BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20220520T013443Z UID:88F474DA-8238-4ACE-8603-DA5C91D003CA DTSTART;TZID=America/New_York:20220519T200000 DTEND;TZID=America/New_York:20220519T213000 DESCRIPTION:Traditionally\, the field of AI and Law has focused on represen ting legal knowledge in ways that computers can use to perform legal reaso ning\, or something like it\, with legally intelligible results. Today\, t he research paradigm in AI and Law has largely shifted to applying new mac hine learning (ML) and natural language processing techniques to legal tex ts. These text analytic techniques use statistical means to extract semant ic information from archives of legal case decisions\, contracts\, or stat utes. Although for some time ML models have been predicting outcomes of ca ses directly from textual descriptions of the facts\, they cannot yet expl ain their predictions or support them with arguments.\n\nThis talk will br iefly explain some basic mechanisms of text analytics and their applicatio n in legal practice and describe some recent research efforts to tease add itional elements of meaning from legal text\, including concepts and argum ent structures. These methods can improve legal information retrieval\, ma y eventually enable ML models to explain and justify their results\, and c ontribute new methods in empirical legal research.\n\nSpeaker(s): Kevin D. Ashley\, \n\nVirtual: https://events.vtools.ieee.org/m/308473 LOCATION:Virtual: https://events.vtools.ieee.org/m/308473 ORGANIZER:dmancl@acm.org SEQUENCE:2 SUMMARY:Legal Text Analytics and its Role in AI and Law URL;VALUE=URI:https://events.vtools.ieee.org/m/308473 X-ALT-DESC:Description: <br /><p>Traditionally\, the field of AI and Law ha s focused on representing legal knowledge in ways that computers can use t o perform legal reasoning\, or something like it\, with legally intelligib le results. Today\, the research paradigm in AI and Law has largely shifte d to applying new machine learning (ML) and natural language processing te chniques to legal texts. These text analytic techniques use statistical me ans to extract semantic information from archives of legal case decisions\ , contracts\, or statutes. Although for some time ML models have been pred icting outcomes of cases directly from textual descriptions of the facts\, they cannot yet explain their predictions or support them with arguments. </p>\n<p>This talk will briefly explain some basic mechanisms of text anal ytics and their application in legal practice and describe some recent res earch efforts to tease additional elements of meaning from legal text\, in cluding concepts and argument structures. These methods can improve legal information retrieval\, may eventually enable ML models to explain and jus tify their results\, and contribute new methods in empirical legal researc h.</p> END:VEVENT END:VCALENDAR