Product developers frequently discuss topics related to their development project with others, but often use technical terms whose meanings are not clear to non-specialists. To provide non-experts with precise and comprehensive understanding of the know-who/know-how being discussed, the method proposed herein categorizes the messages using a taxonomy of the products being developed and a taxonomy of tasks relevant to those products. The instances in the taxonomy are products and/or tasks manually selected as relevant to system development. The concepts are defined by the taxonomy of instances. That proposed method first extracts phrases from discussion logs as data-driven instances relevant to system development. It then classifies those phrases to the concepts defined by taxonomy experts. The innovative feature of our method is that in classifying a phrase to a concept, say C, the method considers the associations of the phrase with not only the instances of C, but also with the instances of the neighbor concepts of C (neighbor is defined by the taxonomy). This approach is quite accurate in classifying phrases to concepts; the phrase is classified to C, not the neighbors of C, even though they are quite similar to C. Next, we attach a data-driven concept to C; the data-driven concept includes instances in C and a classified phrase as a data-driven instance. We analyze know-who and know-how by using not only human-defined concepts but also those data-driven concepts. We evaluate our method using the mailing-list of an actual project. It could classify phrases with twice the accuracy possible with the TF/iDF method, which does not consider the neighboring concepts. The taxonomy with data-driven concepts provides more detailed know-who/know-how than can be obtained from just the human-defined concepts themselves or from the data-driven concepts as determined by the TF/iDF method.
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Makoto NAKATSUJI, Akimichi TANAKA, Takahiro MADOKORO, Kenichiro OKAMOTO, Sumio MIYAZAKI, Tadasu UCHIYAMA, "Extracting Know-Who/Know-How Using Development Project-Related Taxonomies" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 10, pp. 2717-2727, October 2010, doi: 10.1587/transinf.E93.D.2717.
Abstract: Product developers frequently discuss topics related to their development project with others, but often use technical terms whose meanings are not clear to non-specialists. To provide non-experts with precise and comprehensive understanding of the know-who/know-how being discussed, the method proposed herein categorizes the messages using a taxonomy of the products being developed and a taxonomy of tasks relevant to those products. The instances in the taxonomy are products and/or tasks manually selected as relevant to system development. The concepts are defined by the taxonomy of instances. That proposed method first extracts phrases from discussion logs as data-driven instances relevant to system development. It then classifies those phrases to the concepts defined by taxonomy experts. The innovative feature of our method is that in classifying a phrase to a concept, say C, the method considers the associations of the phrase with not only the instances of C, but also with the instances of the neighbor concepts of C (neighbor is defined by the taxonomy). This approach is quite accurate in classifying phrases to concepts; the phrase is classified to C, not the neighbors of C, even though they are quite similar to C. Next, we attach a data-driven concept to C; the data-driven concept includes instances in C and a classified phrase as a data-driven instance. We analyze know-who and know-how by using not only human-defined concepts but also those data-driven concepts. We evaluate our method using the mailing-list of an actual project. It could classify phrases with twice the accuracy possible with the TF/iDF method, which does not consider the neighboring concepts. The taxonomy with data-driven concepts provides more detailed know-who/know-how than can be obtained from just the human-defined concepts themselves or from the data-driven concepts as determined by the TF/iDF method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2717/_p
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@ARTICLE{e93-d_10_2717,
author={Makoto NAKATSUJI, Akimichi TANAKA, Takahiro MADOKORO, Kenichiro OKAMOTO, Sumio MIYAZAKI, Tadasu UCHIYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Extracting Know-Who/Know-How Using Development Project-Related Taxonomies},
year={2010},
volume={E93-D},
number={10},
pages={2717-2727},
abstract={Product developers frequently discuss topics related to their development project with others, but often use technical terms whose meanings are not clear to non-specialists. To provide non-experts with precise and comprehensive understanding of the know-who/know-how being discussed, the method proposed herein categorizes the messages using a taxonomy of the products being developed and a taxonomy of tasks relevant to those products. The instances in the taxonomy are products and/or tasks manually selected as relevant to system development. The concepts are defined by the taxonomy of instances. That proposed method first extracts phrases from discussion logs as data-driven instances relevant to system development. It then classifies those phrases to the concepts defined by taxonomy experts. The innovative feature of our method is that in classifying a phrase to a concept, say C, the method considers the associations of the phrase with not only the instances of C, but also with the instances of the neighbor concepts of C (neighbor is defined by the taxonomy). This approach is quite accurate in classifying phrases to concepts; the phrase is classified to C, not the neighbors of C, even though they are quite similar to C. Next, we attach a data-driven concept to C; the data-driven concept includes instances in C and a classified phrase as a data-driven instance. We analyze know-who and know-how by using not only human-defined concepts but also those data-driven concepts. We evaluate our method using the mailing-list of an actual project. It could classify phrases with twice the accuracy possible with the TF/iDF method, which does not consider the neighboring concepts. The taxonomy with data-driven concepts provides more detailed know-who/know-how than can be obtained from just the human-defined concepts themselves or from the data-driven concepts as determined by the TF/iDF method.},
keywords={},
doi={10.1587/transinf.E93.D.2717},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Extracting Know-Who/Know-How Using Development Project-Related Taxonomies
T2 - IEICE TRANSACTIONS on Information
SP - 2717
EP - 2727
AU - Makoto NAKATSUJI
AU - Akimichi TANAKA
AU - Takahiro MADOKORO
AU - Kenichiro OKAMOTO
AU - Sumio MIYAZAKI
AU - Tadasu UCHIYAMA
PY - 2010
DO - 10.1587/transinf.E93.D.2717
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - Product developers frequently discuss topics related to their development project with others, but often use technical terms whose meanings are not clear to non-specialists. To provide non-experts with precise and comprehensive understanding of the know-who/know-how being discussed, the method proposed herein categorizes the messages using a taxonomy of the products being developed and a taxonomy of tasks relevant to those products. The instances in the taxonomy are products and/or tasks manually selected as relevant to system development. The concepts are defined by the taxonomy of instances. That proposed method first extracts phrases from discussion logs as data-driven instances relevant to system development. It then classifies those phrases to the concepts defined by taxonomy experts. The innovative feature of our method is that in classifying a phrase to a concept, say C, the method considers the associations of the phrase with not only the instances of C, but also with the instances of the neighbor concepts of C (neighbor is defined by the taxonomy). This approach is quite accurate in classifying phrases to concepts; the phrase is classified to C, not the neighbors of C, even though they are quite similar to C. Next, we attach a data-driven concept to C; the data-driven concept includes instances in C and a classified phrase as a data-driven instance. We analyze know-who and know-how by using not only human-defined concepts but also those data-driven concepts. We evaluate our method using the mailing-list of an actual project. It could classify phrases with twice the accuracy possible with the TF/iDF method, which does not consider the neighboring concepts. The taxonomy with data-driven concepts provides more detailed know-who/know-how than can be obtained from just the human-defined concepts themselves or from the data-driven concepts as determined by the TF/iDF method.
ER -