ATLAS: A flexible and extensible architecture for linguistic annotation

We describe a formal model for annotating linguistic artifacts, from which we derive an application programming interface (API) to a suite of tools for manipulating these annotations. The abstract logical model provides for a range of storage formats and promotes the reuse of tools that interact through this API. We focus first on ``Annotation Graphs,'' a graph model for annotations on linear signals (such as text and speech) indexed by intervals, for which efficient database storage and querying techniques are applicable. We note how a wide range of existing annotated corpora can be mapped to this annotation graph model. This model is then generalized to encompass a wider variety of linguistic ``signals,'' including both naturally occuring phenomena (as recorded in images, video, multi-modal interactions, etc.), as well as the derived resources that are increasingly important to the engineering of natural language processing systems (such as word lists, dictionaries, aligned bilingual corpora, etc.). We conclude with a review of the current efforts towards implementing key pieces of this architecture.Comment: 8 pages, 9 figure

Ubiquitous Place Names Standardization and Study in Indonesia

Place names play a vital role in human society. Names exist in all languages and place names are an indispensible part of International communication. This has been acknowledged by the establishment of the United Nations Group of Experts on Geographical Names (UNGEGN). One of UNGEGN's tasks is to coordinate International efforts on the proper use of place names. Indonesia supports this effort and through its National Geospatial Agency (BIG). Place names are also of interest as an object of study in themselves. Academic studies into place names are found in linguistics, onomastics, philosophy and a number of other academic disciplines. This article looks at these two dimensions of place names, standardization efforts under the auspices of International and national bodies, and academic studies of names, with particular reference to the situation in Indonesia

Neural Vector Spaces for Unsupervised Information Retrieval

We propose the Neural Vector Space Model (NVSM), a method that learns representations of documents in an unsupervised manner for news article retrieval. In the NVSM paradigm, we learn low-dimensional representations of words and documents from scratch using gradient descent and rank documents according to their similarity with query representations that are composed from word representations. We show that NVSM performs better at document ranking than existing latent semantic vector space methods. The addition of NVSM to a mixture of lexical language models and a state-of-the-art baseline vector space model yields a statistically significant increase in retrieval effectiveness. Consequently, NVSM adds a complementary relevance signal. Next to semantic matching, we find that NVSM performs well in cases where lexical matching is needed. NVSM learns a notion of term specificity directly from the document collection without feature engineering. We also show that NVSM learns regularities related to Luhn significance. Finally, we give advice on how to deploy NVSM in situations where model selection (e.g., cross-validation) is infeasible. We find that an unsupervised ensemble of multiple models trained with different hyperparameter values performs better than a single cross-validated model. Therefore, NVSM can safely be used for ranking documents without supervised relevance judgments.Comment: TOIS 201

Stabilizing knowledge through standards - A perspective for the humanities

It is usual to consider that standards generate mixed feelings among scientists. They are often seen as not really reflecting the state of the art in a given domain and a hindrance to scientific creativity. Still, scientists should theoretically be at the best place to bring their expertise into standard developments, being even more neutral on issues that may typically be related to competing industrial interests. Even if it could be thought of as even more complex to think about developping standards in the humanities, we will show how this can be made feasible through the experience gained both within the Text Encoding Initiative consortium and the International Organisation for Standardisation. By taking the specific case of lexical resources, we will try to show how this brings about new ideas for designing future research infrastructures in the human and social sciences

Kun-dangwok: ''clan lects'' and Ausbau in western Arnhem Land

The sociolinguistic concept of an Ausbau language is widely thought of as exclusively associated with the standardization of languages for the political and social purposes of nation states. Language policy initiated by state institutions, the development of literacy and new specialist registers of language are typical elements involved in the Ausbau process. However, the linguistic ideologies of small language groups such as those of the minority languages of Aboriginal Australia can drive certain forms of deliberate language elaboration. An important aspect of Aboriginal linguistic ideology is language diversity, reflected in the development of elemental sociolinguistic varieties such as patrician lects. In the Bininj Kun-wok dialect chain of western Arnhem Land, a regional system of lectal differentiation known as Kun-dangwok has developed, reflecting an Aboriginal linguistic ideology whereby being different, especially different ways of speaking, are seen as central aspects of identity. The functions of the Kun-dangwok clan led system are described using examples of naturally occurring conversation which provide evidence that clan lects are the result of an Ausbau process that results in the opposite of language standardization and an increase in Abstand between varieties

A Formal Framework for Linguistic Annotation

`Linguistic annotation' covers any descriptive or analytic notations applied to raw language data. The basic data may be in the form of time functions -- audio, video and/or physiological recordings -- or it may be textual. The added notations may include transcriptions of all sorts (from phonetic features to discourse structures), part-of-speech and sense tagging, syntactic analysis, `named entity' identification, co-reference annotation, and so on. While there are several ongoing efforts to provide formats and tools for such annotations and to publish annotated linguistic databases, the lack of widely accepted standards is becoming a critical problem. Proposed standards, to the extent they exist, have focussed on file formats. This paper focuses instead on the logical structure of linguistic annotations. We survey a wide variety of existing annotation formats and demonstrate a common conceptual core, the annotation graph. This provides a formal framework for constructing, maintaining and searching linguistic annotations, while remaining consistent with many alternative data structures and file formats.Comment: 49 page

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Adversarial Network Bottleneck Features for Noise Robust Speaker Verification

In this paper, we propose a noise robust bottleneck feature representation which is generated by an adversarial network (AN). The AN includes two cascade connected networks, an encoding network (EN) and a discriminative network (DN). Mel-frequency cepstral coefficients (MFCCs) of clean and noisy speech are used as input to the EN and the output of the EN is used as the noise robust feature. The EN and DN are trained in turn, namely, when training the DN, noise types are selected as the training labels and when training the EN, all labels are set as the same, i.e., the clean speech label, which aims to make the AN features invariant to noise and thus achieve noise robustness. We evaluate the performance of the proposed feature on a Gaussian Mixture Model-Universal Background Model based speaker verification system, and make comparison to MFCC features of speech enhanced by short-time spectral amplitude minimum mean square error (STSA-MMSE) and deep neural network-based speech enhancement (DNN-SE) methods. Experimental results on the RSR2015 database show that the proposed AN bottleneck feature (AN-BN) dramatically outperforms the STSA-MMSE and DNN-SE based MFCCs for different noise types and signal-to-noise ratios. Furthermore, the AN-BN feature is able to improve the speaker verification performance under the clean condition