N1005 IBM Research: natural language processing

Tired of answering questions? Let a smart machine help.

<div align="left" class="MsoNormal" style="text-align: left;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"></span></div><span lang="EN-US"><a href="http://researcher.ibm.com/researcher/view.php?person=jp-KAMIJOH"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Koichi Kamijo</span></a></span><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">, a scientist at IBM’s research lab in Tokyo was only 20 questions into a 300-question life insurance form. And he was already exhausted. There had to be an easier way to answer these questions. So, he got together with his colleagues, </span><span lang="EN-US"><a href="http://researcher.ibm.com/researcher/view.php?person=jp-RYOKAWA"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Ryo Kawahara</span></a></span><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">, </span><span lang="EN-US"><a href="http://researcher.ibm.com/researcher/view.php?person=jp-OSOGAMI"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Takayuki Osogami,</span></a></span><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"> Masaki Ono and Shunichi Amano to come up with a cognitive tech answer to the cumbersome process of finding a life insurance product. And they identified two critical issues with the life insurance questionnaire that needed to be solved to make life plan simulator more user-friendly and useful for consumers and financial planners.</span> <br /><div align="left" class="MsoNormal" style="text-align: left;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-no-proof: yes;">                                                 </span><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"></span></div><table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgEJdPJ17uLKTHo82p2igD77P1CRS2ogNOQQml39MSEdiTSOcSRYLcL4FyO9OMtV52rTAHA-DgcdX091mt3-SbBwKX3pR8jLVvAu82IkCwOe98-nVBX9_533N1XUaUJU8HUBdQMDYR2s3_U/s1600/Tokyo_team.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgEJdPJ17uLKTHo82p2igD77P1CRS2ogNOQQml39MSEdiTSOcSRYLcL4FyO9OMtV52rTAHA-DgcdX091mt3-SbBwKX3pR8jLVvAu82IkCwOe98-nVBX9_533N1XUaUJU8HUBdQMDYR2s3_U/s1600/Tokyo_team.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: x-small;">L-R: Masaki Ono, Ryo Kawahara, Koichi Kamijo, Takayuki Osogami<br />and Shunichi Amano of IBM Research – Tokyo</span></td></tr></tbody></table><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"></span><br /><a name="more"></a><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Typically, consumers are asked to answer an average of 300 questions, from name, age, and marital status, to expected salary at the age of retirement, and education expenses for their children. The first issue Koichi and his team recognized: there were too many questions! Going through hundreds of questions is too much for people, even though they help financial planners understand their needs. The second issue he and his team realized they needed to solve was how to help financial planners also predict potential needs. With his colleagues’ expertise in machine learning, business analytics and natural language processing – along with ST-Life (Smile and Thanks Life), whose life plan simulator Dynamic Life Plan Navigator has been widely used by enterprises and organizations in Japan – they developed an insurance information lifecycle management platform to solve these critical issues. <span style="mso-spacerun: yes;"> </span></span><br /><div align="left" class="MsoNormal" style="text-align: left;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt; mso-no-proof: yes;">  </span><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"></span></div><div class="separator" style="clear: both; text-align: center;"></div><div align="left" class="MsoNormal" style="text-align: left;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMXS4y1A49dw4Jns2tN1pwRcqYpnGh7yj2EvZn3iFBProaIAKvGHEpqR6XVwVokMY72yS0gLt8OLl14Y6pZkIFROB9HfBUhO4vtMPQ1S6X_0RPcXVBYHcAv4RgVLU5Bdw9WO2IJJDsE72K/s1600/life_sim.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiMXS4y1A49dw4Jns2tN1pwRcqYpnGh7yj2EvZn3iFBProaIAKvGHEpqR6XVwVokMY72yS0gLt8OLl14Y6pZkIFROB9HfBUhO4vtMPQ1S6X_0RPcXVBYHcAv4RgVLU5Bdw9WO2IJJDsE72K/s1600/life_sim.jpg" /></a></div></div><div align="left" class="MsoNormal" style="text-align: left;"><br /> <br /><div align="left" class="MsoNormal" style="text-align: left;"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">First, Koichi and his team combined two components on the Dynamic Life Plan Navigator: its parameter estimator and human response simulator. The parameter estimator minimized the burden of answering 300 questions down to only eight by applying machine learning and analytics technologies. Just by answering questions like marital status, gender, number of children, income level, date of birth, and home mortgage status, the system then analyzes other users’ answers to automatically fill in answers to the other 292 questions. According to the recent experiment, people who successfully went through life plan simulation questions nearly doubled.</span></div><div align="left" class="MsoNormal" style="text-align: left;"><br /></div><table align="right" border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse: collapse; border: none; margin-left: 6.75pt; margin-right: 6.75pt; mso-border-alt: solid white 1.5pt; mso-border-insideh-themecolor: background1; mso-border-insideh: 1.5pt solid white; mso-border-insidev-themecolor: background1; mso-border-insidev: 1.5pt solid white; mso-border-themecolor: background1; mso-padding-alt: 0in 5.4pt 0in 5.4pt; mso-table-anchor-horizontal: margin; mso-table-anchor-vertical: paragraph; mso-table-left: right; mso-table-lspace: 9.0pt; mso-table-overlap: never; mso-table-rspace: 9.0pt; mso-table-top: 2.85pt; mso-yfti-tbllook: 1184;"> <tbody><tr style="mso-yfti-firstrow: yes; mso-yfti-irow: 0; mso-yfti-lastrow: yes;">  <td style="border: solid white 1.5pt; mso-border-themecolor: background1; padding: 0in 5.4pt 0in 5.4pt; width: 2.25in;" valign="top" width="216"><div align="left" class="MsoNormal" style="mso-element-anchor-horizontal: margin; mso-element-anchor-vertical: paragraph; mso-element-frame-hspace: 9.0pt; mso-element-left: right; mso-element-top: 2.85pt; mso-element-wrap: around; mso-element: frame; mso-height-rule: exactly; text-align: left;"><b style="mso-bidi-font-weight: normal;"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">CREST   Grant</span></b></div><div align="left" class="MsoNormal" style="mso-element-anchor-horizontal: margin; mso-element-anchor-vertical: paragraph; mso-element-frame-hspace: 9.0pt; mso-element-left: right; mso-element-top: 2.85pt; mso-element-wrap: around; mso-element: frame; mso-height-rule: exactly; text-align: left;"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">The preference estimation uses   a technology that IBM Research -Tokyo has researched and developed with a grant   from the </span><a href="http://www.jst.go.jp/kisoken/crest/en/index.html"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Japan   Science and Technology Agency’s Core Research for Evolutionary Science and   Technology (CREST)</span></a><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">.</span></div></td> </tr></tbody></table><div align="left" class="MsoNormal" style="text-align: left;"><span style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Next, the human simulator can help discover a user’s potential preferences, such as insurance premium, coverage type, or what they consider important in choosing a product. The estimator creates several unconventional, two-option hypothetical questions based on the user’s data from the past. Then the simulator suggests an option. By repeating this process four to five times, the simulation system uncovers the user’s potential preferences, including the ones which the user has never imagined, such as job, partner and vacation destination preferences. </span></div></div><div align="left" class="MsoNormal" style="text-align: left;"><br /><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">More than 97 percent of the users who completed these hypothetical questions in the recent experiment did not feel it was a burden. And felt that they received an appropriate life insurance product recommendation.</span><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;"> </span></b><br /><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOBxDlT-2xxmtYYIPxVNnJhwRMQp52Gkr-wut4hmJLCEiFoO9fRKdd-X3lTpXI2WbQROz9ATqg3LenKsyOgU2oXvvdjWlzRULv5dWgtchSZ8LoqxWXe3Qlpwnv8AIM58p0X2ZZc3-E8hQH/s1600/lifesim_question.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiOBxDlT-2xxmtYYIPxVNnJhwRMQp52Gkr-wut4hmJLCEiFoO9fRKdd-X3lTpXI2WbQROz9ATqg3LenKsyOgU2oXvvdjWlzRULv5dWgtchSZ8LoqxWXe3Qlpwnv8AIM58p0X2ZZc3-E8hQH/s1600/lifesim_question.jpg" /></a></div><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Protecting Your Life (Insurance) Choices</span></b></div><div align="left" class="MsoNormal" style="text-align: left;"><br /></div><div align="left" class="MsoNormal" style="text-align: left;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Additionally, the research team created a bonus technology to help avoid posting inappropriate expressions and private information to online life insurance-related forums. In Japan, some users want to reference and reply to “people-like-me” comments in these forums that are often managed by volunteer life insurance agents and financial advisors. But to post a question can be a challenge because it can be difficult to determine what information to contribute, while not disclosing private information.</span></div><div align="left" class="MsoNormal" style="text-align: left;"><br /></div><div align="left" class="MsoNormal" style="text-align: left;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">The context-aware discussion forum technology the team built highlights potentially inappropriate expressions and private information to help users amend their posts before uploading them to a forum. Koichi and his team hope that it will also help prevent “flaming” (hostile interactions) in the forum due to improper comments. </span></div><div align="left" class="MsoNormal" style="text-align: left;"><br /></div><div align="left" class="MsoNormal" style="text-align: left;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Try, try again</span></b></div><div align="left" class="MsoNormal" style="text-align: left;"><br /></div><div align="left" class="MsoNormal" style="text-align: left;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Koichi used the tool again to do his life plan, and this time, it only took him five minutes to answer eight questions, overwrite some past answers, and receive a detailed life insurance plan and product based on future income and stock market performance. </span></div><div align="left" class="MsoNormal" style="text-align: left;"><br /></div><div align="left" class="MsoNormal" style="text-align: left;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">Koichi and his team are now working on applying the machine learning platform to other industries, such as banks and education. For example, like life insurance products, banks may consider using this platform to offer products to their customers that suit their savings goals. In education, such a platform may be applied to help teachers understand their students’ preferences and abilities by asking questions of their parents. If teachers can anticipate each student’s preference and ability based on an estimated value the platform’s analytics technology provides, they may be able to come up with a customized curriculum that effectively provides student support.<span style="mso-spacerun: yes;">  </span><span style="mso-spacerun: yes;">  </span></span></div><div align="left" class="MsoNormal" style="text-align: left;"><br /></div><div align="left" class="MsoNormal" style="text-align: left;"><span lang="EN-US" style="font-family: "helvneue light for ibm" , sans-serif; font-size: 13.0pt;">ST-Life continues to work with Koichi and his team to advance the technology. At IBM Japan, Koichi and his team plan to collaborate with IBM’s consulting unit to deploy the insurance information lifecycle management platform to the insurance industry. <span style="mso-spacerun: yes;"> </span></span></div>

Koichi Kamijo , a scientist at IBM’s research lab in Tokyo was only 20 questions into a 300-question life insurance form. And he was already...

Cognitive Computing for better decision making

8:38 AM N1000 0

<br /><div class="MsoNormal"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh89YTDRNCWQJfza_vO4O9o-lwhDCha11ztNN371hcjdyd8CQtERz3FCrB0dGmWCEHqSMhLXFnvlIksJN5gmE7YK3WMZySm4Fb0s7LfS6WNdkHJFerxf2UNQNUroIOU8zcF_EMhLmbdwOU/s1600/Lea.jpg" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEh89YTDRNCWQJfza_vO4O9o-lwhDCha11ztNN371hcjdyd8CQtERz3FCrB0dGmWCEHqSMhLXFnvlIksJN5gmE7YK3WMZySm4Fb0s7LfS6WNdkHJFerxf2UNQNUroIOU8zcF_EMhLmbdwOU/s1600/Lea.jpg" /></a><span style="font-family: Verdana,sans-serif;"><i><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Editor's note: This article is by </span></i><i style="mso-bidi-font-style: normal;"><span lang="EN-US" style="color: black; font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial; mso-fareast-language: EN-IE;"><a href="http://researcher.ibm.com/researcher/view.php?person=ie-lea.deleris">Léa<span style="mso-fareast-language: EN-US;"> Deleris</span></a></span></i><i><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">, manager of Risk Management at IBM Research-Ireland. Additional contributions made by <a href="http://researcher.ibm.com/researcher/view.php?person=ie-CHARLESJ">Charles Jochim</a>, research staff member at IBM Research-Ireland.</span></i></span></div><div class="MsoNormal"><span style="font-family: Verdana,sans-serif;"><i><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">  </span></i></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><br /><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Alice:</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;"> `Would you tell me, please, which way I ought to go from here?'</span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Cheshire Cat: `</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">That depends a good deal on where you want to get to.' </span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Alice:</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;"> `I don't much care where—‘</span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Cheshire Cat: `</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Then it doesn't matter which way you go.’</span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal" style="margin-bottom: .0001pt; margin-bottom: 0cm;"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Alice:</span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;"> ‘So long as I get somewhere.’</span></span></div><span style="font-family: Verdana,sans-serif;"> </span><br /><div class="MsoNormal"><span style="font-family: Verdana,sans-serif;"><b style="mso-bidi-font-weight: normal;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Cheshire Cat: </span></b><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">`Oh, you're sure to do that, if you only walk long enough.'</span></span><br /><a name="more"></a><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">This dilemma, and Stanford’s Decision Analysis Professor Dr. Rob Howard, who presented the <a href="https://www.cs.cmu.edu/~rgs/alice-VI.html">Alice in Wonderland</a>concept of indifference in a lecture, inspired me to study how mathematics can apply to the risk, uncertainty, and personal preferences that influence the decisions we make every day, about everything. What Alice and the Cheshire Cat so eloquently illustrate is that preferences are not as obvious as they may seem. I wanted to know, as a PhD student sitting among my fellow classmates, could natural language processing and cognitive computing be applied to web applications that could in turn, help us make more logical decisions?</span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Fast forward to today, and from student to IBM research scientist, and I’m applying artificial intelligence to our human intelligence – and how we can debate with machines to help us make good decisions when facing uncertainty. Here’s how I explained it to a <a href="https://www.youtube.com/watch?v=RGUvQdcMmZk">TEDxParis</a>audience in February.</span></span></div><div class="MsoNormal"><br /></div><iframe allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/RGUvQdcMmZk" width="560"></iframe><span style="font-family: Verdana,sans-serif;"></span><br /><div class="MsoNormal"><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Now my team in Dublin is using machines to help medical professionals make more rational decisions. The tool we developed, called <a href="http://www.research.ibm.com/labs/ireland/research_areas/information_extraction.html">MedicalRecap</a>, extracts information from </span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial;"><a href="http://www.ncbi.nlm.nih.gov/pubmed" target="_blank">PubMed</a>’s 24 million online </span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">citations to create a risk model for doctors.</span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial;">MedicalRecap’s semantic module allows doctors to cluster the extracted terms (variables of the risk model) by grouping similar or related terms into concepts. It also has an aggregation module, which allows the user to combine the extracted dependence and probability statements into a dependence graph, also known as a <a href="https://en.wikipedia.org/wiki/Bayesian_network">Bayesian network</a>.</span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;"> </span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">Imagine an instance of a doctor needing to understand the role of tea and coffee consumption on the incidence of endometrial cancer. Currently, doctors would address this task manually by searching for relevant papers, reading them, taking notes (by hand or copy-pasting on a spreadsheet), and aggregating this data.</span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">MedicalRecap, instead, presents extracted and aggregated data in an intuitive graphical format, providing a way for the user to trace back through the summarised information, to the original input. The tool also allows users to edit the output of the algorithms if they encounter an error, which is fed back into the system to improve its knowledge and performance over time.</span><span style="font-size: 12.0pt; mso-ansi-language: EN-IE; mso-bidi-font-family: Helv; mso-fareast-language: EN-IE;"> </span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span style="font-size: 12.0pt; mso-ansi-language: EN-IE; mso-bidi-font-family: Helv; mso-fareast-language: EN-IE;">MedicalRecap also relies on the doctor's expertise, so ideally, errors are reduced by combining the doctor's knowledge with the inferences the tool makes in finding dependency relationships. </span><span style="font-size: 12.0pt; mso-ansi-language: EN-IE; mso-bidi-font-family: "Tms Rmn"; mso-fareast-language: EN-IE;">The assumption is that the doctor does not have all the input required, but is exploring the space. The tool helps the practitioner look for answers, but does not provide them. Ideally, it will reach the same conclusions that the doctor already has made so that he or she will trust the system more.</span></span><br /><br /><iframe allowfullscreen="" frameborder="0" height="315" src="https://www.youtube.com/embed/fmfB4nU3j70" width="560"></iframe><br /><br /><span style="font-family: Verdana,sans-serif;"><span style="font-size: 12.0pt; mso-ansi-language: EN-IE; mso-bidi-font-family: "Tms Rmn"; mso-fareast-language: EN-IE;">We also want MedicalRecap to provide evidence for new conclusions to be drawn. For example, if the doctor sees that coffee consumption is linked to some cancers, which she already knew, the tool could show that in fact this is primarily for certain populations, which she didn't know.</span><span style="font-size: 12.0pt; line-height: 107%; mso-ansi-language: EN-IE; mso-bidi-font-family: "Tms Rmn"; mso-fareast-language: EN-IE;"><span style="mso-spacerun: yes;"> </span></span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span style="font-size: 12.0pt; line-height: 107%; mso-ansi-language: EN-IE; mso-bidi-font-family: "Tms Rmn"; mso-fareast-language: EN-IE;">As similar as it may sound, MedicalRecap is different to <a href="http://www.ibm.com/smarterplanet/us/en/ibmwatson/health/">IBM Watson Health</a>. Our tool is a web-based GUI focused only on published medical literature and is not designed for personalized medicine, but instead to make more global inferences between diseases and related risk factors. </span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">But like Watson, MedicalRecap’s Extractor, Clusterer, and Aggregator services are</span><span style="color: black; font-size: 12.0pt; line-height: 107%; mso-ansi-language: EN-IE; mso-bidi-font-family: Calibri; mso-fareast-language: EN-IE;"> available on IBM’s <a href="http://www.softlayer.com/">SoftLayer</a> cloud infrastructure as a service.</span></span><br /><br /><span style="font-family: Verdana,sans-serif;"><span style="color: black; font-size: 12.0pt; line-height: 107%; mso-ansi-language: EN-IE; mso-bidi-font-family: Calibri; mso-fareast-language: EN-IE;">Our risk information extraction models, like in MedicalRecap, can be applied to other domains. </span><span lang="EN-US" style="font-size: 12.0pt; line-height: 107%;">In the future, oil and gas experts could use the tool to extract information from academic papers about factors influencing reservoir capacity and shape.</span><span lang="EN-US" style="color: black; font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial; mso-fareast-language: EN-IE;"> As long as we have the main ingredient of a large body of literature related to a profession or domain, our decision support system tool might even be able to offer Alice somewhere to go, no matter how unsure she is.</span></span><b><span style="font-family: Verdana,sans-serif;"><span lang="EN-US" style="color: black; font-size: 12.0pt; line-height: 107%; mso-bidi-font-family: Arial; mso-fareast-language: EN-IE;">  </span></span></b></div><br /><div class="MsoNormal"><br /></div>

Cognitive Computing for better decision making

analytics cognitive computing IBM Watson MedicalRecap natural language processing semantic reasoning Cognitive Computing for better decision making

Editor's note: This article is by Léa Deleris , manager of Risk Management at IBM Research-Ireland. Additional contributions made by ...

Multilingual Watson

6:00 AM N1000 0

<div class="MsoNormal"><i><span style="font-family: Verdana; font-size: 13.0pt;">Learning to understand the human gift of language</span></i></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><table cellpadding="0" cellspacing="0" class="tr-caption-container" style="float: left; margin-right: 1em; text-align: left;"><tbody><tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjwPJEcM8zot_m36dtHKOINTebdn1FYdf5PSmrtjlrUESser5iouyFFOUYeN_xGsguMMidzT_r1tpOOg45ZZlQRzsb-qGzjA5w1rsgn-qo_KbsMwQiWjin3dlv8M6NL4uik57OSF0aIBIf7/s1600/dj_mccloskey_blog.jpg" imageanchor="1" style="clear: left; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjwPJEcM8zot_m36dtHKOINTebdn1FYdf5PSmrtjlrUESser5iouyFFOUYeN_xGsguMMidzT_r1tpOOg45ZZlQRzsb-qGzjA5w1rsgn-qo_KbsMwQiWjin3dlv8M6NL4uik57OSF0aIBIf7/s1600/dj_mccloskey_blog.jpg" /></a></td></tr><tr><td class="tr-caption" style="text-align: center;">DJ McCloskey, IBM Watson Group</td></tr></tbody></table><span style="font-family: Verdana; font-size: 13.0pt;">Machines use programming languages to at least appear to understand our human languages. IBM Watson is one of the most sophisticated, helping everyone from healthcare providers to sous chefs by using several programming languages and algorithms to read and comprehend natural language. But the system could only answer questions posed in English – until now.</span></div><div class="MsoNormal"><br /></div><div class="MsoNormal" style="tab-stops: 45.8pt 91.6pt 137.4pt 183.2pt 229.0pt 274.8pt 320.6pt 366.4pt 412.2pt 458.0pt 503.8pt 549.6pt 595.4pt 641.2pt 687.0pt 732.8pt;"><span style="font-family: Verdana; font-size: 13.0pt;">Natural Language Processing architect D.J. McCloskey leads a team “teaching” Watson the fundamental mechanisms to comprender español, entender português, </span><span lang="JA" style="font-family: "MS Mincho"; font-size: 13.0pt; mso-ascii-font-family: Verdana; mso-bidi-font-family: "Courier New"; mso-hansi-font-family: "Courier New";">日本語を理解する</span><span style="font-family: Verdana; font-size: 13.0pt;">(understand Japanese), and many other languages.</span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">“Back in the late 1990s and early 2000s, the notion of a machine reading text was primarily defined by creating search indexes out of the words in text. We wanted to take it one step farther where ‘reading’ actually meant ‘understanding’ the text. So, we created <a href="http://www-01.ibm.com/software/ebusiness/jstart/textanalytics/infocenter.html">LanguageWare</a>in 2001, a technology that could automate fact extraction from the text,” D.J. said. </span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">LanguageWare established a lightweight, optimized library of functions for processing natural language text, using a set of generalized structures and algorithms that captured the essence of language. Multilingual by design, this foundation gave LanguageWare a way to process text from any language so that a machine could understand the atomic sentence context, and build semantic understanding of that sentence in any language.</span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">But D.J.’s team developed this sophisticated tooling with the mantra of “involve the humans” in mind. By letting humans teach the machines everything about language – from <a href="http://en.wikipedia.org/wiki/Morphology_%28linguistics%29">word morphology</a>, to knowing the difference between “run” and “running” and “goose” and “geese,” and transcribing the knowledge of domain experts (learning from a subject’s human masters) – the system can then detect accurately worded facts in text, such as negative reactions to a drug, or an acquisition of one company by another. Today, Watson’s entire suite of cognitive capabilities uses and extends this tooling.</span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">“And in Watson we have employed this capability to capture and apply precise knowledge from oncology experts, providing a way for human experts to teach the system at a deep level,” D.J. said.</span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><b><span style="font-family: Verdana; font-size: 13.0pt;">Gluing it together with open architecture</span></b></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">These analytics and algorithms work together on top of Apache’s Unstructured Information Management Architecture, or <a href="http://ibmresearchnews.blogspot.com/2011/04/open-architecture-helps-watson.html">UIMA</a>(“you-ee-mah”). Its open architecture gave LanguageWare back in 2001, and Watson today, a way to combine their analytics with other complementary analytics to rapidly collaborate and prototype new ideas – a way to end up with a whole much greater than the sum of its parts, like the ideas from the <a href="http://www.ibm.com/smarterplanet/us/en/ibmwatson/challenge.html">Watson Mobile Challenge</a>. </span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">“I remember trying to convince people of the viability of machines understanding unstructured data, pre-Watson,” said D.J. “And then Watson (and UIMA) happened, and now people believe it can cure cancer, and make our tea! </span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">“Amazingly enough, the power of this technology actually has potential to help do both – and more. Watson can’t cure cancer but we have real solutions where Watson Oncology Advisor helps consultant oncologists improve treatment of cancer patients. And a member of our team recently made Chef Watson’s Korean BBQ lemon cupcakes and they were awesome (with my tea)!” </span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><b><span style="font-family: Verdana; font-size: 13.0pt;">Parsing languages (other than English)</span></b></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">Another ingredient in Watson’s NLP pantry is its parser. This set of code helps it analyze and understand the written English language down to the grammar and syntax level. For example, Watson’s parser lets the system know “who did what to whom,” as in “the boy kicked the ball.” So, a question about what was kicked will find “the ball” as the receiver of said action. </span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">But not all sentences operate the same way or in the same order. </span></div><div class="MsoNormal"><br /></div><div align="right"><table align="right" border="1" cellpadding="0" cellspacing="0" class="MsoTableGrid" style="border-collapse: collapse; border: none; margin-left: 6.75pt; margin-right: 6.75pt; mso-border-alt: solid white .5pt; mso-border-insideh: .5pt solid white; mso-border-insidev: .5pt solid white; mso-padding-alt: 0in 5.4pt 0in 5.4pt; mso-table-anchor-horizontal: column; mso-table-anchor-vertical: paragraph; mso-table-left: right; mso-table-lspace: 9.0pt; mso-table-overlap: never; mso-table-rspace: 9.0pt; mso-table-top: .05pt; mso-yfti-tbllook: 480;"> <tbody><tr style="mso-yfti-firstrow: yes; mso-yfti-irow: 0; mso-yfti-lastrow: yes;">  <td style="border: solid white 1.0pt; mso-border-alt: solid white .5pt; padding: 0in 5.4pt 0in 5.4pt; width: 2.0in;" valign="top" width="192"><div class="MsoNormal" style="mso-element-anchor-horizontal: column; mso-element-anchor-vertical: paragraph; mso-element-frame-hspace: 9.0pt; mso-element-left: right; mso-element-top: .05pt; mso-element-wrap: around; mso-element: frame; mso-height-rule: exactly;"><i><span style="color: #999999; font-family: Verdana; font-size: 13.0pt;">Say “Hola” to   Watson, and find out more about its new capabilities, and its new home at Astor Place in New York City, <a href="http://www.ibm.com/smarterplanet/us/en/ibmwatson/">here</a>.</span></i></div></td> </tr></tbody></table></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">In English, the subjects, verbs, and objects follow a certain order: “John saw Mary.” John did the seeing, while Mary was seen in a subject-verb-object order. However, in Hindi it is “Jŏna mairī dēkhā,” or “John Mary saw,” so a subject-object-verb order. And in Ireland, where D.J. lives and works, verbs follow subjects, which follow objects for “Chonaic John Máire” which is “Saw John Mary.” </span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">D.J.’s team chose Spanish first, a widely spoken representative of a romance language, as Watson’s next language to parse, but hopes to build a generic parser that, once plugged into UIMA, will allow Watson to understand any language. </span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">“We are after the mechanics of language to get to a point where Watson works between languages in a pragmatic way, Watson going global!” D.J. said.</span></div><div class="MsoNormal"><br /></div><div class="MsoNormal"><span style="font-family: Verdana; font-size: 13.0pt;">Now, with Watson’s capabilities on BlueMix available to developers all around the world, its ability to process local language just as well as English will be increasingly valuable. New mobile apps could exploit all of Watson’s natural language power on regionally relevant knowledge sources. Ultimately, Watson will be cross lingual, meaning questions in one language can find answers in another and be returned to the user, translated back into his or her native or preferred language – making the knowledge of to world available to all regardless of language.</span><br /><br /><b><span style="font-family: Verdana; font-size: 13.0pt;">More about IBM Watson</span></b><br /><ul><li><span style="font-family: Verdana; font-size: 13.0pt;"><a href="http://www.youtube.com/watch?v=PouxN8btS2A">Watson at Astor Place</a> </span></li><li><a href="http://asmarterplanet.com/blog/2014/10/ibms-watson-grows.html"><span style="font-family: Verdana; font-size: 13.0pt;">IBM’s Watson Grows Up</span></a></li></ul></div>

ibmwatson natural language processing uima Multilingual Watson

Learning to understand the human gift of language DJ McCloskey, IBM Watson Group Machines use programming languages to at least appear to un...