# FrameNet > Mediated Wiki article. Canonical URL: https://mediated.wiki/source/FrameNet > Markdown URL: https://mediated.wiki/source/FrameNet.md > Source: https://en.wikipedia.org/wiki/FrameNet > Source revision: 1333550984 > License: Creative Commons Attribution-ShareAlike 4.0 International (https://creativecommons.org/licenses/by-sa/4.0/) Group of online lexical databases FrameNet Mission statement Building a lexical database based on a theory of meaning called frame semantics. Commercial? No (freely available for download) Type of project Lexical database (containing: frames, frame elements(FE), lexical units (LU), examples sentences, and frame relations) Location International Computer Science Institute in Berkeley, California Owner Collin Baker (current project manager) Founder Charles J. Fillmore Established 1997; 29 years ago (1997) Website framenet.icsi.berkeley.edu **FrameNet** is a group of online [lexical databases](/source/Lexical_resource) based upon the theory of meaning known as [Frame semantics](/source/Frame_semantics_(linguistics)), developed by linguist [Charles J. Fillmore](/source/Charles_J._Fillmore). The project's fundamental notion is simple: most words' meanings may be best understood in terms of a semantic frame, which is a description of a certain kind of event, connection, or item and its actors. As an illustration, the act of cooking usually requires the following: a cook, the food being cooked, a container to hold the food while it is being cooked, and a heating instrument.[1] Within FrameNet, this act is represented by a frame named Apply_heat, and its components (Cook, Food, Container, and Heating_instrument), are referred to as frame elements (FEs). The Apply_heat frame also lists a number of words that represent it, known as lexical units (LUs), like *fry*, *bake*, *boil*, and *broil*. Other frames are simpler. For example, Placing only has an [agent](/source/Agent_(grammar)) or cause, a [theme](/source/Thematic_relation#Theme)—something that is placed—and the location where it is placed. Some frames are more complex, like Revenge, which contains more FEs (offender, injury, injured party, avenger, and punishment).[*[citation needed](https://en.wikipedia.org/wiki/Wikipedia:Citation_needed)*] As in the examples of Apply_heat and Revenge below, FrameNet's role is to define the frames and annotate sentences to demonstrate how the FEs fit syntactically around the word that elicits the frame.[1] ## Concepts ### Frames A frame is a schematic representation of a situation involving various participants, props, and other conceptual roles. Examples of frame names are Being_born and Locative_relation. A frame in FrameNet contains a textual description of what it represents (a frame definition), associated frame elements, lexical units, example sentences, and frame-to-frame relations. ### Frame elements Frame elements (FE) provide additional information to the semantic structure of a sentence. Each frame has a number of core and non-core FEs which can be thought of as semantic roles. Core FEs are essential to the meaning of the frame while non-core FEs are generally descriptive (such as time, place, manner, etc.)[2] For example: - The only core FE of the Being_born frame is called Child; non-core FEs Time, Place, Means, etc.[3] - Core FEs of the Commerce_goods-transfer frame include the Seller, Buyer, and Goods, while non-core FEs include a Place, Purpose, etc.[4] FrameNet includes shallow data on syntactic roles that frame elements play in the example sentences. For example, for a sentence like "She was born about AD 460", FrameNet would mark *She* as a [noun phrase](/source/Noun_phrase) referring to the Child frame element, and "about AD 460" as a [noun phrase](/source/Noun_phrase) corresponding to the Time frame element. Details of how frame elements can be realized in a sentence are important because this reveals important information about the [subcategorization frames](/source/Subcategorization_frame) as well as possible [diathesis alternations](/source/Diathesis_alternation) (e.g. "John broke the window" vs. "The window broke") of a verb. ### Lexical units [Lexical units](/source/Lexical_item) (LUs) are lemmas, with their part of speech, that evoke a specific frame. In other words, when an LU is identified in a sentence, that specific LU can be associated with its specific frame(s). For each frame, there may be many LUs associated to that frame, and also there may be many frames that share a specific LU; this is typically the case with LUs that have multiple word senses.[2] Alongside the frame, each lexical unit is associated with specific frame elements by means of the annotated example sentences. For example, lexical units that evoke the Complaining frame (or more specific perspectivized versions of it, to be precise), include the verbs *complain*, *grouse*, *lament*, and others.[5] ### Example sentences Frames are associated with example sentences and frame elements are marked within the sentences. Thus, the sentence - *She was **born** about AD 460* is associated with the frame Being_born, while *She* is marked as the frame element Child and "about AD 460" is marked as Time.[3] From the start, the FrameNet project has been committed to looking at evidence from actual language use as found in text collections like the [British National Corpus](/source/British_National_Corpus). Based on such example sentences, automatic [semantic role labeling](/source/Semantic_role_labeling) tools are able to determine frames and mark frame elements in new sentences. ### Valences FrameNet also exposes statistics on the *valence* of each frame; that is, the number and position of the frame elements within example sentences. The sentence - *She was **born** about AD 460* falls in the valence pattern - NP Ext, INI --, NP Dep which occurs twice in the FrameNet's annotation report for the born.v lexical unit,[3] namely: - She*was **born***about AD 460*, daughter and granddaughter of Roman and Byzantine emperors, whose family had been prominent in Roman politics for over 700 years.* - *He was soon posted to north Africa, and never met their only child,*a daughter***born***8 June 1941*.* ### Frame relations This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed. (September 2023) (Learn how and when to remove this message) FrameNet additionally captures relationships between different frames using relations. These include the following: - **Inheritance**: When one frame is a more specific version of another, more abstract, parent frame. Anything that is true about the parent frame must also be true about the child frame, and a mapping is specified between the frame elements of the parent and the frame elements of the child. - **Perspectivization**: A neutral frame is connected to a frame with a specific perspective of the same scenario. For example, Commerce_transfer-goods is considered from the perspective of the buyer in Commerce_buy and from that of the seller in Commerce_sell. - **Subframe**: Some frames refer to complex scenarios that consist of several individual states or events that can be described by separate frames. For example, Criminal_process is composed of Arrest, Trial, and so on. - **Precedence:** This relation captures the temporal order that holds between subframes of a complex frame. For example, within the Cycle_of_life_and_death frame, the subframe Death is preceded by the subframe Being_born. - **Causative and Inchoative**: These two relations mark, for [causative](/source/Causative)- and [inchoative](/source/Inchoative_aspect)-aspect frames, the separate [stative](/source/Stative_verb) frame they refer to. For example, the stative Position_on_a_scale (e.g. "She had a *high* salary") is described by the causative Cause_change_of_scalar_position (e.g. "She *raised* his salary") and by the inchoative Change_position_on_a_scale frame (e.g. "Her salary *increased*"). - **Using**: This relation marks a frame that in some way involves another frame. For example, Judgment_communication *uses* both Judgment and Statement, but does not inherit from either of them because there is no clear correspondence of frame elements. - **See also**: Connects frames that bear some resemblance but need to be distinguished carefully. ## Applications FrameNet has proven to be useful in a number of computational applications, because computers need additional knowledge in order to recognize that "John sold a car to Mary" and "Mary bought a car from John" describe essentially the same situation, despite using two quite different verbs, different prepositions and a different word order. FrameNet has been used in applications like [question answering](/source/Question_answering), [paraphrasing](/source/Paraphrasing_(computational_linguistics)), recognizing [textual entailment](/source/Textual_entailment), and [information extraction](/source/Information_extraction), either directly or by means of [Semantic Role Labeling](/source/Semantic_Role_Labeling) tools. The first automatic system for [Semantic Role Labeling](/source/Semantic_Role_Labeling) (SRL, sometimes also referred to as "shallow semantic parsing") was developed by Daniel Gildea and [Daniel Jurafsky](/source/Daniel_Jurafsky) based on FrameNet in 2002.[6] Semantic Role Labeling has since become one of the standard tasks in natural language processing, with the latest version (1.7) of FrameNet now fully supported in the [Natural Language Toolkit](/source/Natural_Language_Toolkit).[7] Since frames are essentially semantic descriptions, they are similar across languages, and several projects have arisen over the years that have relied on the original FrameNet as the basis for additional non-English FrameNets, for Spanish, Japanese, German, and Polish, among others. ## See also - [BabelNet](/source/BabelNet): a multilingual semantic network integrating FrameNet - [PropBank](/source/PropBank) - [WordNet](/source/WordNet) - [Null instantiation](/source/Null_instantiation) - [Frame language](/source/Frame_language) - [UBY](/source/UBY): a database of 10 resources including FrameNet ## References 1. ^ [***a***](#cite_ref-:0_1-0) [***b***](#cite_ref-:0_1-1) ["What is FrameNet?"](https://framenet.icsi.berkeley.edu/WhatIsFrameNet). *FrameNet*. [Archived](https://web.archive.org/web/20230803104807/https://framenet.icsi.berkeley.edu/WhatIsFrameNet) from the original on 2023-08-03. Retrieved 2023-09-09. 1. ^ [***a***](#cite_ref-gloss_2-0) [***b***](#cite_ref-gloss_2-1) ["Glossary"](https://framenet.icsi.berkeley.edu/glossary). *FrameNet*. [Archived](https://web.archive.org/web/20230803034720/https://framenet.icsi.berkeley.edu/glossary) from the original on 2023-08-03. Retrieved 2023-09-09. 1. ^ [***a***](#cite_ref-born.v_3-0) [***b***](#cite_ref-born.v_3-1) [***c***](#cite_ref-born.v_3-2) ["Being_born.born.v (Annotation)"](https://framenet.icsi.berkeley.edu/fnReports/data/lu/lu9791.xml?mode=annotation). *FrameNet*. [Archived](https://web.archive.org/web/20230909200057/https://framenet.icsi.berkeley.edu/fnReports/data/lu/lu9791.xml?mode=annotation) from the original on 2023-09-09. Retrieved 2023-09-09. 1. **[^](#cite_ref-4)** ["Commerce_goods-transfer"](https://framenet.icsi.berkeley.edu/fnReports/data/frameIndex.xml?frame=Commerce_goods-transfer). *FrameNet*. [Archived](https://web.archive.org/web/20230909203034/https://framenet.icsi.berkeley.edu/fnReports/data/frameIndex.xml?frame=Commerce_goods-transfer) from the original on 2023-09-09. Retrieved 2023-09-09. 1. **[^](#cite_ref-5)** ["Complaining"](https://framenet.icsi.berkeley.edu/fnReports/data/frame/Complaining.xml). *FrameNet*. [Archived](https://web.archive.org/web/20230909194930/https://framenet.icsi.berkeley.edu/fnReports/data/frame/Complaining.xml) from the original on 2023-09-09. Retrieved 2023-09-09. 1. **[^](#cite_ref-6)** Gildea, Daniel; Jurafsky, Daniel (2002). ["Automatic Labeling of Semantic Roles"](https://www.cs.rochester.edu/~gildea/gildea-cl02.pdf) (PDF). *Computational Linguistics*. **28** (3): 245–288. [doi](/source/Doi_(identifier)):[10.1162/089120102760275983](https://doi.org/10.1162%2F089120102760275983). [S2CID](/source/S2CID_(identifier)) [207747200](https://api.semanticscholar.org/CorpusID:207747200). 1. **[^](#cite_ref-7)** Schneider, Nathan; Wooters, Chuck (2017). "The NLTK FrameNet API: Designing for Discoverability with a Rich Linguistic Resource". *EMNLP 2017: Conference on Empirical Methods in Natural Language Processing*. [arXiv](/source/ArXiv_(identifier)):[1703.07438](https://arxiv.org/abs/1703.07438). [Bibcode](/source/Bibcode_(identifier)):[2017arXiv170307438S](https://ui.adsabs.harvard.edu/abs/2017arXiv170307438S). ### Further reading - Ruppenhofer, Josef; Ellsworth, Michael; Petruck, Miriam R. L.; Johnson, Christopher R.; Baker, Collin F.; Scheffczyk, Jan (November 1, 2016). [*FrameNet II: Extended Theory and Practice*](https://web.archive.org/web/20221026121837/https://framenet2.icsi.berkeley.edu/docs/r1.7/book.pdf) (PDF) (revised ed.). Berkeley, CA: International Computer Science Institute. Archived from [the original](https://framenet2.icsi.berkeley.edu/docs/r1.7/book.pdf) (PDF) on 2022-10-26. ## External links - [FrameNet home page](http://framenet.icsi.berkeley.edu/) - [Chinese FrameNet](http://sccfn.sxu.edu.cn/) - [Danish FrameNet](http://framenet.dk/) - [German FrameNet](http://gframenet.gmc.utexas.edu/) [Archived](https://web.archive.org/web/20080511212723/http://gframenet.gmc.utexas.edu/) 2008-05-11 at the [Wayback Machine](/source/Wayback_Machine) - [Japanese FrameNet](http://jfn.st.hc.keio.ac.jp/) - [Korean FrameNet](http://framenet.kaist.ac.kr/) [Archived](https://web.archive.org/web/20190126021155/http://framenet.kaist.ac.kr/) 2019-01-26 at the [Wayback Machine](/source/Wayback_Machine) - [Polish FrameNet](http://www.ramki.uw.edu.pl/en/index.html) - [Portuguese FrameNet (Brazil)](http://www.ufjf.br/framenetbr/) - [Spanish FrameNet](http://gemini.uab.cat/SFN/) - [Swedish FrameNet](http://spraakbanken.gu.se/eng/swefn/) v t e Natural language processing General terms AI-complete Bag-of-words n-gram Bigram Trigram Computational linguistics Natural language understanding Stop words Text processing Text analysis Argument mining Collocation extraction Concept mining Coreference resolution Deep linguistic processing Distant reading Information extraction Knowledge extraction Logic translation Named-entity recognition Ontology learning Parsing semantic syntactic Part-of-speech tagging Semantic analysis Semantic role labeling Semantic decomposition Semantic similarity Sentiment analysis Stylometry adversarial Terminology extraction Text mining Textual entailment Truecasing Word-sense disambiguation Word-sense induction Text segmentation Compound-term processing Lemmatization Lexical analysis Text chunking Stemming Sentence segmentation Word segmentation Automatic summarization Multi-document summarization Sentence extraction Text simplification Machine translation Computer-assisted Example-based Rule-based Statistical Transfer-based Neural Distributional semantics models BERT Document-term matrix Explicit semantic analysis fastText GloVe Language model large small Latent semantic analysis Long short-term memory Seq2seq Transformer Word embedding Word2vec Language resources, datasets and corpora Types and standards Corpus linguistics Lexical resource Linguistic Linked Open Data Machine-readable dictionary Parallel text PropBank Semantic network Simple Knowledge Organization System Speech corpus Text corpus Thesaurus (information retrieval) Treebank Universal Dependencies Data BabelNet Bank of English DBpedia FrameNet Google Ngram Viewer UBY WordNet Wikidata Automatic identification and data capture Speech recognition Speech segmentation Speech synthesis Natural language generation Topic model Document classification Dynamic topic model Latent Dirichlet allocation Pachinko allocation Computer-assisted reviewing Automated essay scoring Concordancer Grammar checker Predictive text Pronunciation assessment Spell checker Natural language user interface Chatbot Interactive fiction Prompt engineering Question answering Virtual assistant Voice user interface Visual-linguistic Automatic image annotation CLIP Multimodal sentiment analysis Optical character recognition Vision-language model Vision–language–action model Related Formal semantics Gensim Hallucination Natural Language Toolkit spaCy --- Adapted from the Wikipedia article [FrameNet](https://en.wikipedia.org/wiki/FrameNet) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/FrameNet?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.