77,259 research outputs found
Japanese/English Cross-Language Information Retrieval: Exploration of Query Translation and Transliteration
Cross-language information retrieval (CLIR), where queries and documents are
in different languages, has of late become one of the major topics within the
information retrieval community. This paper proposes a Japanese/English CLIR
system, where we combine a query translation and retrieval modules. We
currently target the retrieval of technical documents, and therefore the
performance of our system is highly dependent on the quality of the translation
of technical terms. However, the technical term translation is still
problematic in that technical terms are often compound words, and thus new
terms are progressively created by combining existing base words. In addition,
Japanese often represents loanwords based on its special phonogram.
Consequently, existing dictionaries find it difficult to achieve sufficient
coverage. To counter the first problem, we produce a Japanese/English
dictionary for base words, and translate compound words on a word-by-word
basis. We also use a probabilistic method to resolve translation ambiguity. For
the second problem, we use a transliteration method, which corresponds words
unlisted in the base word dictionary to their phonetic equivalents in the
target language. We evaluate our system using a test collection for CLIR, and
show that both the compound word translation and transliteration methods
improve the system performance
Structure-semantics interplay in complex networks and its effects on the predictability of similarity in texts
There are different ways to define similarity for grouping similar texts into
clusters, as the concept of similarity may depend on the purpose of the task.
For instance, in topic extraction similar texts mean those within the same
semantic field, whereas in author recognition stylistic features should be
considered. In this study, we introduce ways to classify texts employing
concepts of complex networks, which may be able to capture syntactic, semantic
and even pragmatic features. The interplay between the various metrics of the
complex networks is analyzed with three applications, namely identification of
machine translation (MT) systems, evaluation of quality of machine translated
texts and authorship recognition. We shall show that topological features of
the networks representing texts can enhance the ability to identify MT systems
in particular cases. For evaluating the quality of MT texts, on the other hand,
high correlation was obtained with methods capable of capturing the semantics.
This was expected because the golden standards used are themselves based on
word co-occurrence. Notwithstanding, the Katz similarity, which involves
semantic and structure in the comparison of texts, achieved the highest
correlation with the NIST measurement, indicating that in some cases the
combination of both approaches can improve the ability to quantify quality in
MT. In authorship recognition, again the topological features were relevant in
some contexts, though for the books and authors analyzed good results were
obtained with semantic features as well. Because hybrid approaches encompassing
semantic and topological features have not been extensively used, we believe
that the methodology proposed here may be useful to enhance text classification
considerably, as it combines well-established strategies
Rotation-invariant features for multi-oriented text detection in natural images.
Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes
What’s going on in my city? Recommender systems and electronic participatory budgeting
In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities – Cambridge, Miami and New York City–, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area
Applying Machine Translation to Two-Stage Cross-Language Information Retrieval
Cross-language information retrieval (CLIR), where queries and documents are
in different languages, needs a translation of queries and/or documents, so as
to standardize both of them into a common representation. For this purpose, the
use of machine translation is an effective approach. However, computational
cost is prohibitive in translating large-scale document collections. To resolve
this problem, we propose a two-stage CLIR method. First, we translate a given
query into the document language, and retrieve a limited number of foreign
documents. Second, we machine translate only those documents into the user
language, and re-rank them based on the translation result. We also show the
effectiveness of our method by way of experiments using Japanese queries and
English technical documents.Comment: 13 pages, 1 Postscript figur
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
Machine translation evaluation resources and methods: a survey
We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT.
This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content
- …