We present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and query translation (QT).Our experiments are conducted using the cross-lingual test collection produced within the CLEF eHealth information retrieval tasks in 2013-2015 containing English documents and queries in several European languages.We exploit the Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) paradigms and train several domain-specific and task-specific machine translation systems to translate the non-English queries into English (for the QT approach) and the English documents to all the query languages (for the DT approach).The results show that the quality of QT by SMT is sufficient enough to outperform the retrieval results of the DT approach for all the languages.NMT then further boosts translation quality and retrieval quality for both QT and DT for most languages, but still, QT provides generally better retrieval results than DT.

Optical Music Recognition (OMR) promises to make accessible the content of large amounts of musical documents, an important component of cultural heritage. However, the field does not have an adequate dataset and ground truth for benchmarking OMR systems, which has been a major obstacle to measurable progress. Furthermore, machine learning methods for OMR require training data. We design and collect MUSCIMA++, a new dataset for OMR. Ground truth in MUSCIMA++ is a notation graph, which our analysis shows to be a necessary and sufficient representation of music notation. Building on the CVC-MUSCIMA dataset for staffline removal, the MUSCIMA++ dataset v1.0 consists of 140 pages of handwritten music, with 91254 manually annotated notation symbols and 82247 explicitly marked relationships between symbol pairs. The dataset allows training and directly evaluating models for symbol classification, symbol localization, and notation graph assembly, and indirectly musical content extraction, both in isolation and jointly. Open-source tools are provided for manipulating the dataset, visualizing the data and annotating further, and the data is made available under an open license.

In this paper, we survey different state-of-the-art visual processing methods and utilize them in hyperlinking. Visual information, calculated using Features Signatures, SIMILE descriptors and convolutional neural networks (CNN), is utilized as similarity between video frames and used to find similar faces, objects and setting. Visual concepts in frames are also automatically recognized and textual output of the recognition is combined with search based on subtitles and transcripts. All presented experiments were performed in the Search and Hyperlinking 2014 MediaEval task and Video Hyperlinking 2015 TRECVid task.

Abstract A spelling error detection and correction application is typically based on three main components: a dictionary (or reference word list), an error model and a language model. While most of the attention in the literature has been directed to the language model, we show how improvements in any of the three components can lead to significant cumulative improvements in the overall performance of the system. We develop our dictionary of 9.2 million fully-inflected Arabic words (types) from a morphological transducer and a large corpus, validated and manually revised. We improve the error model by analyzing error types and creating an edit distance re-ranker. We also improve the language model by analyzing the level of noise in different data sources and selecting an optimal subset to train the system on. Testing and evaluation experiments show that our system significantly outperforms Microsoft Word 2013, OpenOffice Ayaspell 3.4 and Google Docs.

This paper explores the use of linguistic information for the selection of data to train language models. We depart from the state-of-the-art method in perplexity-based data selection and extend it in order to use word-level linguistic units (i.e. lemmas, named entity categories and part-of-speech tags) instead of surface forms. We then present two methods that combine the different types of linguistic knowledge as well as the surface forms (1, naïve selection of the top ranked sentences selected by each method; 2, linear interpolation of the datasets selected by the different methods). The paper presents detailed results and analysis for four languages with different levels of morphologic complexity (English, Spanish, Czech and Chinese). The interpolation-based combination outperforms the purely statistical baseline in all the scenarios, resulting in language models with lower perplexity. In relative terms the improvements are similar regardless of the language, with perplexity reductions achieved in the range 7.72–13.02%. In absolute terms the reduction is higher for languages with high type-token ratio (Chinese, 202.16) or rich morphology (Czech, 81.53) and lower for the remaining languages, Spanish (55.2) and English (34.43 on the English side of the same parallel dataset as for Czech and 61.90 on the same parallel dataset as for Spanish).

This paper deals with Information Retrieval from audio-visual recordings. Such recordings are often quite long and users may want to find the exact starting points of relevant passages they search for. In Passage Retrieval, the recordings are automatically segmented into smaller parts, on which the standard retrieval techniques are applied. In this paper, we discuss various techniques for segmentation of audio-visual recordings and focus on machine learning approaches which decide on segment boundaries based on various features combined in a decision-tree model. Our experiments are carried out on the data used for the Search and Hyperlinking Task and Similar Segments in Social Speech Task of the MediaEval Benchmark 2013.

We develop an open-source large-scale finite-state morphological processing toolkit (AraComLex) for Modern Standard Arabic (MSA) distributed under the GPLv3 license (http://aracomlex.sourceforge.net). The morphological transducer is based on a lexical database specifically constructed for this purpose. In contrast to previous resources, the database is tuned to MSA, eliminating lexical entries no longer attested in contemporary use. The database is built using a corpus of 1,089,111,204 word tokens, a pre-annotation tool, machine learning techniques and knowledge-based pattern matching to automatically acquire lexical knowledge. Our morphological transducer is evaluated and compared to LDC's SAMA(Standard Arabic Morphological Analyser). We also develop a finite-state morphological guesser as part of a methodology for extracting unknown word forms, lemmatizing them, and giving them a priority weight for inclusion in the lexicon.

Searching for relevant webpages and following hyperlinks to related content is a widely accepted and effective approach to information seeking on the textual web. Existing work on multimedia information retrieval has focused on search for individual relevant items or on content linking without specific attention to search results. We describe our research exploring integrated multimodal search and hyperlinking for multimedia data. Our investigation is based on the MediaEval 2012 Search and Hyperlinking task. This includes a known-item search task using the Blip10000 internet video collection, where automatically created hyperlinks link each relevant item to related items within the collection. The search test queries and link assessment for this task was generated using the Amazon Mechanical Turk crowdsourcing platform. Our investigation examines a range of alternative methods which seek to address the challenges of search and hyperlinking using multimodal approaches. The results of our experiments are used to propose a research agenda for developing effective techniques for search and hyperlinking of multimedia content.

Collocation is a common language phenomenon which has attracted the interest of researchers in many subfields of both theoretical and computational linguistics.Although there is no commonly accepted and precise definition of this phenomenon, collocations are generally understood as complex lexical items, often characterized as unpredictable, idiosyncratic, holistic, mutually selective, and so forth.Together with other types of multiword expressions (or phraseological units, such as compound nouns, phrasal verbs, idioms, etc.), collocations form a borderline phenomenon positioned between lexis and grammar: On one hand, they are unpredictable and must be learned in the same way as single words are (as whole units); on the other hand, they often also have internal syntactic structure and their components must then adhere to grammatical rules.Collocations play an important role in applications involving text production (e.g., machine translation and language generation), text analysis (e.g., parsing and word sense disambiguation), and also in other related tasks (such as information extraction, text classification, etc.).The book Syntax-Based Collocation Extraction by Violeta Seretan is based on her doctoral dissertation defended in 2008 at the Department of Linguistics, University of Geneva, under the supervision of Eric Wehrli, and refers to a number of their previous publications.The main text is divided into six chapters (amounting to 128 pages) and six appendices (70 pages).The first chapter can be regarded as a motivation for the whole work.It introduces the notion of collocation, explains its relevance (and importance) for natural language processing, specifies the aims of the work, and most importantly, it presents arguments for syntax-based collocation extraction as a more appropriate alternative to the traditional syntax-free n-gram and window-based techniques.

Thesauri and ontologies provide important value in facilitating access to digital archives by representing underlying principles of organization. Translation of such resources into multiple languages is an important component for providing multilingual access. However, the specificity of vocabulary terms in most ontologies precludes fully-automated machine translation using general-domain lexical resources. In this paper, we present an efficient process for leveraging human translations when constructing domain-specific lexical resources. We evaluate the effectiveness of this process by producing a probabilistic phrase dictionary and translating a thesaurus of 56,000 concepts used to catalogue a large archive of oral histories. Our experiments demonstrate a cost-effective technique for accurate machine translation of large ontologies.