23 research outputs found
Relation Extraction Using Convolution Tree Kernel Expanded with Entity Features
PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200
Extracting spatial relations from document for geographic information retrieval
IEEE Geoscience and Remote Sensing Society (IEEE GRSS); East China Norm. Univ., Sch. Resour. Environ. Sci.; Shanghai Urban Dev. Inf. Res. Cent.; The Geographical Society of Shanghai; East China Univ. Sci. Technol., Bus. Sch.<span class="MedBlackText">Geographic information retrieval (GIR) is developed to retrieve geographical information from unstructured text (commonly web documents). Previous researches focus on applying traditional information retrieval (IR) techniques to GIR, such as ranking geographic relevance by vector space model (VSM). In many cases, these keyword-based methods can not support spatial query very well. For example, searching documents on "debris flow took place in Hunan last year", the documents selected in this way may only contain the words "debris flow" and "Hunan" rather than refer to "debris" flow actually occurred in "Hunan". Lack of spatial relations between thematic activates (debris flow) and geographic entities (Hunan) is the key reason for this problem. In this paper, we present a kernel-based approach and apply it in support vector machine (SVM) to extract spatial relations from free text for further GIS service and spatial reasoning. First, we analyze the characters of spatial relation expressions in natural language and there are two types of spatial relations: topology and direction. Both of them are used to qualitatively describe the relative positions of spatial objects to each other. Then we explore the use of dependency tree (a dependency tree represents the grammatical dependencies in a sentence and it can be generated by syntax parser) to identify these spatial relations. We observe that the features required to find a relationship between two spatial named entities in the same sentence is typically captured by the shortest path between the two entities in the dependency tree. Therefore, we construct a shortest path dependency kernel for SVM to complete the task. The experiment results show that our dependency tree kernel achieves significant improvement than previous method. </span
Sentiment Sentence Extraction Using a Hierarchical Directed Acyclic graph Structure and Bootstrap Approach
PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200
Ekstraksi Relasi Antar Entitas di Bahasa Indonesia Menggunakan Neural Network
Dengan perkembangan zaman yang begitu pesat, berdampak pada perkembangan data pula. Salah satu bentuk data yang paling banyak saat ini berupa data tekstual seperti artikel sederhana maupun dokumen lain yang terdapat di internet. Agar data tekstual tersebut dapat dimengerti dan dimanfaatkan dengan baik oleh manusia, maka perlu di proses dan disederhanakan agar menjadi informasi yang ringkas dan jelas. Oleh karena itu, semakin berkembang pula penelitian dalam bidang Information Extraction (IE) dan salah satu contoh penelitian di IE adalah Relation Extraction (RE). Penelitian RE sudah banyak dilakukan terutama pada Bahasa Inggris dimana resourcenya sudah termasuk banyak. Metode yang digunakan pun bermacam-macam seperti kernel, tree kernel, support vector machine, long short-term memory, convulution recurrent neural network, dan lain sebagainya. Pada penelitian kali ini adalah penelitian RE pada Bahasa Indonesia dengan menggunakan metode convulution recurrent neural network yang sudah dipergunakan untuk RE Bahasa Inggris. Dataset yang digunakan pada penelitian ini adalah dataset Bahasa Indonesia yang berasal dari file xml wikipedia. File xml wikipedia ini kemudian diproses sehingga menghasilkan dataset seperti yang digunakan pada CRNN dalam Bahasa inggris yaitu dalam format SemEval-2 Task 8. Uji coba dilakukan dengan berbagai macam perbandingan data training dan testing yaitu 80:20, 70:30, dan 60:40. Selain itu, parameter pooling untuk CRNN yang digunakan ada dua macam yaitu ‘att’ dan ‘max’. Dari uji coba yang dilakukan, hasil yang didapatkan adalah bervariasi mulai dari mendekati maupun lebih baik bila dibandingkan dengan CRNN dengan menggunakan dataset Bahasa inggris sehingga dapat disimpulkan bahwa dengan CRNN ini bisa digunakan untuk proses RE pada Bahasa Indonesia apabila dataset yang digunakan sesuai dengan penelitian sebelumnya
Using Tree Kernels for Classifying Temporal Relations between Events
PACLIC 23 / City University of Hong Kong / 3-5 December 200
Tree Kernel Usage in Naive Bayes Classifiers
We present a novel approach in machine learning
by combining naive Bayes classifiers with tree
kernels. Tree kernel methods produce promising
results in machine learning tasks containing treestructured
attribute values. These kernel methods
are used to compare two tree-structured attribute
values recursively. Up to now tree kernels are
only used in kernel machines like Support Vector
Machines or Perceptrons.
In this paper, we show that tree kernels can be
utilized in a naive Bayes classifier enabling the
classifier to handle tree-structured values. We
evaluate our approach on three datasets containing
tree-structured values. We show that our
approach using tree-structures delivers significantly
better results in contrast to approaches using
non-structured (flat) features extracted from
the tree. Additionally, we show that our approach
is significantly faster than comparable kernel machines
in several settings which makes it more
useful in resource-aware settings like mobile devices