projects. This goal is achieved thanks to methodologies declared by the 'agile process', and by putting into effect suitable use case modeling, design patterns, and component-based architectures. The reusability and flexibility of the system have been tested on an Arabic case study: the system allows users to choose the morphological engine (such as AraMorph or Al-Khalil), along with linguistic granularity (i.e. with or without declension). Finally, the application enables the construction of annotated resources for further statistical engines (training set). © 2014 IEEE

Utilisation des réseaux de neurones récurrents pour la projection interlingue d'étiquettes morpho-syntaxiques à partir d'un corpus parallèle

International audienceIn this paper, we propose a method to automatically induce linguistic analysis tools for languages that have no labeled training data. This method is based on cross-language projection of linguistic annotations from parallel corpora. Our method does not assume any knowledge about foreign languages, making it applicable to a wide range of resource-poor languages. No word alignment information is needed in our approach. We use Recurrent Neural Networks (RNNs) as cross-lingual analysis tool. To illustrate the potential of our approach, we firstly investigate Part-Of-Speech (POS) tagging. Combined with a simple projection method (using word alignment information), it achieves performance comparable to the one of recently published approaches for cross-lingual projection. Mots-clés : Multilinguisme, transfert crosslingue, étiquetage morpho-syntaxique, réseaux de neurones récurrents

Représentation à base de connaissance pour une méthode de classification transductive de document multilangue

International audienceMultilingual document classification is often addressed by approaches that rely on language-specific resources (e.g., bilingual dictionaries and machine translation tools) to evaluate cross-lingual document similarities. However, the required transformations may alter the original document semantics, raising additional issues to the known difficulty of obtaining high-quality labeled datasets. To overcome such issues we propose a new framework for multilingual document classification under a transductive learning setting. We exploit a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. We resort to a state-of-the-art transductive learner to produce the document classification. Results on two real-world multilingual corpora have highlighted the effectiveness of the proposed document model w.r.t. document representations usually involved in multilingual and cross-lingual analysis, and the robustness of the transductive setting for multilingual document classification

Classifying Crises-Information Relevancy with Semantics

Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis

Cross-Lingual Classification of Crisis Data

Many citizens nowadays flock to social media during crises to share or acquire the latest information about the event. Due to the sheer volume of data typically circulated during such events, it is necessary to be able to efficiently filter out irrelevant posts, thus focusing attention on the posts that are truly relevant to the crisis. Current methods for classifying the relevance of posts to a crisis or set of crises typically struggle to deal with posts in different languages, and it is not viable during rapidly evolving crisis situations to train new models for each language. In this paper we test statistical and semantic classification approaches on cross-lingual datasets from 30 crisis events, consisting of posts written mainly in English, Spanish, and Italian. We experiment with scenarios where the model is trained on one language and tested on another, and where the data is translated to a single language. We show that the addition of semantic features extracted from external knowledge bases improve accuracy over a purely statistical model

An Empirical Study on L2 Accents of Cross-lingual Text-to-Speech Systems via Vowel Space

With the recent developments in cross-lingual Text-to-Speech (TTS) systems, L2 (second-language, or foreign) accent problems arise. Moreover, running a subjective evaluation for such cross-lingual TTS systems is troublesome. The vowel space analysis, which is often utilized to explore various aspects of language including L2 accents, is a great alternative analysis tool. In this study, we apply the vowel space analysis method to explore L2 accents of cross-lingual TTS systems. Through the vowel space analysis, we observe the three followings: a) a parallel architecture (Glow-TTS) is less L2-accented than an auto-regressive one (Tacotron); b) L2 accents are more dominant in non-shared vowels in a language pair; and c) L2 accents of cross-lingual TTS systems share some phenomena with those of human L2 learners. Our findings imply that it is necessary for TTS systems to handle each language pair differently, depending on their linguistic characteristics such as non-shared vowels. They also hint that we can further incorporate linguistics knowledge in developing cross-lingual TTS systems.Comment: Submitted to ICASSP 202

Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology

Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by Borth et al. (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia platform for the creation of a large-scale multilingual visual sentiment concept ontology (MVSO). Unlike the flat structure in Borth et al. (2013), our unified ontology is organized hierarchically by multilingual clusters of visually detectable nouns and subclusters of emotionally biased versions of these nouns. In addition, we present an image-based prediction task to show how generalizable language-specific models are in a multilingual context. A new, publicly available dataset of >15.6K sentiment-biased visual concepts across 12 languages with language-specific detector banks, >7.36M images and their metadata is also released.Comment: 11 pages, to appear at ACM MM'1