International Conference on Asian Digital Libraries 

About: International Conference on Asian Digital Libraries is an academic conference. The conference publishes majorly in the area(s): Digital library & Metadata. Over the lifetime, 874 publications have been published by the conference receiving 4636 citations.

Keyphrase extraction in scientific publications

Abstract: We present a keyphrase extraction algorithm for scientific publications Different from previous work, we introduce features that capture the positions of phrases in document with respect to logical sections found in scientific discourse We also introduce features that capture salient morphological phenomena found in scientific keyphrases, such as whether a candidate keyphrase is an acronyms or uses specific terminologically productive suffixes We have implemented these features on top of a baseline feature set used by Kea [1] In our evaluation using a corpus of 120 scientific publications multiply annotated for keyphrases, our system significantly outperformed Kea at the p < 05 level As we know of no other existing multiply annotated keyphrase document collections, we have also made our evaluation corpus publicly available We hope that this contribution will spur future comparative research

Sentiment Classification of Drug Reviews Using a Rule-Based Linguistic Approach

Abstract: Clause-level sentiment classification algorithm is developed and applied to drug reviews on a discussion forum. The algorithm adopts a pure linguistic approach of computing the sentiment of a clause from the prior sentiment scores assigned to individual words, taking into consideration the grammatical dependency structure of the clause using the sentiment analysis rules. MetaMap, a medical resource tool, is used to identify various disease terms in the review documents to utilize domain knowledge for sentiment classification. Experiment results with 1,000 clauses show the effectiveness of the proposed approach, and it performed significantly better than baseline machine learning approaches. Various challenging issues were identified through error analysis, and we will continue improving our linguistic algorithm.

Sentence-level sentiment polarity classification using a linguistic approach

Abstract: Recent sentiment analysis research has focused on the functional relations of words using typed dependency parsing as this provides a refined analysis on the grammar and semantics of the textual data, which could improve performance. However, typed dependencies only provide the grammatical relationships between individual words while there exist more complex relationships between words that could influence a sentence sentiment polarity. In this paper, we propose a linguistic approach, called Polarity Prediction Model (PPM), that combines typed dependencies and subjective phrase analysis to detect sentence-level sentiment polarity. Our approach also considers the intensity of words and domain terms that could influence the sentiment polarity output. PPM is shown to provide a fine-grained analysis for handling and explaining the complex relationships between words in detecting a sentence sentiment polarity. PPM was found to consistently outperform a baseline model by 5% in terms of overall F1-score, and exceeding 10% in terms of positive F1- score when compared to a Typed-dependency only approach.

Ontology-based metadata integration in the cultural heritage domain

Abstract: In this paper, we propose an ontology-based metadata integration methodology for the cultural heritage domain. The proposed real - world approach considers an integration architecture in which CIDOC/CRM ontology acts as a mediating scheme. In this context, we present a mapping methodology from Encoded Archival Description (EAD) and Dublin Core (DC) metadata to CIDOC/CRM, and discuss the faced difficulties.

VideoCube: A Novel Tool for Video Mining and Classification

Abstract: We propose a new tool to classify a video clip into one of n given classes (e.g., "news", "commercials", etc). The first novelty of our approach is a method to automatically derive a "vocabulary" from each class of video clips, using the powerful method of "Independent Component Analysis" (ICA). Second, the method is unified in that it works with both video and audio information, and gives vocabulary describing not only the still images, but also motion and the audio part. Further-more, this vocabulary is natural in that it is closely related to human perceptual processing. More specifically, every class of video clips gives a list of "basis functions", which can compress its members very well. Once we represent video clips in "vocabularies", we can do classification and pattern discovery. For the classification of a video clip, we propose using compression: we test which of the "vocabularies" can compress the video clip best, and we assign it to the corresponding class.For data mining, we inspect the basis functions of each video genre class and identify genre characteristics such as fast motions/transitions, more harmonic audio, etc. In experiments on real data of 62 news and 43 commercial clips, our method achieved overall accuracy of ? 81%.