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.icon-hamburger-1 svg.icon-hamburger { display: block; } .icon-hamburger-1 .fill-color.icon-hamburger { fill: #fff; }.login-button-1 .dropdown-toggle.login-button { display: block; height: 4rem; font-size: 1.6rem; text-transform: uppercase; text-decoration: none; color: #ccc; cursor: pointer; } .login-button-1 .dropdown-toggle.login-button .fill-color.login-button { fill: #999; } .login-button-1 .dropdown-toggle.login-button:active .fill-color.login-button,.login-button-1 .dropdown-toggle.login-button:focus .fill-color.login-button,.login-button-1 .dropdown-toggle.login-button:hover .fill-color.login-button { fill: #fff; } .login-button-1 .active.login-button { border-radius: 1rem 1rem 0 0; background: #333; } .login-button-1 .active.login-button .fill-color.login-button { fill: #fff; } .login-button-1 span.login-button { display: none; font-size: 1.4rem; text-transform: uppercase; color: #999; } .login-button-1 span.login-button a.login-button { color: inherit; text-decoration: none; } .login-button-1 a.login-button:hover,.login-button-1 a.login-button:active,.login-button-1 a.login-button:focus { color: #fff; } @media (min-width: 890px) { .login-button-1 .logged-out-toolbar.login-button { transform: translateY(-.5rem); } .login-button-1 .active.login-button { background: transparent; } .login-button-1 .dropdown-toggle.login-button { display: inline-block; vertical-align: middle; } .login-button-1 span.login-button { display: inline; vertical-align: middle; } }.nav-search-1 input.nav-search:focus { outline: none; } .nav-search-1 button.nav-search { background: none; color: inherit; border: none; font: inherit; cursor: pointer; } .nav-search-1 button.nav-search:focus { outline: none; } .nav-search-1 .search.nav-search { padding-top: 0; margin-right: .5rem; } .nav-search-1 .search.nav-search svg.nav-search { position: relative; top: -5px; right: -3px; fill:; } .nav-search-1 .search-activated.nav-search { display: -webkit-box; display: -ms-flexbox; display: flex; position: absolute; top: 0; right: 5rem; bottom: 0; left: 5rem; z-index: 3; padding: 0.5rem 0.2rem; border-radius: 1rem 1rem 0 0; background: #333; } .nav-search-1 .search-inactive.nav-search { display: none; } .nav-search-1 .search-activated.nav-search .highlight.nav-search,.nav-search-1 .search-activated.nav-search .search.nav-search { background: #fff; border-radius: 0.5rem; } .nav-search-1 .search-activated.nav-search .highlight.nav-search { display: -webkit-box; display: -ms-flexbox; display: flex; width: 100%; margin: 0 .5rem; } .nav-search-1 .search-activated.nav-search .search.nav-search { height: 100%; padding: 0; margin-right: 0; -ms-flex-item-align: center; -ms-grid-row-align: center; align-self: center; } .nav-search-1 .search-activated.nav-search .search-field.nav-search { width: 100%; height: 100%; box-sizing: border-box; padding-left: 1rem; border-radius: 0.5rem; border: none; font-size: 1.6rem; text-align: center; } .nav-search-1 .search-activated.nav-search .search-field.nav-search:focus { outline: none; } @keyframes fade-in-nav-search-1 { 0% { opacity: 0; } 100% { opacity: 1; } } .nav-search-1 .fade-in.nav-search { animation: fade-in-nav-search-1 .2s forwards; } @media (min-width: 890px) { .nav-search-1 .search.nav-search svg.nav-search { display: inline; width: 28px; height: 28px; vertical-align: -14px; } .nav-search-1 .search.nav-search path.nav-search { fill: #333; } .nav-search-1 .search-inactive.nav-search,.nav-search-1 .search-activated.nav-search { display: block; position: static; padding: 1.2rem .2rem; background: transparent; } .nav-search-1 .search-activated.nav-search .highlight.nav-search { width: 13rem; height: 2.8rem; -webkit-box-orient: horizontal; -webkit-box-direction: reverse; -ms-flex-direction: row-reverse; flex-direction: row-reverse; } .nav-search-1 .search-activated.nav-search .search-field.nav-search { width: calc(100% - 28px); height: 100%; padding-left: 0; font-size: 1.4rem; text-align: left; } .nav-search-1 .search-activated.nav-search .search.nav-search svg.nav-search { width: 28px; height: 28px; } }.media-button-1 a.media-button { display: inline-block; text-decoration: none; } .media-button-1 .menu-item.media-button { display: inline-flex; width: 100%; padding: 0; font-size: 1.6rem; text-align: left; background: transparent; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } .media-button-1 .menu-item.media-button:focus { outline: none; } .media-button-1 .label.media-button { display: inline-block; padding: 0; font-weight: 400; color: #fff; text-align: left; vertical-align: middle; } .media-button-1 .menu-item.media-button > .icon.media-button { display: inline-flex; width: 42px; height: 42px; vertical-align: middle; -webkit-box-align: center; -ms-flex-align: center; align-items: center; -webkit-box-pack: center; -ms-flex-pack: center; justify-content: center; } .media-button-1 .menu-item.selected.media-button .icon.media-button { background-color: #333; border-radius: 1rem 0 0 1rem; } .media-button-1 .icon.media-button .fill-color.media-button { fill: #999; } .media-button-1 .icon.active.media-button .fill-color.media-button { fill: #fff; } .media-button-1 .donate.media-button .fill-color.media-button { fill: #f00; } @media (min-width: 890px) { .media-button-1 .menu-item.media-button { width: auto; height: 5rem; color: #999; } .media-button-1 .menu-item.media-button:hover,.media-button-1 .menu-item.media-button:active,.media-button-1 .menu-item.media-button:focus { color: #fff; } .media-button-1 .menu-item.media-button:hover .fill-color.media-button,.media-button-1 .menu-item.media-button:active .fill-color.media-button,.media-button-1 .menu-item.media-button:focus .fill-color.media-button { fill: #fff; } .media-button-1 .label.media-button { display: none; } .media-button-1 .label.media-button,.media-button-1 .web.media-button:after { padding-right: 1rem; font-size: 1.3rem; text-transform: uppercase; color: inherit; } .media-button-1 .web.media-button:after { display: none; content: "web"; } .media-button-1 .donate.media-button,.media-button-1 .more.media-button { display: none; } .media-button-1 .menu-item.selected.media-button { background: #474747; } .media-button-1 .menu-item.selected.media-button .label.media-button,.media-button-1 .menu-item.selected.web.media-button:after { color: #fff; } .media-button-1 .menu-item.selected.media-button .icon.media-button { background: transparent; } .media-button-1 .web.selected.media-button .fill-color.media-button { fill: #ffcd27; } .media-button-1 .texts.selected.media-button .fill-color.media-button { fill: #faab3c; } .media-button-1 .video.selected.media-button .fill-color.media-button { fill: #f1644b; } .media-button-1 .audio.selected.media-button .fill-color.media-button { fill: #00adef; } .media-button-1 .software.selected.media-button .fill-color.media-button { fill: #9ecc4f; } .media-button-1 .images.selected.media-button .fill-color.media-button { fill: #aa99c9; } } @media (min-width: 1300px) { .media-button-1 .label.media-button,.media-button-1 .web.media-button:after { display: inline; } .media-button-1 .web.media-button .label.media-button { display: none; } }.media-menu-1 { outline: none; } .media-menu-1 .media-menu.media-menu { position: absolute; z-index: -1; top: -100vh; width: 100%; background-color: #222; margin: 0; overflow: hidden; } .media-menu-1 .media-menu.tx-slide.media-menu { transition-property: top; transition-duration: 0.2s; transition-timing-function: ease; } .media-menu-1 .media-menu.tx-slide.open.media-menu { top: 100%; } .media-menu-1 .media-menu.tx-slide.closed.media-menu { top: -100vh; } .media-menu-1 .media-menu.tx-slide.closed.media-menu { transition-duration: 0.2s; } .media-menu-1 .menu-group.media-menu { position: relative; line-height: normal; } @media (min-width: 890px) { .media-menu-1 .media-menu.media-menu { display: inline-block; position: static; width: auto; height: 5rem; } .media-menu-1 .media-menu.tx-slide.media-menu { transition-property: none; } .media-menu-1 .media-menu.tx-slide.open.media-menu,.media-menu-1 .media-menu.tx-slide.closed.media-menu { top: 0; } .media-menu-1 .menu-group.media-menu { font-size: 0; } }.primary-nav-1 button.primary-nav:focus,.primary-nav-1 a.primary-nav:focus,.primary-nav-1 input.primary-nav:focus { outline: none; } .primary-nav-1 nav.primary-nav { position: relative; display: -ms-grid; display: grid; height: 4rem; grid-template-areas: "hamburger empty search user"; -ms-grid-columns: 4rem minmax(1rem, 100%) 4.3rem 5rem; grid-template-columns: 4rem auto 4.3rem 5rem; -ms-grid-rows: 100%; grid-template-rows: 100%; background: #222; border-bottom: 1px solid #333; } .primary-nav-1 button.primary-nav { background: none; color: inherit; border: none; font: inherit; cursor: pointer; } .primary-nav-1 .link-home.primary-nav { position: absolute; top: 50%; left: 50%; z-index: 2; text-decoration: none; -webkit-transform: translate(-50%, -50%); -ms-transform: translate(-50%, -50%); transform: translate(-50%, -50%); } .primary-nav-1 media-menu.primary-nav { grid-column-start: hamburger-start; grid-column-end: user-end; } .primary-nav-1 .ia-logo.primary-nav { display: block; } .primary-nav-1 .ia-wordmark.primary-nav { display: none; } .primary-nav-1 .hamburger.primary-nav { -ms-grid-row: 1; -ms-grid-column: 1; grid-area: hamburger; padding: 0; } .primary-nav-1 .search-trigger.primary-nav { -ms-grid-row: 1; -ms-grid-column: 3; grid-area: search; position: relative; padding: 0; z-index: 1; width: 100%; text-align: right; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } .primary-nav-1 .search-trigger.primary-nav .fill-color.primary-nav { fill: #999; } .primary-nav-1 .search-activated.primary-nav { position: relative; z-index: 3; } .primary-nav-1 .upload.primary-nav { display: none; } .primary-nav-1 .upload.primary-nav span.primary-nav { display: none; } .primary-nav-1 .user-info.primary-nav { -ms-grid-row: 1; -ms-grid-column: 4; grid-area: user; -ms-grid-row-align: stretch; align-self: stretch; -ms-grid-column-align: end; justify-self: end; } .primary-nav-1 .username.primary-nav { display: none; font-size: 1.3rem; vertical-align: middle; text-transform: uppercase; } .primary-nav-1 .user-menu.primary-nav { height: 100%; padding: .5rem 1rem; color: #999; } .primary-nav-1 .user-menu.primary-nav:hover { color: #fff; } .primary-nav-1 .user-menu.active.primary-nav { border-radius: 1rem 1rem 0 0; background: #333; } .primary-nav-1 .user-menu.primary-nav img.primary-nav { display: block; width: 30px; height: 30px; } @media (min-width: 890px) { .primary-nav-1 { ; } .primary-nav-1 nav.primary-nav { display: block; z-index: 2; height: 5rem; padding-right: 1.5rem; } .primary-nav-1 .link-home.primary-nav { position: static; float: left; margin-top: 1rem; padding: 0 10px 0 13px; -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); transform: translate(0, 0); } .primary-nav-1 .ia-logo.primary-nav,.primary-nav-1 .ia-wordmark.primary-nav { display: inline-block; vertical-align: middle; } .primary-nav-1 .ia-wordmark.primary-nav { margin-left: 1rem; } .primary-nav-1 .hamburger.primary-nav,.primary-nav-1 .search-trigger.primary-nav { display: none; } .primary-nav-1 .user-info.primary-nav { float: right; padding-top: 1rem; } .primary-nav-1 .user-menu.primary-nav { padding-top: 0; } .primary-nav-1 .user-menu.active.primary-nav { background: transparent; } .primary-nav-1 .user-menu.primary-nav img.primary-nav { display: inline-block; vertical-align: middle; } .primary-nav-1 .upload.primary-nav { display: block; float: right; margin-top: 1rem; font-size: 1.4rem; text-transform: uppercase; text-decoration: none; color: #999; } .primary-nav-1 .upload.primary-nav:active,.primary-nav-1 .upload.primary-nav:focus,.primary-nav-1 .upload.primary-nav:hover { color: #fff; } .primary-nav-1 .upload.primary-nav svg.primary-nav { width: 32px; height: 32px; vertical-align: middle; fill: #999; } .primary-nav-1 .upload.primary-nav:hover svg.primary-nav,.primary-nav-1 .upload.primary-nav:focus svg.primary-nav,.primary-nav-1 .upload.primary-nav:active svg.primary-nav { fill: #fff; } .primary-nav-1 nav-search.primary-nav { float: right; margin-left: 1rem; } .primary-nav-1 login-button.primary-nav { display: block; margin-right: 1rem; } } @media (min-width: 990px) { .primary-nav-1 .username.primary-nav { display: inline-block; } .primary-nav-1 .upload.primary-nav span.primary-nav { display: inline; } }.search-menu-1 { ; } .search-menu-1 button.search-menu:focus,.search-menu-1 input.search-menu:focus { outline-color: #428bca; outline-width: 0.16rem; outline-style: auto; } .search-menu-1 .search-menu.search-menu { position: absolute; top: -800px; right: 0; left: 0; z-index: 1; padding: 0 4.5rem; font-size: 1.6rem; background-color: #333; } .search-menu-1 .tx-slide.search-menu { overflow: hidden; transition-property: top; transition-duration: 0.2s; transition-timing-function: ease; } .search-menu-1 .initial.search-menu,.search-menu-1 .closed.search-menu { top: -800px; } .search-menu-1 .closed.search-menu { transition-duration: 0.2s; } .search-menu-1 .open.search-menu { top: 4rem; } .search-menu-1 label.search-menu,.search-menu-1 a.search-menu { padding: 1rem; display: block; } .search-menu-1 .advanced-search.search-menu { text-decoration: none; color: #428bca; } @media (min-width: 890px) { .search-menu-1 .search-menu.search-menu { overflow: visible; top: -400px; right: 2rem; left: auto; z-index: 5; padding: 1rem 2rem; transition: opacity .2s ease-in-out; font-size: 1.4rem; color: #333; border-radius: 2px; background: #fff; box-shadow: 0 1px 2px 1px rgba(0, 0, 0, .15); } .search-menu-1 .search-menu.search-menu:after { position: absolute; right: 7px; top: -7px; width: 12px; height: 7px; box-sizing: border-box; color: #fff; content: ""; border-bottom: 7px solid currentColor; border-left: 6px solid transparent; border-right: 6px solid transparent; } .search-menu-1 .initial.search-menu,.search-menu-1 .closed.search-menu { opacity: 0; transition-duration: .2s; } .search-menu-1 .open.search-menu { top: 5.1rem; opacity: 1; } .search-menu-1 label.search-menu { padding: 0; } .search-menu-1 label.search-menu + label.search-menu { padding-top: 7px; } .search-menu-1 a.search-menu { padding: 1rem 0 0 0; } }.media-subnav-1 a.media-subnav { text-decoration: none; color: #fff; } .media-subnav-1 img.media-subnav { display: block; width: 90px; height: 90px; margin: 0 auto 1rem auto; border-radius: 45px; } .media-subnav-1 h3.media-subnav { margin-top: 0; font-size: 1.8rem; } .media-subnav-1 h4.media-subnav { font-size: 1.6rem; } .media-subnav-1 ul.media-subnav { padding: 0; margin: 0; list-style: none; } .media-subnav-1 li.media-subnav + li.media-subnav { padding-top: 1.5rem; } .media-subnav-1 .icon-links.media-subnav { display: -webkit-box; display: -ms-flexbox; display: flex; -webkit-box-pack: space-evenly; -ms-flex-pack: space-evenly; justify-content: space-evenly; text-align: center; } .media-subnav-1 .icon-links.media-subnav a.media-subnav { display: inline-block; width: 120px; margin-bottom: 1.5rem; overflow: hidden; white-space: nowrap; text-align: center; text-overflow: ellipsis; } .media-subnav-1 .icon-links.media-subnav a.media-subnav + a.media-subnav { margin-left: 2rem; } .media-subnav-1 .featured.media-subnav h4.media-subnav { display: none; } @media (min-width: 890px) { .media-subnav-1 { display: -ms-grid; display: grid; -ms-grid-columns: 40% 20% 40%; grid-template-columns: 40% 20% 40%; } .media-subnav-1 .wayback-search.media-subnav { -ms-grid-column: 1; -ms-grid-column-span: 3; grid-column: 1 / 4; } .media-subnav-1 h3.media-subnav { display: none; } .media-subnav-1 h4.media-subnav { margin: 0 0 1rem 0; font-weight: 100; } .media-subnav-1 ul.media-subnav { font-size: 1.3rem; } .media-subnav-1 li.media-subnav { padding-bottom: .5rem; } .media-subnav-1 li.media-subnav + li.media-subnav { padding-top: 0; } .media-subnav-1 li.media-subnav a.media-subnav { display: block; overflow: hidden; white-space: nowrap; text-overflow: ellipsis; } .media-subnav-1 .icon-links.media-subnav { -ms-grid-column: 1; } .media-subnav-1 .icon-links.media-subnav a.media-subnav { padding-top: 3.5rem; max-width: 160px; } .media-subnav-1 .links.media-subnav { padding: 0 1.5rem; } .media-subnav-1 .featured.media-subnav { -ms-grid-column: 2; } .media-subnav-1 .featured.media-subnav h4.media-subnav { display: block; } .media-subnav-1 .top.media-subnav { -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav { display: -ms-grid; display: grid; -ms-grid-columns: 50% 3rem 50%; grid-template-columns: 50% 50%; -ms-grid-rows: (auto)[7]; grid-template-rows: repeat(7, auto); grid-column-gap: 3rem; grid-auto-flow: column; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(1) { -ms-grid-row: 1; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(2) { -ms-grid-row: 2; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(3) { -ms-grid-row: 3; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(4) { -ms-grid-row: 4; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(5) { -ms-grid-row: 5; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(6) { -ms-grid-row: 6; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(7) { -ms-grid-row: 7; -ms-grid-column: 1; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(8) { -ms-grid-row: 1; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(9) { -ms-grid-row: 2; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(10) { -ms-grid-row: 3; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(11) { -ms-grid-row: 4; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(12) { -ms-grid-row: 5; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(13) { -ms-grid-row: 6; -ms-grid-column: 3; } .media-subnav-1 .top.media-subnav ul.media-subnav > *.media-subnav:nth-child(14) { -ms-grid-row: 7; -ms-grid-column: 3; } }.media-slider-1 .overflow-clip.media-slider { display: none; position: absolute; top: 4rem; right: 0; left: 4rem; height: 368px; overflow-x: hidden; } .media-slider-1 .information-menu.media-slider { position: absolute; top: 0; right: 0; left: 0; padding: 0; height: 368px; overflow-x: hidden; font-size: 1.4rem; background: #333; } .media-slider-1 .open.media-slider { display: block; } .media-slider-1 .info-box.media-slider { padding: 1rem; } @media (min-width: 890px) { .media-slider-1 .overflow-clip.media-slider { display: block; top: 0; left: 0; height: auto; overflow-x: visible; transform: translate(0, -100%); transition: transform .2s ease; } .media-slider-1 .information-menu.media-slider { left: 0; z-index: 1; height: auto; min-height: 21rem; background: #474747; transform: translate(0, -100%); transition: transform .2s ease; } .media-slider-1 .overflow-clip.open.media-slider { transform: translate(0, 8rem); } .media-slider-1 .information-menu.open.media-slider { transform: translate(0, 0); } .media-slider-1 .info-box.media-slider { max-width: 1000px; padding: 1.5rem 0; margin: 0 auto; } }.desktop-subnav-1 ul.desktop-subnav { position: relative; z-index: 3; padding: .8rem 0; margin: 0; font-size: 1.2rem; text-transform: uppercase; text-align: center; background: #333; } .desktop-subnav-1 li.desktop-subnav { display: inline-block; padding: 0 15px; } .desktop-subnav-1 a.desktop-subnav { text-decoration: none; color: #aaa; } .desktop-subnav-1 a.desktop-subnav:hover,.desktop-subnav-1 a.desktop-subnav:active,.desktop-subnav-1 a.desktop-subnav:focus { color: #fff; } .desktop-subnav-1 .donate.desktop-subnav svg.desktop-subnav { width: 16px; height: 16px; vertical-align: -4px; fill: #f00; }.signed-out-dropdown-1 { ; } .signed-out-dropdown-1 nav.signed-out-dropdown { position: absolute; top: -1500px; right: 0; z-index: 1; overflow: hidden; font-size: 1.6rem; background-color: #333; transition-property: top; transition-duration: 0.2s; transition-timing-function: ease; } .signed-out-dropdown-1 .initial.signed-out-dropdown,.signed-out-dropdown-1 .closed.signed-out-dropdown { top: -1500px; } .signed-out-dropdown-1 .closed.signed-out-dropdown { transition-duration: 0.5s; } .signed-out-dropdown-1 .open.signed-out-dropdown { top: 4rem; max-width: 100vw; overflow: auto; } .signed-out-dropdown-1 h3.signed-out-dropdown { padding: 0.6rem 2rem; margin: 0; font-size: inherit; overflow: hidden; text-overflow: ellipsis; } .signed-out-dropdown-1 ul.signed-out-dropdown { padding: 0.4rem 0 0.7rem 0; margin: 0; list-style: none; max-height: calc(100vh - 7.2rem + 1px); overflow: auto; box-sizing: border-box; } .signed-out-dropdown-1 .divider.signed-out-dropdown { margin: .5rem 0; border-bottom: 1px solid #666; } .signed-out-dropdown-1 a.signed-out-dropdown,.signed-out-dropdown-1 .info-item.signed-out-dropdown { display: block; color: #fff; text-decoration: none; padding: 1rem 2rem; } .signed-out-dropdown-1 .info-item.signed-out-dropdown { font-size: .8em; color: #999; } @media (min-width: 890px) { .signed-out-dropdown-1 nav.signed-out-dropdown { overflow: visible; top: calc(100% + 7px); left: auto; z-index: 5; transition: opacity .2s ease-in-out; font-size: 1.4rem; border-radius: 2px; background: #fff; box-shadow: 0 1px 2px 1px rgba(0, 0, 0, .15); } .signed-out-dropdown-1 nav.signed-out-dropdown:after { position: absolute; right: 7px; top: -7px; width: 12px; height: 7px; box-sizing: border-box; color: #fff; content: ""; border-bottom: 7px solid currentColor; border-left: 6px solid transparent; border-right: 6px solid transparent; } .signed-out-dropdown-1 h3.signed-out-dropdown { display: none; } .signed-out-dropdown-1 ul.signed-out-dropdown { max-height: calc(100vh - 8.5rem + 1px); } .signed-out-dropdown-1 .divider.signed-out-dropdown { border-bottom-color: #666; } .signed-out-dropdown-1 a.signed-out-dropdown { padding: .5rem 2rem; color: #333; transition: background .1s ease-out, color .1s ease-out; } .signed-out-dropdown-1 .info-item.signed-out-dropdown { padding: .5rem 2rem; font-size: .8em; } .signed-out-dropdown-1 a.signed-out-dropdown:hover,.signed-out-dropdown-1 a.signed-out-dropdown:active,.signed-out-dropdown-1 a.signed-out-dropdown:focus { color: #fff; 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[Coursera] Natural Language Processing

by: Michael Collins (Columbia University)

Publisher: Academic Torrents

Contributor: Academic Torrents

Course Description:

COMS W4705 is a graduate introduction to natural language processing, the study of human language from a computational perspective. We will cover syntactic, semantic and discourse processing models. The emphasis will be on machine learning or corpus-based methods and algorithms. We will describe the use of these methods and models in applications including syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization.

Syllabus:

Here is a tentative syllabus for class:
Introduction
Estimation techniques, and language modeling
Tagging, hidden Markov models
Statistical parsing
Machine translation
Log-linear models
Conditional random fields, global linear models
Unsupervised and semi-supervised learning in NLP

Addeddate: 2018-08-12 18:54:36

External-identifier: urn:academictorrents:f99e7184fca947ee8f77901679e171fcadbf82e7

Identifier: academictorrents_f99e7184fca947ee8f77901679e171fcadbf82e7

Ocr: ABBYY FineReader 11.0 (Extended OCR)

Ppi: 300

Scanner: Internet Archive Python library 1.8.1

Source: http://academictorrents.com/details/f99e7184fca947ee8f77901679e171fcadbf82e7
torrent:urn:sha1:f99e7184fca947ee8f77901679e171fcadbf82e7

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14.2M

Natural Language Processing 0.0 Introduction (Part 1) (1117).mp4

11.9M

Natural Language Processing 0.1 Introduction (Part 2) (1028).mp4

7.5M

Natural Language Processing 1.0 Introduction to the Language Modeling Problem (Part 1) (617).mp4

8.3M

Natural Language Processing 1.1 Introduction to the Language Modeling Problem (Part 2) (712).mp4

10.2M

Natural Language Processing 1.2 Markov Processes (Part 1) (856).mp4

7.4M

Natural Language Processing 1.3 Markov Processes (Part 2) (628).mp4

11.1M

Natural Language Processing 1.4 Trigram Language Models (940).mp4

14.7M

Natural Language Processing 1.5 Evaluating Language Models Perplexity (1236).mp4

9.1M

Natural Language Processing 2.0 Linear Interpolation (Part 1) (746).mp4

13.7M

Natural Language Processing 2.1 Linear Interpolation (Part 2) (1135).mp4

11.2M

Natural Language Processing 2.2 Discounting Methods (Part 1) (926).mp4

4.4M

Natural Language Processing 2.3 Discounting Methods (Part 2) (334).mp4

3.0M

Natural Language Processing 3.0 Summary (231).mp4

1.1M

Natural Language Processing 18.0 Introduction (102).mp4

9.2M

Natural Language Processing 18.1 Recap of GLMs (740).mp4

6.9M

Natural Language Processing 18.2 GLMs for Tagging (Part 1) (526).mp4

9.3M

Natural Language Processing 18.3 GLMs for Tagging (Part 2) (735).mp4

8.7M

Natural Language Processing 18.4 GLMs for Tagging (Part 3) (706).mp4

7.2M

Natural Language Processing 18.5 GLMs for Tagging (Part 4) (600).mp4

699.7K

Natural Language Processing 19.0 Introduction (037).mp4

6.4M

Natural Language Processing 19.1 The Dependency Parsing Problem (Part 1) (521).mp4

17.1M

Natural Language Processing 19.2 The Dependency Parsing Problem (Part 2) (1353).mp4

14.2M

Natural Language Processing 19.3 GLMs for Dependency Parsing (Part 1) (1159).mp4

11.0M

Natural Language Processing 19.4 GLMs for Dependency Parsing (Part 2) (828).mp4

6.9M

Natural Language Processing 19.5 Experiments with GLMs for Dep. Parsing (538).mp4

3.4M

Natural Language Processing 19.6 Summary (250).mp4

13.0M

Natural Language Processing 4.0 The Tagging Problem (1001).mp4

10.7M

Natural Language Processing 4.1 Generative Models for Supervised Learning (857).mp4

14.9M

Natural Language Processing 4.2 Hidden Markov Models (HMMs) Basic Definitions (1200).mp4

16.3M

Natural Language Processing 4.3 Parameter Estimation in HMMs (1316).mp4

17.0M

Natural Language Processing 4.4 The Viterbi Algorithm for HMMs (Part 1) (1407).mp4

4.2M

Natural Language Processing 4.5 The Viterbi Algorithm for HMMs (Part 2) (331).mp4

9.3M

Natural Language Processing 4.6 The Viterbi Algorithm for HMMs (Part 3) (733).mp4

2.2M

Natural Language Processing 4.7 Summary (150).mp4

1,016.7K

Natural Language Processing 5.0 Introduction (028).mp4

12.6M

Natural Language Processing 5.1 Introduction to the Parsing Problem (Part 1) (1037).mp4

5.1M

Natural Language Processing 5.2 Introduction to the Parsing Problem (Part 2) (420).mp4

14.5M

Natural Language Processing 5.3 Context-Free Grammars (Part 1) (1211).mp4

2.8M

Natural Language Processing 5.4 Context-Free Grammars (Part 2) (222).mp4

12.6M

Natural Language Processing 5.5 A Simple Grammar for English (Part 1) (1032).mp4

6.4M

Natural Language Processing 5.6 A Simple Grammar for English (Part 2) (530).mp4

13.9M

Natural Language Processing 5.7 A Simple Grammar for English (Part 3) (1121).mp4

2.8M

Natural Language Processing 5.8 A Simple Grammar for English (Part 4) (220).mp4

6.7M

Natural Language Processing 5.9 Examples of Ambiguity (556).mp4

1.3M

Natural Language Processing 6.0 Introduction (112).mp4

11.6M

Natural Language Processing 6.1 Basics of PCFGs (Part 1) (943).mp4

10.8M

Natural Language Processing 6.2 Basics of PCFGs (Part 2) (826).mp4

9.4M

Natural Language Processing 6.3 The CKY Parsing Algorithm (Part 1) (731).mp4

16.6M

Natural Language Processing 6.4 The CKY Parsing Algorithm (Part 2) (1322).mp4

12.4M

Natural Language Processing 6.5 The CKY Parsing Algorithm (Part 3) (1007).mp4

17.9M

Natural Language Processing 7.0 Weaknesses of PCFGs (1459).mp4

330.4K

Natural Language Processing 8.0 Introduction (0017).mp4

12.8M

Natural Language Processing 8.1 Lexicalization of a Treebank (1044).mp4

16.0M

Natural Language Processing 8.2 Lexicalized PCFGs Basic Definitions (1240).mp4

6.5M

Natural Language Processing 8.3 Parameter Estimation in Lexicalized PCFGs (Part 1) (528).mp4

11.1M

Natural Language Processing 8.4 Parameter Estimation in Lexicalized PCFGs (Part 2) (908).mp4

12.1M

Natural Language Processing 8.5 Evaluation of Lexicalized PCFGs (Part 1) (932).mp4

14.3M

Natural Language Processing 8.6 Evaluation of Lexicalized PCFGs (Part 2) (1128).mp4

452.4K

Natural Language Processing 9.0 Opening Comments (025).mp4

2.4M

Natural Language Processing 9.1 introduction (203).mp4

9.4M

Natural Language Processing 9.2 Challenges in MT (806).mp4

10.0M

Natural Language Processing 9.3 Classical Approaches to MT (Part 1) (802).mp4

7.3M

Natural Language Processing 9.4 Classical Approaches to MT (Part 2) (556).mp4

15.7M

Natural Language Processing 9.5 Introduction to Statistical MT (1231).mp4

4.0M

Natural Language Processing 10.0 Introduction (324).mp4

16.1M

Natural Language Processing 10.1 IBM Model 1 (Part 1) (1306).mp4

10.9M

Natural Language Processing 10.2 IBM Model 1 (Part 2) (901).mp4

13.9M

Natural Language Processing 10.3 IBM Model 2 (1127).mp4

6.4M

Natural Language Processing 10.4 The EM Algorithm for IBM Model 2 (Part 1) (509).mp4

11.2M

Natural Language Processing 10.5 The EM Algorithm for IBM Model 2 (Part 2) (837).mp4

11.6M

Natural Language Processing 10.6 The EM Algorithm for IBM Model 2 (Part 3) (928).mp4

6.1M

Natural Language Processing 10.7 The EM Algorithm for IBM Model 2 (Part 4) (452).mp4

2.3M

Natural Language Processing 10.8 Summary (148).mp4

741.6K

Natural Language Processing 11.0 Introduction (041).mp4

11.5M

Natural Language Processing 11.1 Learning Phrases from Alignments (Part 1) (918).mp4

8.5M

Natural Language Processing 11.2 Learning Phrases from Alignments (Part 2) (701).mp4

11.1M

Natural Language Processing 11.3 Learning Phrases from Alignments (Part 3) (847).mp4

9.9M

Natural Language Processing 11.4 A Sketch of Phrase-based Translation (817).mp4

11.7M

Natural Language Processing 12.0 Definition of the Decoding Problem (Part 1) (912).mp4

15.9M

Natural Language Processing 12.1 Definition of the Decoding Problem (Part 2) (1300).mp4

13.5M

Natural Language Processing 12.2 Definition of the Decoding Problem (Part 3) (1043).mp4

18.0M

Natural Language Processing 12.3 The Decoding Algorithm (Part 1) (1439).mp4

7.6M

Natural Language Processing 12.4 The Decoding Algorithm (Part 2) (623).mp4

15.9M

Natural Language Processing 12.5 The Decoding Algorithm (Part 3) (1229).mp4

849.6K

Natural Language Processing 13.0 Introduction (047).mp4

14.1M

Natural Language Processing 13.1 Two Example Problems (1119).mp4

17.1M

Natural Language Processing 13.2 Features in Log-Linear Models (Part 1) (1356).mp4

12.5M

Natural Language Processing 13.3 Features in Log-Linear Models (Part 2) (1013).mp4

14.6M

Natural Language Processing 13.4 Definition of Log-linear Models (Part 1) (1150).mp4

4.5M

Natural Language Processing 13.5 Definition of Log-linear Models (Part 2) (345).mp4

15.5M

Natural Language Processing 13.6 Parameter Estimation in Log-linear Models (Part 1) (1244).mp4

5.2M

Natural Language Processing 13.7 Parameter Estimation in Log-linear Models (Part 2) (413).mp4

19.3M

Natural Language Processing 13.8 SmoothingRegularization in Log-linear Models (1512).mp4

1.9M

Natural Language Processing 14.0 Introduction (141).mp4

4.3M

Natural Language Processing 14.1 Recap of the Tagging Problem (315).mp4

10.3M

Natural Language Processing 14.2 Independence Assumptions in Log-linear Taggers (832).mp4

16.3M

Natural Language Processing 14.3 Features in Log-Linear Taggers (1321).mp4

4.8M

Natural Language Processing 14.4 Parameters in Log-linear Models (359).mp4

11.4M

Natural Language Processing 14.5 The Viterbi Algorithm for Log-linear Taggers (937).mp4

11.6M

Natural Language Processing 14.6 An Example Application (928).mp4

3.3M

Natural Language Processing 14.7 Summary (245).mp4

873.9K

Natural Language Processing 15.0 Introduction (047).mp4

8.7M

Natural Language Processing 15.1 Conditional History-based Models (714).mp4

8.7M

Natural Language Processing 15.2 Representing Trees as Decision Sequences (Part 1) (723).mp4

12.0M

Natural Language Processing 15.3 Representing Trees as Decision Sequences (Part 2) (1020).mp4

14.6M

Natural Language Processing 15.4 Features and Beam Search (1210).mp4

1.4M

Natural Language Processing 15.5 Summary (112).mp4

670.9K

Natural Language Processing 16.0 Introduction (036).mp4

11.1M

Natural Language Processing 16.1 Word Cluster Representations (836).mp4

14.4M

Natural Language Processing 16.2 The Brown Clustering Algorithm (Part 1) (1150).mp4

10.6M

Natural Language Processing 16.3 The Brown Clustering Algorithm (Part 2) (830).mp4

11.7M

Natural Language Processing 16.4 The Brown Clustering Algorithm (Part 3) (918).mp4

15.3M

Natural Language Processing 16.5 Clusters in NE Recognition (Part 1) (1133).mp4

8.9M

Natural Language Processing 16.6 Clusters in NE Recognition (Part 2) (728).mp4

574.8K

Natural Language Processing 17.0 Introduction (030).mp4

9.0M

Natural Language Processing 17.1 Recap of History-based Models (711).mp4

7.9M

Natural Language Processing 17.2 Motivation for GLMs (634).mp4

17.3M

Natural Language Processing 17.3 Three Components of GLMs (1439).mp4

12.8M

Natural Language Processing 17.4 GLMs for Parse Reranking (1036).mp4

7.3M

Natural Language Processing 17.5 Parameter Estimation with the Perceptron Algorithm (611).mp4

3.8M

Natural Language Processing 17.6 Summary (301).mp4

39.3M

Natural Language Processing 0.0 Introduction (Part 1) (1117).ogv

27.2M

Natural Language Processing 0.1 Introduction (Part 2) (1028).ogv

18.2M

Natural Language Processing 1.0 Introduction to the Language Modeling Problem (Part 1) (617).ogv

22.6M

Natural Language Processing 1.1 Introduction to the Language Modeling Problem (Part 2) (712).ogv

25.7M

Natural Language Processing 1.2 Markov Processes (Part 1) (856).ogv

17.8M

Natural Language Processing 1.3 Markov Processes (Part 2) (628).ogv

27.6M

Natural Language Processing 1.4 Trigram Language Models (940).ogv

41.0M

Natural Language Processing 1.5 Evaluating Language Models Perplexity (1236).ogv

26.9M

Natural Language Processing 2.0 Linear Interpolation (Part 1) (746).ogv

39.3M

Natural Language Processing 2.1 Linear Interpolation (Part 2) (1135).ogv

35.7M

Natural Language Processing 2.2 Discounting Methods (Part 1) (926).ogv

15.5M

Natural Language Processing 2.3 Discounting Methods (Part 2) (334).ogv

8.8M

Natural Language Processing 3.0 Summary (231).ogv

2.5M

Natural Language Processing 18.0 Introduction (102).ogv

31.0M

Natural Language Processing 18.1 Recap of GLMs (740).ogv

23.8M

Natural Language Processing 18.2 GLMs for Tagging (Part 1) (526).ogv

33.2M

Natural Language Processing 18.3 GLMs for Tagging (Part 2) (735).ogv

30.6M

Natural Language Processing 18.4 GLMs for Tagging (Part 3) (706).ogv

24.6M

Natural Language Processing 18.5 GLMs for Tagging (Part 4) (600).ogv

1.5M

Natural Language Processing 19.0 Introduction (037).ogv

21.3M

Natural Language Processing 19.1 The Dependency Parsing Problem (Part 1) (521).ogv

60.6M

Natural Language Processing 19.2 The Dependency Parsing Problem (Part 2) (1353).ogv

44.2M

Natural Language Processing 19.3 GLMs for Dependency Parsing (Part 1) (1159).ogv

36.6M

Natural Language Processing 19.4 GLMs for Dependency Parsing (Part 2) (828).ogv

24.3M

Natural Language Processing 19.5 Experiments with GLMs for Dep. Parsing (538).ogv

10.5M

Natural Language Processing 19.6 Summary (250).ogv

42.7M

Natural Language Processing 4.0 The Tagging Problem (1001).ogv

32.7M

Natural Language Processing 4.1 Generative Models for Supervised Learning (857).ogv

53.2M

Natural Language Processing 4.2 Hidden Markov Models (HMMs) Basic Definitions (1200).ogv

54.1M

Natural Language Processing 4.3 Parameter Estimation in HMMs (1316).ogv

55.7M

Natural Language Processing 4.4 The Viterbi Algorithm for HMMs (Part 1) (1407).ogv

13.1M

Natural Language Processing 4.5 The Viterbi Algorithm for HMMs (Part 2) (331).ogv

32.4M

Natural Language Processing 4.6 The Viterbi Algorithm for HMMs (Part 3) (733).ogv

6.9M

Natural Language Processing 4.7 Summary (150).ogv

2.0M

Natural Language Processing 5.0 Introduction (028).ogv

39.8M

Natural Language Processing 5.1 Introduction to the Parsing Problem (Part 1) (1037).ogv

15.0M

Natural Language Processing 5.2 Introduction to the Parsing Problem (Part 2) (420).ogv

48.1M

Natural Language Processing 5.3 Context-Free Grammars (Part 1) (1211).ogv

7.8M

Natural Language Processing 5.4 Context-Free Grammars (Part 2) (222).ogv

41.3M

Natural Language Processing 5.5 A Simple Grammar for English (Part 1) (1032).ogv

16.9M

Natural Language Processing 5.6 A Simple Grammar for English (Part 2) (530).ogv

48.0M

Natural Language Processing 5.7 A Simple Grammar for English (Part 3) (1121).ogv

9.5M

Natural Language Processing 5.8 A Simple Grammar for English (Part 4) (220).ogv

13.8M

Natural Language Processing 5.9 Examples of Ambiguity (556).ogv

2.3M

Natural Language Processing 6.0 Introduction (112).ogv

39.8M

Natural Language Processing 6.1 Basics of PCFGs (Part 1) (943).ogv

36.0M

Natural Language Processing 6.2 Basics of PCFGs (Part 2) (826).ogv

31.9M

Natural Language Processing 6.3 The CKY Parsing Algorithm (Part 1) (731).ogv

58.2M

Natural Language Processing 6.4 The CKY Parsing Algorithm (Part 2) (1322).ogv

41.8M

Natural Language Processing 6.5 The CKY Parsing Algorithm (Part 3) (1007).ogv

59.1M

Natural Language Processing 7.0 Weaknesses of PCFGs (1459).ogv

601.3K

Natural Language Processing 8.0 Introduction (0017).ogv

42.6M

Natural Language Processing 8.1 Lexicalization of a Treebank (1044).ogv

53.2M

Natural Language Processing 8.2 Lexicalized PCFGs Basic Definitions (1240).ogv

21.1M

Natural Language Processing 8.3 Parameter Estimation in Lexicalized PCFGs (Part 1) (528).ogv

38.6M

Natural Language Processing 8.4 Parameter Estimation in Lexicalized PCFGs (Part 2) (908).ogv

38.7M

Natural Language Processing 8.5 Evaluation of Lexicalized PCFGs (Part 1) (932).ogv

48.2M

Natural Language Processing 8.6 Evaluation of Lexicalized PCFGs (Part 2) (1128).ogv

581.0K

Natural Language Processing 9.0 Opening Comments (025).ogv

5.5M

Natural Language Processing 9.1 introduction (203).ogv

26.0M

Natural Language Processing 9.2 Challenges in MT (806).ogv

33.6M

Natural Language Processing 9.3 Classical Approaches to MT (Part 1) (802).ogv

23.5M

Natural Language Processing 9.4 Classical Approaches to MT (Part 2) (556).ogv

54.8M

Natural Language Processing 9.5 Introduction to Statistical MT (1231).ogv

11.0M

Natural Language Processing 10.0 Introduction (324).ogv

53.9M

Natural Language Processing 10.1 IBM Model 1 (Part 1) (1306).ogv

33.8M

Natural Language Processing 10.2 IBM Model 1 (Part 2) (901).ogv

45.4M

Natural Language Processing 10.3 IBM Model 2 (1127).ogv

21.9M

Natural Language Processing 10.4 The EM Algorithm for IBM Model 2 (Part 1) (509).ogv

36.4M

Natural Language Processing 10.5 The EM Algorithm for IBM Model 2 (Part 2) (837).ogv

39.4M

Natural Language Processing 10.6 The EM Algorithm for IBM Model 2 (Part 3) (928).ogv

21.0M

Natural Language Processing 10.7 The EM Algorithm for IBM Model 2 (Part 4) (452).ogv

7.5M

Natural Language Processing 10.8 Summary (148).ogv

1.1M

Natural Language Processing 11.0 Introduction (041).ogv

39.2M

Natural Language Processing 11.1 Learning Phrases from Alignments (Part 1) (918).ogv

29.6M

Natural Language Processing 11.2 Learning Phrases from Alignments (Part 2) (701).ogv

38.1M

Natural Language Processing 11.3 Learning Phrases from Alignments (Part 3) (847).ogv

31.7M

Natural Language Processing 11.4 A Sketch of Phrase-based Translation (817).ogv

38.8M

Natural Language Processing 12.0 Definition of the Decoding Problem (Part 1) (912).ogv

55.8M

Natural Language Processing 12.1 Definition of the Decoding Problem (Part 2) (1300).ogv

46.7M

Natural Language Processing 12.2 Definition of the Decoding Problem (Part 3) (1043).ogv

62.8M

Natural Language Processing 12.3 The Decoding Algorithm (Part 1) (1439).ogv

23.2M

Natural Language Processing 12.4 The Decoding Algorithm (Part 2) (623).ogv

54.7M

Natural Language Processing 12.5 The Decoding Algorithm (Part 3) (1229).ogv

1.2M

Natural Language Processing 13.0 Introduction (047).ogv

49.2M

Natural Language Processing 13.1 Two Example Problems (1119).ogv

58.4M

Natural Language Processing 13.2 Features in Log-Linear Models (Part 1) (1356).ogv

44.3M

Natural Language Processing 13.3 Features in Log-Linear Models (Part 2) (1013).ogv

49.8M

Natural Language Processing 13.4 Definition of Log-linear Models (Part 1) (1150).ogv

15.5M

Natural Language Processing 13.5 Definition of Log-linear Models (Part 2) (345).ogv

53.6M

Natural Language Processing 13.6 Parameter Estimation in Log-linear Models (Part 1) (1244).ogv

17.3M

Natural Language Processing 13.7 Parameter Estimation in Log-linear Models (Part 2) (413).ogv

65.4M

Natural Language Processing 13.8 SmoothingRegularization in Log-linear Models (1512).ogv

4.7M

Natural Language Processing 14.0 Introduction (141).ogv

13.8M

Natural Language Processing 14.1 Recap of the Tagging Problem (315).ogv

34.0M

Natural Language Processing 14.2 Independence Assumptions in Log-linear Taggers (832).ogv

56.9M

Natural Language Processing 14.3 Features in Log-Linear Taggers (1321).ogv

16.7M

Natural Language Processing 14.4 Parameters in Log-linear Models (359).ogv

36.7M

Natural Language Processing 14.5 The Viterbi Algorithm for Log-linear Taggers (937).ogv

39.1M

Natural Language Processing 14.6 An Example Application (928).ogv

10.6M

Natural Language Processing 14.7 Summary (245).ogv

1.6M

Natural Language Processing 15.0 Introduction (047).ogv

29.5M

Natural Language Processing 15.1 Conditional History-based Models (714).ogv

28.2M

Natural Language Processing 15.2 Representing Trees as Decision Sequences (Part 1) (723).ogv

35.0M

Natural Language Processing 15.3 Representing Trees as Decision Sequences (Part 2) (1020).ogv

49.8M

Natural Language Processing 15.4 Features and Beam Search (1210).ogv

3.9M

Natural Language Processing 15.5 Summary (112).ogv

1.2M

Natural Language Processing 16.0 Introduction (036).ogv

37.5M

Natural Language Processing 16.1 Word Cluster Representations (836).ogv

49.7M

Natural Language Processing 16.2 The Brown Clustering Algorithm (Part 1) (1150).ogv

36.5M

Natural Language Processing 16.3 The Brown Clustering Algorithm (Part 2) (830).ogv

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Natural Language Processing 16.4 The Brown Clustering Algorithm (Part 3) (918).ogv

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Natural Language Processing 16.5 Clusters in NE Recognition (Part 1) (1133).ogv

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Natural Language Processing 16.6 Clusters in NE Recognition (Part 2) (728).ogv

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Natural Language Processing 17.0 Introduction (030).ogv

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Natural Language Processing 17.1 Recap of History-based Models (711).ogv

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Natural Language Processing 17.2 Motivation for GLMs (634).ogv

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Natural Language Processing 17.3 Three Components of GLMs (1439).ogv

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Natural Language Processing 17.4 GLMs for Parse Reranking (1036).ogv

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Natural Language Processing 17.5 Parameter Estimation with the Perceptron Algorithm (611).ogv

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Natural Language Processing 17.6 Summary (301).ogv

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