ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference

MT on and for the Web

Abstract A Systran MT server became available on the minitel network in 1984, and on Internet in 1994. Since then we have come to a better understanding of the nature of MT systems by separately analyzing their linguistic, computational, and operational architectures. Also, thanks to the CxAxQ metatheorem, the systems' inherent limits have been clarified, and design choices can now be made in an informed manner according to the translation situations. MT evaluation has also matured: tools based on reference translations are useful for measuring progress; those based on subjective judgments for estimating future usage quality; and task-related objective measures (such as post-editing distances) for measuring operational quality. Moreover, the same technological advances that have led to "Web 2.0" have brought several futuristic predictions to fruition. Free Web MT services have democratized assimilation MT beyond belief. Speech translation research has given rise to usable systems for restricted tasks running on PDAs or on mobile phones connected to servers. New man-machine interface techniques have made interactive disambiguation usable in large-coverage multimodal MT. Increases in computing power have made statistical methods workable, and have led to the possibility of building low-linguisticquality but still useful MT systems by machine learning from aligned bilingual corpora (SMT, EBMT). In parallel, progress has been made in developing interlingua-based MT systems, using hybrid methods. Unfortunately, many misconceptions about MT have spread among the public, and even among MT researchers, because of ignorance of the past and present of MT R&D. A compensating factor is the willingness of end users to freely contribute to building essential parts of the linguistic knowledge needed to construct MT systems, whether corpus-related or lexical. Finally, some developments we anticipated fifteen years ago have not yet materialized, such as online writing tools equipped with interactive disambiguation, and as a corollary the possibility of transforming source documents into self-explaining documents (SEDs) and of producing corresponding SEDs fully automatically in several target languages. These visions should now be realized, thanks to the evolution of Web programming and multilingual NLP techniques, leading towards a true Semantic Web, "Web 3.0", which will support ubilingual (ubiquitous multilingual) computing

Multilinguisation d'ontologies dans le cadre de la recherche d'information translingue dans des collections d'images accompagnées de textes spontanés

Le Web est une source proliférante d'objets multimédia, décrits dans différentes langues natu- relles. Afin d'utiliser les techniques du Web sémantique pour la recherche de tels objets (images, vidéos, etc.), nous proposons une méthode d'extraction de contenu dans des collections de textes multilingues, paramétrée par une ou plusieurs ontologies. Le processus d'extraction est utilisé pour indexer les objets multimédia à partir de leur contenu textuel, ainsi que pour construire des requêtes formelles à partir d'énoncés spontanés. Il est basé sur une annotation interlingue des textes, conservant les ambiguïtés de segmentation et la polysémie dans des graphes. Cette première étape permet l'utilisation de processus de désambiguïsation factorisés au niveau d'un lexique pivot (de lexèmes interlingues). Le passage d'une ontologie en paramètre du système se fait en l'alignant de façon automatique avec le lexique interlingue. Il est ainsi possible d'utiliser des ontologies qui n'ont pas été conçues pour une utilisation multilingue, et aussi d'ajouter ou d'étendre l'ensemble des langues et leurs couvertures lexicales sans modifier les ontologies. Un démonstrateur pour la recherche multilingue d'images, développé pour le projet ANR OMNIA, a permis de concrétiser les approches proposées. Le passage à l'échelle et la qualité des annotations produites ont ainsi pu être évalués.The World Wide Web is a proliferating source of multimedia objects described using various natural languages. In order to use semantic Web techniques for retrieval of such objects (images, videos, etc.), we propose a content extraction method in multilingual text collections, using one or several ontologies as parameters. The content extraction process is used on the one hand to index multimedia objects using their textual content, and on the other to build formal requests from spontaneous user requests. The process is based on an interlingual annotation of texts, keeping ambiguities (polysemy and segmentation) in graphs. This first step allows using common desambiguation processes at th elevel of a pivot langage (interlingual lexemes). Passing an ontology as a parameter of the system is done by aligning automatically its elements with the interlingual lexemes of the pivot language. It is thus possible to use ontologies that have not been built for a specific use in a multilingual context, and to extend the set of languages and their lexical coverages without modifying the ontologies. A demonstration software for multilingual image retrieval has been built with the proposed approach in the framework of the OMNIA ANR project, allowing to implement the proposed approaches. It has thus been possible to evaluate the scalability and quality of annotations produiced during the retrieval process.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Mining photographic collections to enhance the precision and recall of search results using semantically controlled query expansion

Driven by a larger and more diverse user-base and datasets, modern Information Retrieval techniques are striving to become contextually-aware in order to provide users with a more satisfactory search experience. While text-only retrieval methods are significantly more accurate and faster to render results than purely visual retrieval methods, these latter provide a rich complementary medium which can be used to obtain relevant and different results from those obtained using text-only retrieval. Moreover, the visual retrieval methods can be used to learn the user’s context and preferences, in particular the user’s relevance feedback, and exploit them to narrow down the search to more accurate results. Despite the overall deficiency in precision of visual retrieval result, the top results are accurate enough to be used for query expansion, when expanded in a controlled manner. The method we propose overcomes the usual pitfalls of visual retrieval: 1. The hardware barrier giving rise to prohibitively slow systems. 2. Results dominated by noise. 3. A significant gap between the low-level features and the semantics of the query. In our thesis, the first barrier is overcome by employing a simple block-based visual features which outperforms a method based on MPEG-7 features specially at early precision (precision of the top results). For the second obstacle, lists from words semantically weighted according to their degree of relation to the original query or to relevance feedback from example images are formed. These lists provide filters through which the confidence in the candidate results is assessed for inclusion in the results. This allows for more reliable Pseudo-Relevance Feedback (PRF). This technique is then used to bridge the third barrier; the semantic gap. It consists of a second step query, re-querying the data set with an query expanded with weighted words obtained from the initial query, and semantically filtered (SF) without human intervention. We developed our PRF-SF method on the IAPR TC-12 benchmark dataset of 20,000 tourist images, obtaining promising results, and tested it on the different and much larger Belga benchmark dataset of approximately 500,000 news images originating from a different source. Our experiments confirmed the potential of the method in improving the overall Mean Average Precision, recall, as well as the level of diversity of the results measured using cluster recall

Sentiment Analysis of Textual Content in Social Networks. From Hand-Crafted to Deep Learning-Based Models

Aquesta tesi proposa diversos mètodes avançats per analitzar automàticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells d’anàlisi i en diferents idiomes. Comencem proposant un sistema d’anàlisi de sentiments, anomenat SentiRich, basat en un conjunt ric d’atributs, inclosa la informació extreta de lèxics de sentiments i models de word embedding pre-entrenats. A continuació, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sèrie de tasques d’anàlisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques típiques d’anàlisi de sentiments fins a determinar automàticament la intensitat d’una emoció (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. També proposem un nou sistema basat en Deep Learning per solucionar el problema de classificació de les emocions múltiples a Twitter. A més, es va considerar el problema de l’anàlisi del sentiment depenent de l’objectiu. Per a aquest propòsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com l’anglès, disposen d’una àmplia gamma de recursos per permetre l’anàlisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tècnica d'anàlisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingüe i basat en Deep Learning per a llenguatges amb pocs recursos lingüístics. Proposem combinar l’ajuda a la presa de decisions multi-criteri i anàlisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat d’explotar tant les opinions com les seves preferències en el procés de classificació d’alternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicació de les marques de destinació a través de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turístiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels enfocaments d’última generació i mostren troballes apassionants.Esta tesis propone varios métodos avanzados para analizar automáticamente el contenido textual compartido en las redes sociales e identificar opiniones, emociones y sentimientos, en diferentes niveles de análisis y en diferentes idiomas. Comenzamos proponiendo un sistema de análisis de sentimientos, llamado SentiRich, que está basado en un conjunto rico de características, que incluyen la información extraída de léxicos de sentimientos y modelos de word embedding previamente entrenados. Luego, proponemos un sistema basado en redes neuronales convolucionales y regresores XGboost para resolver una variedad de tareas de análisis de sentimientos y emociones en Twitter. Estas tareas van desde las típicas tareas de análisis de sentimientos hasta la determinación automática de la intensidad de una emoción (como alegría, miedo, ira, etc.) y la intensidad del sentimiento de los autores de los tweets. También proponemos un novedoso sistema basado en Deep Learning para abordar el problema de clasificación de emociones múltiples en Twitter. Además, consideramos el problema del análisis de sentimientos dependiente del objetivo. Para este propósito, proponemos un sistema basado en Deep Learning que identifica y extrae el objetivo de los tweets. Si bien algunos idiomas, como el inglés, tienen una amplia gama de recursos para permitir el análisis de sentimientos, la mayoría de los idiomas carecen de ellos. Por lo tanto, utilizamos la técnica de Análisis de Sentimiento Inter-lingual para desarrollar un sistema novedoso, multilingüe y basado en Deep Learning para los lenguajes con pocos recursos lingüísticos. Proponemos combinar la Ayuda a la Toma de Decisiones Multi-criterio y el análisis de sentimientos para desarrollar un sistema que brinde a los usuarios la capacidad de explotar las opiniones junto con sus preferencias en el proceso de clasificación de alternativas. Finalmente, aplicamos los sistemas desarrollados al campo de la comunicación de las marcas de destino a través de las redes sociales. Con este fin, recopilamos tweets de personas locales, visitantes, y gabinetes oficiales de Turismo de diferentes destinos turísticos y analizamos las opiniones y las emociones compartidas en ellos. En general, los métodos propuestos en esta tesis mejoran el rendimiento de los enfoques de vanguardia y muestran hallazgos interesa.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at a different level of analysis and in different languages. We start by proposing a sentiment analysis system, called SentiRich, based on a set of rich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter. Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target of the tweets. While some languages, such as English, have a vast array of resources to enable sentiment analysis, most low-resource languages lack them. So, we utilise the Cross-lingual Sentiment Analysis technique to develop a novel, multi-lingual and Deep Learning-based system for low resource languages. We propose to combine Multi-Criteria Decision Aid and sentiment analysis to develop a system that gives users the ability to exploit reviews alongside their preferences in the process of alternatives ranking. Finally, we applied the developed systems to the field of communication of destination brands through social networks. To this end, we collected tweets of local people, visitors, and official brand destination offices from different tourist destinations and analysed the opinions and the emotions shared in these tweets

Temporal processing of news : annotation of temporal expressions, verbal events and temporal relations

The ability to capture the temporal dimension of a natural language text is essential to many natural language processing applications, such as Question Answering, Automatic Summarisation, and Information Retrieval. Temporal processing is a ¯eld of Computational Linguistics which aims to access this dimension and derive a precise temporal representation of a natural language text by extracting time expressions, events and temporal relations, and then representing them according to a chosen knowledge framework. This thesis focuses on the investigation and understanding of the di®erent ways time is expressed in natural language, on the implementation of a temporal processing system in accordance with the results of this investigation, on the evaluation of the system, and on the extensive analysis of the errors and challenges that appear during system development. The ultimate goal of this research is to develop the ability to automatically annotate temporal expressions, verbal events and temporal relations in a natural language text. Temporal expression annotation involves two stages: temporal expression identi¯cation concerned with determining the textual extent of a temporal expression, and temporal expression normalisation which ¯nds the value that the temporal expression designates and represents it using an annotation standard. The research presented in this thesis approaches these tasks with a knowledge-based methodology that tackles temporal expressions according to their semantic classi¯cation. Several knowledge sources and normalisation models are experimented with to allow an analysis of their impact on system performance. The annotation of events expressed using either ¯nite or non-¯nite verbs is addressed with a method that overcomes the drawback of existing methods v which associate an event with the class that is most frequently assigned to it in a corpus and are limited in coverage by the small number of events present in the corpus. This limitation is overcome in this research by annotating each WordNet verb with an event class that best characterises that verb. This thesis also describes an original methodology for the identi¯cation of temporal relations that hold among events and temporal expressions. The method relies on sentence-level syntactic trees and a propagation of temporal relations between syntactic constituents, by analysing syntactic and lexical properties of the constituents and of the relations between them. The detailed evaluation and error analysis of the methods proposed for solving di®erent temporal processing tasks form an important part of this research. Various corpora widely used by researchers studying di®erent temporal phenomena are employed in the evaluation, thus enabling comparison with state of the art in the ¯eld. The detailed error analysis targeting each temporal processing task helps identify not only problems of the implemented methods, but also reliability problems of the annotated resources, and encourages potential reexaminations of some temporal processing tasks.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multilingual Information Access: Practices and Perceptions of Bi/multilingual Academic Users

The research reported in this dissertation explored linguistic determinants in online information searching, and examined to what extent bi/multilingual academic users utilize Multilingual Information Access (MLIA) tools and what impact these have on their information searching behavior. The aim of the study was three-pronged: to provide tangible data that can support recommendations for the effective user-centered design of Multilingual Information Retrieval (MLIR) systems; to provide a user-centered evaluation of existing MLIA tools, and to offer the basis of a framework for Library & Information Science (LIS) professionals in teaching information literacy and library skills for bi/multilingual academic users. In the first phase of the study, 250 bi/multilingual students participated in a web survey that investigated their language choices while searching for information on the internet and electronic databases. 31 of these participants took part in the second phase which involved a controlled lab-based user experiment and post experiment questionnaire that investigated their use of MLIA tools on Google and WorldCat and their opinions of these tools. In the third phase, 19 students participated in focus groups discussions and 6 librarians were interviewed to find out their perspectives on multilingual information literacy. Results showed that though machine translation has alleviated some of the linguistic related challenges in online information searching, language barriers do still exist for some users especially at the query formulation stage. Captures from the experiment revealed great diversity in the way MLIA tools were utilized while the focus group discussions and interviews revealed a general lack of awareness by both librarians and students of the tools that could help enhance and promote multilingual information literacy. The study highlights the roles of both IR system designers as well as LIS professionals in enhancing and promoting multilingual information access and literacy: User- centered design, user-modeling were found to be key aspects in the development of more effective multilingual information retrieval (MLIR) systems. The study also highlights the distinction between being multilingually information literate and being multilingual information literate. Suitable models for instruction for bi/multilingual academic users point towards Specialized Information Literacy Instruction (SILI) and Personalized Information Literacy Instruction (PILI)