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Pemodelan frasa pengandung jawaban (ABP-LG) untuk sistem tanya jawab = least generalized answer bearing phrase (ABP-LG) model for answer extraction / Hapnes Toba

Hapnes Toba; Belawati H. Widjaja, promotor; Ito Wasito, examiner; Indra Budi, examiner; Achmad Nizar Hidayanto, promotor; Rila Mandala, examiner; Mirna Adriani, co-promotor; Manurung, Maruli, co-promotor ([Publisher not identified] , 2014)

 Abstrak

[Sebuah sistem tanya jawab (STJ) adalah sebuah sistem komputer yang dirancang
untuk mencari jawaban yang paling tepat terhadap sebuah pertanyaan yang
diajukan dalam sebuah bahasa alami. Penelitian terkait STJ telah dilakukan sejak
awal tahun 60-an, dan mengalami perkembangan yang pesat sejak diadakannya
forum-forum evaluasi STJ sejak tahun 90-an sampai saat ini. Bidang-bidang
penelitian dalam ilmu komputer yang memberikan kontribusi besar dalam
perkembangan STJ meliputi antara lain: temu balik informasi, pemrosesan bahasa
alami, dan kecerdasan buatan.
Secara khusus dalam riset doktoral ini dilakukan eksplorasi terhadap
komponen validasi jawaban. Riset bertujuan untuk menghasilkan metode baru
yang dapat meningkatkan relevansi cuplikan teks dan mencari strategi untuk
melakukan ekstraksi jawaban dengan mengkombinasikan pendekatan statist ik dan
simbolik. Terdapat dua usulan yang diberikan guna mencapai tujuan riset. Usul
yang pertama adalah penggunaan model kualitas jawaban yang dikembangkan
dari STJ berbasis komunitas sebagai alat untuk melakukan pengurutan ulang
cuplikan teks. Usul yang kedua adalah pembentukan model jawaban melalui
pembelajaran frasa pengandung jawaban terkecil dan terlengkap (least
generalized answer bearing phrase/ABP-LG) sebagai sarana untuk memprediksi
bagian kalimat yang paling memungkinkan mengandung jawaban. Model ABPLG
memanfaatkan informasi struktur kalimat pada pertanyaan dan cuplikan teks
sebagai indikator yang menentukan peluang kandungan jawaban dalam sebuah
bagian kalimat.
Hasil eksperimen dengan berbagai koleksi data memperlihatkan bahwa
kombinasi model ABP-LG dengan sistem berbasis pola mampu memberikan
kontribusi untuk perbaikan hasil ekstraksi jawaban secara signifikan untuk tipe
pertanyaan faktoid maupun kompleks (tipe lain-lain). Keunggulan model ABP-LG
jika dibandingkan dengan STJ berbasis entitas bernama ataupun kamus adalah
kemampuannya untuk mempelajari indikasi 'cara menjawab' dan portabilitasnya
untuk diterapkan dalam domain pertanyaan yang berbeda-beda, khususnya untuk
tipe-tipe pertanyaan yang dapat mencakup konteks apapun, seperti dalam tipe
'other' (lain-lain). Kelemahan model ABP-LG yang teramati selama eksperimen
adalah ketergantungannya pada kualitas teks. Problem terakhir ini secara parsial
berhasil ditangani oleh model pengurutan ulang cuplikan teks sebagai penyaring
kandidat-kandidat kalimat yang dianggap mengandung jawaban dari hasil temu
balik informasi.;The task of a question answering system (QAS) is to find a final answer given a
natural language question. Since it was introduced in the 1960s, the task of QAS
has always been at the forefront of technology advances. Along with the advances
in the fields of information retrieval, computational linguistics, and artificial
intelligence, research on QAS are broadened into unstructured textual documents
in open domains. Evaluation forums for QAS have steered the development of QAS
into an established and large-scale research methodologies and evaluations.
This doctoral research investigates various techniques in the answer
validation component. The main objective of the research is to develop new
methods in snippet reranking and answer extraction process by combining the
statistical and the symbolic (semantics) approaches. Two novel techniques are
proposed as the results of this doctoral research. The first one is the snippets'
reranking model which is developed by using the question-answer pairs'
characteristics in a community-based QAS. This answer quality model forms the
basic ingredient for the snippet reranking process. The second proposal is the least
generalized answer bearing phrase model (ABP-LG) to predict the final answer
location of a given question which is extracted from a number of good quality
snippets, after a reranking process. The ABP-LG model employs syntactic tree
information of question-answer (snippet) pairs as indicators to predict the answer
bearing possibility in each part of a snippet.
The experiment results show that the ABP-LG model combines with the
pattern-based approach contributes considerably in the answer extraction process
for factoid- and complex (other)-typed questions. The main advantage of the ABPLG
model beyond the common approaches, which are based on named-entity
recognizers or dictionaries, is its ability to predict the 'way-of-answering', either in
factoid or complex question types. Based on the analysis of the experiment
results, the main weaknesses of the ABP-LG model is its high dependency on
good quality snippets which partially has been tackled by employing the snippets'
reranking model., The task of a question answering system (QAS) is to find a final answer given a
natural language question. Since it was introduced in the 1960s, the task of QAS
has always been at the forefront of technology advances. Along with the advances
in the fields of information retrieval, computational linguistics, and artificial
intelligence, research on QAS are broadened into unstructured textual documents
in open domains. Evaluation forums for QAS have steered the development of QAS
into an established and large-scale research methodologies and evaluations.
This doctoral research investigates various techniques in the answer
validation component. The main objective of the research is to develop new
methods in snippet reranking and answer extraction process by combining the
statistical and the symbolic (semantics) approaches. Two novel techniques are
proposed as the results of this doctoral research. The first one is the snippets'
reranking model which is developed by using the question-answer pairs'
characteristics in a community-based QAS. This answer quality model forms the
basic ingredient for the snippet reranking process. The second proposal is the least
generalized answer bearing phrase model (ABP-LG) to predict the final answer
location of a given question which is extracted from a number of good quality
snippets, after a reranking process. The ABP-LG model employs syntactic tree
information of question-answer (snippet) pairs as indicators to predict the answer
bearing possibility in each part of a snippet.
The experiment results show that the ABP-LG model combines with the
pattern-based approach contributes considerably in the answer extraction process
for factoid- and complex (other)-typed questions. The main advantage of the ABPLG
model beyond the common approaches, which are based on named-entity
recognizers or dictionaries, is its ability to predict the 'way-of-answering', either in
factoid or complex question types. Based on the analysis of the experiment
results, the main weaknesses of the ABP-LG model is its high dependency on
good quality snippets which partially has been tackled by employing the snippets'
reranking model.]

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 Metadata

No. Panggil : D1990
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Entri tambahan-Nama badan :
Penerbitan : [Place of publication not identified]: [Publisher not identified], 2014
Program Studi :
Bahasa : ind
Sumber Pengatalogan :
Tipe Konten : text
Tipe Media : unmediated ; computer
Tipe Carrier : volume ; online resource
Deskripsi Fisik : xix, 204 pages : illustration ; 28 cm + appendix
Naskah Ringkas :
Lembaga Pemilik : Universitas Indonesia
Lokasi : Perpustakaan UI, Lantai 3
  • Ketersediaan
  • Ulasan
No. Panggil No. Barkod Ketersediaan
D1990 TERSEDIA
Ulasan:
Tidak ada ulasan pada koleksi ini: 20404484