2,774 research outputs found
Tagging Scientific Publications using Wikipedia and Natural Language Processing Tools. Comparison on the ArXiv Dataset
In this work, we compare two simple methods of tagging scientific
publications with labels reflecting their content. As a first source of labels
Wikipedia is employed, second label set is constructed from the noun phrases
occurring in the analyzed corpus. We examine the statistical properties and the
effectiveness of both approaches on the dataset consisting of abstracts from
0.7 million of scientific documents deposited in the ArXiv preprint collection.
We believe that obtained tags can be later on applied as useful document
features in various machine learning tasks (document similarity, clustering,
topic modelling, etc.)
Smartphone picture organization: a hierarchical approach
We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin
Multiple Retrieval Models and Regression Models for Prior Art Search
This paper presents the system called PATATRAS (PATent and Article Tracking,
Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach
presents three main characteristics: 1. The usage of multiple retrieval models
(KL, Okapi) and term index definitions (lemma, phrase, concept) for the three
languages considered in the present track (English, French, German) producing
ten different sets of ranked results. 2. The merging of the different results
based on multiple regression models using an additional validation set created
from the patent collection. 3. The exploitation of patent metadata and of the
citation structures for creating restricted initial working sets of patents and
for producing a final re-ranking regression model. As we exploit specific
metadata of the patent documents and the citation relations only at the
creation of initial working sets and during the final post ranking step, our
architecture remains generic and easy to extend
Semantic modelling of user interests based on cross-folksonomy analysis
The continued increase in Web usage, in particular participation in folksonomies, reveals a trend towards a more dynamic and interactive Web where individuals can organise and share resources. Tagging has emerged as the de-facto standard for the organisation of such resources, providing a versatile and reactive knowledge management mechanism that users find easy to use and understand. It is common nowadays for users to have multiple profiles in various folksonomies, thus distributing their tagging activities. In this paper, we present a method for the automatic consolidation of user profiles across two popular social networking sites, and subsequent semantic modelling of their interests utilising Wikipedia as a multi-domain model. We evaluate how much can be learned from such sites, and in which domains the knowledge acquired is focussed. Results show that far richer interest profiles can be generated for users when multiple tag-clouds are combine
An approach to graph-based analysis of textual documents
In this paper a new graph-based model is proposed for the representation of textual documents. Graph-structures are obtained from textual documents by making use of the well-known Part-Of-Speech (POS) tagging technique. More specifically, a simple rule-based (re) classifier is used to map each tag onto graph vertices and edges. As a result, a decomposition of textual documents is obtained where tokens are automatically parsed and attached to either a vertex or an edge. It is shown how textual documents can be aggregated through their graph-structures and finally, it is shown how vertex-ranking methods can be used to find relevant tokens.(1)
What to do about non-standard (or non-canonical) language in NLP
Real world data differs radically from the benchmark corpora we use in
natural language processing (NLP). As soon as we apply our technologies to the
real world, performance drops. The reason for this problem is obvious: NLP
models are trained on samples from a limited set of canonical varieties that
are considered standard, most prominently English newswire. However, there are
many dimensions, e.g., socio-demographics, language, genre, sentence type, etc.
on which texts can differ from the standard. The solution is not obvious: we
cannot control for all factors, and it is not clear how to best go beyond the
current practice of training on homogeneous data from a single domain and
language.
In this paper, I review the notion of canonicity, and how it shapes our
community's approach to language. I argue for leveraging what I call fortuitous
data, i.e., non-obvious data that is hitherto neglected, hidden in plain sight,
or raw data that needs to be refined. If we embrace the variety of this
heterogeneous data by combining it with proper algorithms, we will not only
produce more robust models, but will also enable adaptive language technology
capable of addressing natural language variation.Comment: KONVENS 201
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
Extracting corpus specific knowledge bases from Wikipedia
Thesauri are useful knowledge structures for assisting information retrieval. Yet their production is labor-intensive, and few domains have comprehensive thesauri that cover domain-specific concepts and contemporary usage. One approach, which has been attempted without much success for decades, is to seek statistical natural language processing algorithms that work on free text. Instead, we propose to replace costly professional indexers with thousands of dedicated amateur volunteers--namely, those that are producing Wikipedia. This vast, open encyclopedia represents a rich tapestry of topics and semantics and a huge investment of human effort and judgment. We show how this can be directly exploited to provide WikiSauri: manually-defined yet inexpensive thesaurus structures that are specifically tailored to expose the topics, terminology and semantics of individual document collections. We also offer concrete evidence of the effectiveness of WikiSauri for assisting information retrieval
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