{"id":359,"date":"2020-06-13T21:36:33","date_gmt":"2020-06-14T04:36:33","guid":{"rendered":"http:\/\/linguamonium.com\/?p=359"},"modified":"2021-04-25T05:23:43","modified_gmt":"2021-04-25T05:23:43","slug":"frame-semantics-and-framenet","status":"publish","type":"post","link":"https:\/\/linguamonium.com\/?p=359","title":{"rendered":"Frame Semantics and FrameNet"},"content":{"rendered":"

I\u2019d like to discuss a theory in cognitive linguistics which is very near to my heart[1]<\/a>: frame semantics<\/strong>. I\u2019ll also present FrameNet<\/strong>, a database built using frame semantic theory, which has been and continues to be an excellent resource in the fields of natural language processing (NLP)<\/strong> and machine learning (ML)<\/strong>.<\/p>\n

Why is frame semantics cool? Why should you want to learn about it? Just this: the theory is an intuitive and comprehensive way to categorize the meaning of any scenario you could possibly dream up and express via language. Unlike many other semantic and syntactic theories, the core concepts are quickly understandable to the non-linguist. What\u2019s more, frame semantics can apply to language meaning at many different levels (from the tiniest morpheme to entire swaths of discourse), and it works equally well for any particular language \u2013 be it English, Mandarin, Arabic, or Xhosa. I\u2019ll try to demonstrate the theory\u2019s accessibility and applicability with some details.<\/p>\n

American linguist Charles Fillmore<\/a> developed the frame semantics research program in the 1980s, using the central idea of a frame<\/strong>: a cognitive scene or situation which is based on a person\u2019s prototypical understanding of real-world (social, cultural, biological) experiences. A frame is \u2018evoked\u2019 by language \u2013 this can be a single word (called a lexical unit<\/strong>), a clause, a sentence, or even longer discourse. Each frame contains various participants and props, called frame elements (FEs)<\/strong>. If you\u2019ve studied syntax\/semantics (the generative grammar kind), FEs are somewhat analogous to traditional theta roles<\/strong>.<\/p>\n

FrameNet<\/a> is a corpus<\/strong><\/a>-based lexicographic and relational database (sort of a complex dictionary) of English frames, the lexical units evoking them, annotated sentences containing those lexical units, and a hierarchy of frame-to-frame relations. It was built and continues to grow at the International Computer Science Institute (ICSI)<\/a>, a nonprofit research center affiliated with UC Berkeley. FrameNets have also been developed in other languages, such as Spanish, Brazilian Portuguese, Japanese, Swedish, French, Chinese, Italian, and Hebrew.<\/p>\n

Each frame entry includes a definition, example sentences, frame elements, lexical units, and annotation that illustrates the various fillers<\/strong> (words) of the FEs as well as their syntactic patterns. Let\u2019s unpack all of this!<\/p>\n

We\u2019ll take a look at the Motion<\/strong> <\/a>frame in FrameNet. Some screenshots of the frame entry follow.<\/p>\n

\"framenet_motion1\"<\/p>\n

The Motion<\/strong> frame is first defined. Its definition includes the frame elements that belong to the frame (the text with color highlighting):<\/p>\n

\u201cSome entity (Theme<\/strong>) starts out in one place (Source<\/strong>) and ends up in some other place (Goal<\/strong>), having covered some space between the two (Path<\/strong>). Alternatively, the Area<\/strong> or Direction <\/strong>in which the Theme<\/strong> moves or the Distance <\/strong>of the movement may be mentioned.\u201d<\/p>\n

After the definition come example sentences, featuring lexical units that evoke the frame (the black-backgrounded text) such as move, drift, float, roll, go<\/em>.<\/p>\n

Further down is the list of frame elements with their definitions and examples.<\/p>\n

\"framenet_motion2\"<\/p>\n

Here, the Theme<\/strong> FE is \u201cthe entity that changes location,\u201d while the Goal<\/strong> FE is \u201cthe location the Theme<\/strong> ends up in.\u201d In order for language to evoke this Motion<\/strong> frame, it must have some words or phrases which instantiate the Theme<\/strong>, the Goal<\/strong>, and the other FEs listed. In the examples above, me <\/em>is a Theme<\/strong> in The explosion made [me] MOVE in a hurry<\/em>; and into the slow lane <\/em>is a Goal<\/strong> in The car MOVED [into the slow lane]<\/em>.<\/p>\n

At the bottom of the entry is a list of lexical units that belong to or evoke the frame, as well as links to annotation of sentences from real data that contain those words.<\/p>\n

\"framenet_motion3\"<\/strong><\/p>\n

Verbs like come, glide, roll, travel, <\/em>and zigzag <\/em>all evoke, quite sensibly, the Motion<\/strong> frame.<\/p>\n

Once you click on the \u201cAnnotation\u201d link for a particular lexical item, you\u2019re taken to a page that looks like this:<\/p>\n

\"framenet_motion4\"<\/strong><\/p>\n

Natural language sentences pulled from online corpora (texts from newspapers, magazines, books, tv transcripts, scholarly articles, etc.) are annotated for their Motion<\/strong> FEs. Annotation for the lexical item glide<\/em> gives us an idea of the types of \u201centities\u201d (the purple-backgrounded text, or Theme<\/strong> FEs) that \u201cchange location\u201d (i.e. that glide<\/em>) \u2013 boats, pink clouds, men, cars, planes<\/em>, gondolas<\/em>, and so on.<\/p>\n

* * * * *<\/p>\n

After this mini FrameNet dive, you may be wondering how the database is used in a concrete sense. To illustrate, let\u2019s compare two sentences:<\/p>\n

    \n
  1. The boat <\/em>GLIDED into the harbor.<\/em><\/li>\n
  2. The dingy <\/em>DRIFTED away from the harbor. <\/em><\/li>\n<\/ol>\n

    The entities differ (boat <\/em>vs. dingy<\/em>), the verbs differ (glide <\/em>vs. drift<\/em>) and the prepositions differ (into <\/em>vs. [away<\/em>] from<\/em>). Yet at a higher level, both of these sentences describe a Theme<\/strong> which \u201cchanges location\u201d \u2013 either moving towards a Goal<\/strong> in (1), or from a Source<\/strong> in (2). They both indicate motion<\/u>. Because FrameNet helps machines \u201clearn\u201d that sentences with a variety of nouns, verbs, prepositions, and syntactic patterns can basically point to the same scenario, it\u2019s a useful tool for many applications in the computational realm.<\/p>\n

    These days computers do all kinds of language-y things for us: answer questions, paraphrase texts, extract relevant information from text (and then maybe organize it thematically \u2013 for instance, around people, places, or events), and even generate new texts. These feats require that a computer parse natural language into accurate semantic chunks. FrameNet\u2019s semantically- and syntactically-annotated data can be used as training input for machine models that \u201clearn\u201d how to analyze such meaning chunks, enabling our electronic devices to respond, paraphrase, or extract information appropriately.<\/p>\n

    To peruse a (very long) list of the projects which have used FrameNet data (organized by requester\/researcher), check out the FrameNet Downloaders page<\/a>.<\/p>\n

    So \u2013 on the off-chance that you find yourself stuck at home and bored out of your mind (?!?!)\u2026 you might perhaps enjoy a little investigation of frame-semantic characterization of scenes that involve applying heat<\/a>, intoxication<\/a>, or temporal collocation<\/a>. \ud83d\ude42<\/p>\n

     <\/p>\n

    [1]<\/a> Why am I so fond of frame semantics? A terrific professor of mine during grad school introduced the theory, and it resonated with me immediately. I used it in my master\u2019s thesis, then presented the paper at the International Conference on Construction Grammar in 2014. Eventually, I had the privilege of working at FrameNet, where I came to know the brilliant lexicographers\/semanticists\/cognitive linguists who have dedicated decades of their lives to the theory and the project. Sadly, I never met the legendary Chuck Fillmore, as he passed away the year before I joined the FrameNet team.<\/p>\n","protected":false},"excerpt":{"rendered":"

    I\u2019d like to discuss a theory in cognitive linguistics which is very near to my heart[1]: frame semantics. I\u2019ll also present FrameNet, a database built using frame semantic theory, which has been and continues to be an excellent resource in the fields of natural language processing (NLP) and machine learning (ML). Why is frame semantics…<\/p>\n","protected":false},"author":1,"featured_media":360,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"footnotes":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[4,7,17,20,28],"tags":[51,69,80,81,91,163],"class_list":["post-359","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cognitive-linguistics","category-corpus-linguistics","category-lexicology-lexicography","category-nlu-nlp-ml-ai","category-semantics","tag-charles-fillmore","tag-dictionaries","tag-framenet","tag-frames","tag-icsi","tag-theories"],"jetpack_publicize_connections":[],"yoast_head":"\nFrame Semantics and FrameNet - Linguamonium<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/linguamonium.com\/?p=359\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Frame Semantics and FrameNet - Linguamonium\" \/>\n<meta property=\"og:description\" content=\"I\u2019d like to discuss a theory in cognitive linguistics which is very near to my heart[1]: frame semantics. 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