• Computational linguistics

    Abstract: In this paper, we discuss the interactive visualization project “How Does ‘Hamilton,’ the Non Stop, Hip-hop Broadway Sensation Tap Rap’s Master Rhymes to Blur Musical Lines?” and the rhyme-pattern analysis algorithm that made the piece possible. This interactive extracts and describes the rhyme patterns interlaced throughout Lin-Manuel Miranda’s Broadway hit “Hamilton,” analyzing their higher-order structure to produce a genealogy of rap influence in the popular musical. The final section of the interactive allows readers to input their own lyrics

    and view the algorithmic categorization of their submission’s rhymes. The project was created for the Wall Street Journal and published online. An adapted portion of the project also appeared in the print edition. This paper

    explains the motivations behind the project, and the research and development of the user-interaction models, a unique visual language for rhyme patterns and the algorithm that produces them. We then use this process to suggest methods for getting news readers to play with pop-cultural analysis as a tactic to create

    strong feedback loops of engagement with news interactives that are not specifically games.


    Keywords: Information visualization; audio; lyric analysis; rhyme schemes, rap; hip-hop; musical theater; pop culture; Hamilton

    Abstract: Imperfect and internal rhymes are two important features in rap music previously ignored in the music information retrieval literature. We developed a method of scoring potential rhymes using a probabilistic model based on phoneme frequencies in rap lyrics. We used this scoring scheme to automatically identify internal and line-final rhymes in song lyrics and demonstrated the performance of this method compared to rules-based models. We then calculated higher-level rhyme features and used them to compare rhyming styles in song lyrics from different genres, and for different rap artists. We found that these detected features corresponded to real world descriptions of rhyming style and were strongly characteristic of different rappers, resulting in potential applications to style-based comparison, music recommendation, and authorship identification.


    Keywords: song lyrics, phonetic similarity, rhyme, hip hop, artist classification


    Abstract: This paper describes a new digital corpus of rap transcriptions known as the Musical Corpus of Flow (MCFlow). MCFlow currently contains transcriptions of verses from 124 popular rap songs, performed by 86 different rappers, containing a total of 374 verses, and consisting of 5,803 measures of music. MCFlow transcriptions contain rhythmic information, encoded in musical durations, as well as prosodic information, syntactic information, and phonetic information, including the identification of rhymes. In the second part of the paper, preliminary analyses of the corpus are presented, describing the "norms" of several important features of rap deliveries. These features include speed, rhyme density, metric position of stressed syllables, metric position of rhymes, phrase length, and the metric position of phrases. Several historical trends are identified, including an increase in rhyme density and phrase variability between 1980 and 2000. In each analysis, variance between different performers is compared to variance between songs. It is found that there is generally more variability between songs than between performers.


    Keywords: rap; corpus; rhythm; rhyme; phrasing; historical trends


    Abstract: Textsetting, the matching of linguistic objects and rhythmic ones, is a subject of enduring interest to researchers. Most studies of textsetting involve relatively simple forms such as children’s songs (Halle 1999), folk verse (Hayes & MacEachern 1996), and art verse similar to these traditions (Lerdahl 2001a). Hip-hop departs from many of the conventions of these genres, introducing additional levels of complexity. In this paper, I offer an analysis of hip-hop rhyme based on Lerdahl’s (2001b) analogy between rhyme and harmony in music. I argue that adopting the formalism of prolongational reduction from Lerdahl & Jackendoff’s (1983) seminal work A Generative Theory of Tonal Music (hencforth GTTM) allows us to analyze and actually predict many of the complex rhythmic phenomena encountered in modern hip-hop.


    Abstract: Writing rap lyrics requires both creativity to construct a meaningful, interesting story and lyrical skills to produce complex rhyme patterns, which form the cornerstone of good flow. We present a rap lyrics generation method that captures both of these aspects. First, we develop a prediction model to identify the next line of existing lyrics from a set of candidate next lines. This model is based on two machine-learning techniques: the RankSVM algorithm and a deep neural network model with a novel structure. Results show that the prediction model can identify the true next line among 299 randomly selected lines with an accuracy of 17%, i.e., over 50 times more likely than by random. Second, we employ the prediction model to combine lines from existing songs, producing lyrics with rhyme and a meaning. An evaluation of the produced lyrics shows that in terms of quantitative rhyme density, the method outperforms the best human rappers by 21%. The rap lyrics generator has been deployed as an online tool called DeepBeat, and the performance of the tool has been assessed by analyzing its usage logs. This analysis shows that machine-learned rankings correlate with user preferences.


    Abstract:   While text Information Retrieval applications often focus on extracting semantic features to identify the topic of a document, and Music Information Research tends to deal with melodic, timbral or meta-tagged data of songs, useful information can be gained from surface-level features of musical texts as well. This is especially true for texts such as song lyrics and poetry, in which the sound and structure of the words is important. These types of lyrical verse usually contain regular and repetitive patterns, like the rhymes in rap lyrics or the meter in metrical poetry. The existence of such patterns is not always categorical, as there may be a degree to which they appear or apply in any sample of text. For example, rhymes in hip hop are often imperfect and vary in the degree to which their constituent parts differ. Although a definitive decision as to the existence of any such feature cannot always be made, large corpora of known examples can be used to train probabilistic models enumerating the likelihood of their appearance. In this thesis, we apply likelihood-based methods to identify and characterize patterns in lyrical verse. We use a probabilistic model of mishearing in music to resolve misheard lyric search queries. We then apply a probabilistic model of rhyme to detect imperfect and internal rhymes in rap lyrics and quantitatively characterize rappers’ styles in their use. Finally, we compute likelihoods of prosodic stress in words to perform automated scansion of poetry and compare poets’ usage of and adherence to meter. In these applications, we find that likelihood-based methods outperform simpler, rule-based models at finding and quantifying lyrical features in text.


    Abstract:  In this paper, we focus on large scale poetry classification by meter. We repurposed an open source poetry

    scanning program (the Scandroid by Charles O. Hartman) as a feature extractor. Our machine learning experiments

    show a useful ability to classify poems by poetic meter. We also made our own rhyme detector using the Carnegie Melon University Pronouncing Dictionary as our primary source of pronunciation information. Future work will involve classifying rhyme and assembling a graph (or graphs) as part of the Graph Poem Project depicting the interconnected.


    Keywords: nlp; poetry; machine learning; decision trees; artificial intelligence; graph theory


    Abstract:  This dissertation describes the motivation, methodology, structure and content of a new symbolic corpus of rap vocal transcriptions known as the Musical Corpus of Flow (MCFlow). This corpus is intended to afford and inform research into the sonic organization of rapped vocals. An operational music theory of rap is presented, identifying the most artistically important features of rapped vocals and their most basic organizational structures. This theory informs and motivates the sampling and encoding scheme of MCFlow, which is described in detail. The content of the current MCFlow dataset is described as well: the current dataset includes transcriptions of 124 hip-hop songs by 47 artists, comprising 6,107 measures of music which contain 54,248 rapped words. Several preliminary descriptive analyses of the current dataset are presented as illustrations of MCFlow's usefulness for: (1) identifying normative structures in rap; (2) comparing the styles of different artists; (3) studying the historical evolution of rap artistry. Information regarding access to MCFlow data and tools for analyzing the data are presented and the MCFlow online Graphical User Interface usable by any user with no special software requirement sis described.


    The full dissertation is available here.


    Abstract: This paper describes an unsupervised, language-independent model for finding rhyme schemes in poetry, using no prior knowledge about rhyme or pronunciation.


    There's a more detailed presentation here


    Abstract: We employ statistical methods to analyze, generate, and translate rhythmic poetry. We first apply unsupervised learning to reveal word-stress patterns in a corpus of raw poetry. We then use these word-stress patterns, in addition to rhyme and discourse models, to generate English love poetry. Finally, we translate Italian poetry into English, choosing target realizations that conform to desired rhythmic patterns.


    Abstract:  We describe Hafez, a program that generates any number of distinct poems on a user supplied topic. Poems obey rhythmic and rhyme constraints. We describe the poetry generation algorithm, give experimental data concerning its parameters, and show its generality with respect to language and poetic form.


    Abstract: Identification functions of 20 initial and 20 final consonants were characterized in 9600 randomly sampled consonant-vowel-consonant CVC tokens presented in speech-spectrum noise. Because of differences in the response criteria for different consonants, signal detection measures were used to quantify identifiability. Consonant-specific baseline signal-to-noise ratios SNRs were adjusted to produce a d of 2.20 for each consonant. Consonant identification was measured at baseline SNRs B , at B−6, and at B+6 dB. Baseline SNRs varied by more than 40 dB for different consonants. Confusion analysis revealed that single-feature place-of-articulation errors predominated at the highest SNR, while combined-feature errors predominated at the lowest SNR. Most consonants were identified at lower SNRs in initial than final syllable position. Vowel nuclei /Ä /, /i/, or /u/ significantly influenced the identifiability of 85% of consonants, with consistent vowel effects seen for consonant classes defined by manner, voicing, and place. Manner and voicing of initial and final consonants were processed independently, but place cues interacted: initial and final consonants differing in place of articulation were identified more accurately than those sharing the same place. Consonant identification in CVCs reveals contextual complexities in consonant processing.


    Abstract:  Computational techniques for scoring essays have recently come into use. Their bases and development methods raise both old and new measurement issues. However, coming principally from computer and cognitive sciences, they have received little attention from the educational measurement community. We briefly survey the state of the technology, then describe one such system, the Intelligent Essay Assessor (IEA). IEA is based largely on Latent Semantic Analysis (LSA), a machine-learning model that induces the semantic similarity of words and passages by analysis of large bodies of domain-relevant text. IEA’s dominant variables are computed from comparisons with pre-scored essays of highly similar content as measured by LSA. Over many validation studies with a wide variety of topics and test-takers, IEA correlated with human graders as well as they correlated with each other. The technique also supports other educational applications. Critical measurement questions are posed and discussed.


    Abstract:  Tra-la-Lyrics is a system that generates song lyrics automatically. In its original version, the main focus was to produce text where stresses matched the rhythm of given melodies. There were no concerns on whether the text made sense or if the selected words shared some kind of semantic association. In this article, we describe the development of a new version of Tra-la-Lyrics, where text is generated on a semantic domain, defined by one or more seed words. This effort involved the integration of the original rhythm module of Tra-la-Lyrics in PoeTryMe, a generic platform that generates poetry with semantically coherent sentences. To measure our progress, the rhythm, the rhymes, and the semantic coherence in lyrics produced by the original Tra-la-Lyrics were analysed and compared with lyrics produced by the new instantiation of this system, dubbed Tra-la-Lyrics 2.0. The analysis showed that, in the lyrics by the new system, words have higher semantic association among them and with the given seeds, while the rhythm is still matched and rhymes are present. The previous analysis was complemented with a crowdsourced evaluation, where contributors answered a survey about relevant features of lyrics produced by the previous and

    the current versions of Tra-la-Lyrics. Though tight, the survey results confirmed the improvements of the lyrics by Tra-la-Lyrics 2.0.


    Keywords: computational creativity, linguistic creativity, song lyrics, poetry, rhythm, semantics

    Exploring the Music Genome: Lyric Clustering with Heterogeneous Features


    Abstract:   This research explores the clustering of songs using lyrics features grouped into similar classes and heterogeneous combinations. Simple techniques are used to extract 140 features for analysis with Kohonen self-organising maps. These maps are evaluated using visual analysis and objective measures of validity with respect to the clustering of eight hand-selected song pairs. According to gold standard human-authored playlists, judgments of song similarity are based strongly on music, however this observation may be limited to playlists and is not necessarily extensible to music in the wider domain. In particular, since test song pairs could only be effectively matched when they were from the same genre, analysis of the correspondence between lyrics and expert human judgments of genre and style may be more fruitful than comparison with similarities observed in playlists. Results suggest that for music in the hard-to-differentiate categories of pop, rock and related genres, a combination of features relating to language, grammar, sentiment and repetition improve on the clustering performance of Information Space with a more accurate analysis of song similarity and increased sensitivity to the nuances of

    song style. SOM analysis further suggests that a few well-chosen attributes may be as good as, if not better than, deep analysis using many features. Results using stress patterns are inconclusive. Although results are preliminary and need to be validated with further research on a larger data set, to the knowledge of this author this is the first time success has been reported in differentiating songs in the rock/pop genre.


    Abstract:   How individuals perceive music is influenced by many dif- ferent factors. The audible part of a piece of music, its sound, does for sure contribute, but is only one aspect to be taken into account. Cultural information influences how we experience music, as does the songs' text and its sound. Next to symbolic and audio based music information re- trieval, which focus on the sound of music, song lyrics, may thus be used to improve classification or similarity ranking of music. Song lyrics exhibit specific properties different from traditional text documents - many lyrics are for exam- ple composed in rhyming verses, and may have different fre- quencies for certain parts-of-speech when compared to other text documents. Further, lyrics may use 'slang' language or differ greatly in the length and complexity of the language used, which can be measured by some statistical features such as word / verse length, and the amount of repetative text. In this paper, we present a novel set of features devel- oped for textual analysis of song lyrics, and combine them with and compare them to classical bag-of-words indexing approaches. We present results for musical genre classifica- tion on a test collection in order to demonstrate our analysis.


    Abstract:  When someone wishes to find the lyrics for a song they typically go online and use a search engine. There are a large number of lyrics available on the internet as the effort required to transcribe and post lyrics is minimal.

    These lyrics are promptly returned to the user with customary search engine page ranking formula deciding the

    ordering of these results based on links, views, clicks, etc. However the content, and specifically, the accuracy of the
    lyrics in question are not analysed or used in any way to determine the rank of the lyrics, despite this being of con-
    cern to the searcher. In this work, we show that online lyrics are often inaccurate and the ranking methods used

    by search engines do not distinguish the more accurate annotations. We present an alternative method for ranking

    lyrics based purely on the collection of lyrics themselves using the Lyrics Concurrence.


    Abstract:  Retrieving the lyrics of a sung recording from a database of text documents is a research topic that has not received much attention so far. Such a retrieval system has many practical applications, e.g. for karaoke applications or for indexing large song databases by their lyric content. We present a new method for lyrics retrieval. An acoustic model trained on singing is used to obtain phoneme probabilities from sung

    queries, which are then mapped to phoneme sequences. These are compared against lines of textual lyrics in a large corpus in order to retrieve the best-matching song. The approach is tested on three sung datasets. Lyrics are

    retrieved from a set of 300 possible songs (12,000 lines of lyrics). The results are highly encouraging and could be used further to perform automatic lyrics alignment and keyword spotting for large databases of songs, or for retrieving lyrics from the internet.


    Keywords: Lyrics, Text retrieval, Singing, Automatic Speech Recognition, Music Information Retrieval


    Abstract:  Not all learning takes place in an educational setting: more and more self-motivated learners are turning to on-line text to learn about new topics. Our goal is to provide such learners with the well-known benefits of testing by automatically generating quiz questions for on-line text. Prior work on question generation has focused on the grammaticality of generated questions and generating effective multiple-choice distractors for individual question

    targets, both key parts of this problem. Our work focuses on the complementary aspect of determining what part of a sentence we should be asking about in the first place; we call this “gap selection.” We address this problem by
    asking human judges about the quality of questions generated from a Wikipedia-based corpus, and then training a model to effectively replicate these judgments. Our data shows that good gaps are of variable length and span
    all semantic roles, i.e., nouns as well as verbs, and that a majority of good questions do not focus on named entities. Our resulting system can generate fill-in-the-blank (cloze) questions from generic source materials.


    Executive summary: The new assessment systems designed to measure the Common Core standards will include far more performance-based items and tasks than most of today’s assessments. In order to provide feedback quickly and make scoring of the responses affordable, the new assessments will rely heavily on artificial intelligence scoring, hereafter referred to as automated scoring. Familiarity with the current range of applications and the operational accuracy of automatic scoring technology can help provide an understanding of which item types can be scored automatically in the near term. This document describes several examples of current item types that Pearson has designed and fielded successfully with automatic scoring. The item examples are presented along with operational reliability and accuracy figures, as well as information of the nature and development of the automated scoring systems used by Pearson.


    Abstract: WordNet is an on-line lexical reference system whose design is inspired by current psycholinguistic theories of human lexical memory. English nouns, verbs, and adjectives are organized into synonym sets, each representing one underlying lexical concept. Different relations link the synonym sets.


    Abstract: In the REAP system, users are automatically provided with texts to read targeted to their individual reading levels. To find appropriate texts, the user’s vocabulary knowledge must be assessed. We describe an approach to automatically generating questions for vocabulary assessment. Traditionally, these assessments have been hand-written. Using data from WordNet, we generate 6 types of vocabulary questions. They can have several forms, including wordbank and multiple-choice. We present experimental results that suggest that these automatically-generated questions give a measure of vocabulary skill that correlates well with subject perform-
    ance on independently developed human-written questions. In addition, strong correlations with standardized vocabulary tests point to the validity of our approach to automatic assessment of word knowledge.

    All Posts
    Terms & Conditions
    Terms & Conditions
    Thanks for visiting LYRICAL KOMBAT. We’ll do all we can to ensure you have an enjoyable experience. When we refer to "LYRICAL KOMBAT" in these Terms, we mean LYRICAL KOMBAT LIMITED, the www.lyricalkomb.at site, and/or any LYRICAL KOMBAT application(s) that you access, according to the context.
    Here’s what you need to know...
    1. Introduction
    2. Our trading details
    3. Use of the Website
    4. Offers and Promotions
    5. General
    1. Introduction
    These terms and conditions (the "Terms") set out your rights and obligations, and those of LYRICAL KOMBAT ("we" or "us"), in relation to this website, www.lyricalkomb.at (the "Website"), any LYRICAL KOMBAT application(s) that you access (the "App") and any products, services and material of any kind available through the Site, the App or us (the "Services").
    We are a company registered in England and Wales under registration number 10203727 under the name LYRICAL KOMBAT LIMITED; the address of our registered office is 27 Old Gloucester Street, London, WC1N 3AX. When we refer to "LYRICAL KOMBAT" in these Terms, we mean us, the Site, the App and/or the Services, according to the context.
    By registering with LYRICAL KOMBAT, you accept that you are entering into a contract with us under these Terms. People who register with LYRICAL KOMBAT establish an "Account", and become "Users". Visitors to LYRICAL KOMBAT who do not register with LYRICAL KOMBAT, but who nevertheless use the Site or download the App, affirm that they are bound by these Terms each time they access LYRICAL KOMBAT.
    You must be at least 13 years of age to use this website or the app.
    If you do not agree to these Terms, you must not use this Website or the App.
    You should be aware that these Terms may change from time to time
    You will be able to access most areas of LYRICAL KOMBAT without registering your details with us.
    2. Trading details
    Whenever you buy a product through this Website or the App, you will be contracting with LYRICAL KOMBAT, a limited liability company incorporated in England and Wales under company number 11263442.
    Registered office: 27 Old Gloucester Street, London, WC1N 3AX.  
    Email address: hello@lyricalkombat.com
    3. Use of the Website 
    3.1 Content Rules
    You are responsible for the content you create and should not create anything that includes any content or material which:
    *infringes anyone's copyright: 
    *infringes any other rights, such as a trade mark, of any person or entity or a duty owed to any person or entity, such as a duty of confidentiality
    *contravenes any applicable law (including, without limitation, any criminal law) or regulation
    *is false, inaccurate, misleading, offensive, abusive, threatening or defamatory, or that might cause needless annoyance, inconvenience or distress to any person
    misrepresents identity or impersonates any person
    *includes any material containing personally identifying information about another person, such as their address, phone number, or email address, except with the written approval of that person
    *contains material which is pornographic, obscene, indecent or offensive, which promotes discrimination based on race, sex, religion, nationality, disability, sexual orientation or age, or that may incite hatred or violence against any person or group
    may harass, upset, embarrass or alarm any person
    *advocates, promotes or assists any unlawful act
    The above are our Content Rules..
    3.2 Circumstances Beyond Our Control
    Neither we, nor any delivery service that we use, shall be liable for any failure to perform Services where such failure or delay results from any circumstances outside our reasonable control; these circumstances include but are not limited to adverse weather conditions (such as snow, flood and extreme winds), fire, explosion, accident, traffic congestion, obstruction of any private or public highway, riot, terrorism, act of God, or industrial dispute or strike.
    3.3 Abuse of service
    You agree not to use LYRICAL KOMBAT directly or indirectly for any unlawful purpose, or to cause distress or offence to any person. You also agree not to upload, email to us or print any material which might infringe our Content Rules 
    3.4 Intellectual Property
    All text, software, music, sound, photographs, graphics, video, page layouts, design and other material that appears on the Website or App is protected by LYRICAL KOMBAT’s or third party copyrights, trade marks, service marks, patents or other proprietary rights and laws. "LYRICAL KOMBAT" is our trademark, and you agree not to display or use it in any manner without our prior written consent.
    You may use and access LYRICAL KOMBAT to the extent required for the use of the Services in accordance with these Terms, and for the purpose that we make them available. You are not allowed to remove any copyright, trade mark or other intellectual property notices contained in material taken from LYRICAL KOMBAT.
    You are not allowed to use LYRICAL KOMBAT (or to copy or use any material found on LYRICAL KOMBAT) for any commercial purpose other than purpose for which rights of usage have been granted.
    No-one may use any robot, spider, scraper or other automated means to access LYRICAL KOMBAT for any purpose without our prior express written permission.
    3.5 Website content and service access
    We will always try to ensure that LYRICAL KOMBAT is available 24 hours a day. However, we will not be liable if for any reason the Website is unavailable at any time, or for any period.
    3.6 Links
    Occasionally, we may provide links to other websites or resources for your convenience. We do not endorse the contents of those websites and are not responsible for their availability or service. We will not be liable in any way for any loss or damage which you may suffer by using those websites. If you decide to access linked third party websites you do so at your own risk.
    3.7 Liability
    We use reasonable care and skill to provide LYRICAL KOMBAT in accordance with our specifications for LYRICAL KOMBAT but:
    the Services are provided "as is"
    *we cannot and do not guarantee that LYRICAL KOMBAT or the Services will meet your requirements
    *We shall have no obligation, duty or liability whatsoever in contract, tort (including negligence, breach of statutory duty and any other tort) or otherwise, save as expressly provided in these Terms.
    *Nothing in these Terms excludes or restricts our liability for fraudulent misrepresentation or for death or personal injury resulting from our negligence, nor our contractual obligations in respect of products we agree to supply.
    We, our agents, directors, officers, shareholders, employees and subcontractors will not be liable to you or anyone else, whether in contract, tort (including negligence, breach of statutory duty or other tort) or otherwise:
    *for any loss of revenue, data, business, anticipated savings, profits, opportunity, goodwill or reputation, or for any business interruption
    *any loss or corruption of data
    *any loss or damage which does not directly result from (or which exceeds that which was caused as a direct result of) our breach of this Agreement, howsoever caused or arising
    *Except as expressly stated elsewhere in these Terms, all representations, warranties, 
    conditions and other terms, whether express or implied (by common law, statute, 
    collaterally or otherwise) are hereby excluded, except in the case of fraud, or where such exclusion is not permitted by law.
    *We will not be liable for any failure to perform our obligations under these Terms caused by matters beyond our reasonable control.
    *You agree to indemnify us fully, defend and hold us, and our officers, directors, employees and agents, harmless from and against all claims, liability, damages, losses, costs (including reasonable legal fees) arising out of any breach of the Terms by you, your use of the Website or App, or anything done while your Account is logged into the Website or App.
    The provisions of this section shall survive the termination or expiry of these Terms.
    3.8 Suspension
    We reserve the right at any time and without notice (a) to suspend or terminate your ability to access LYRICAL KOMBAT, (b) to suspend or terminate your use of all or part of the Services and/or any then-current orders for products, and/or (c) to take technical and legal steps to stop you from using LYRICAL KOMBAT if you appear to us to be in breach of any provision of these Terms. Should we do so, you acknowledge that we shall not incur any liability to you or to any other person.
    In addition, we are entitled to suspend provision of all or part of the Services at any time if we are obliged or advised to comply with an order, instruction or request of any limb of government, or any regulator, court or other competent authority.
    3.9 Contact from third parties
    If anyone contacts us in relation to your or your Account's use of LYRICAL KOMBAT or a transaction associated with you or your Account, then you agree:
    *to provide all reasonable information and assistance we may require in connection with responding to that contact
    *to respond promptly and accurately to it, should we pass the message to you for a response
    4. Offers and Promotions
    The offers and promotions which we make available on our Website or through our App are governed by terms and conditions which will be accessible to you at the time here.
    5. General
    These Terms are intended to contain your entire agreement with us relating to the Services, the Website and the App.
    If any of these Terms is found to be invalid this shall not affect the validity of the remaining provisions which shall remain fully enforceable. Our failure to act with respect to a breach of these Terms by you or others does not waive our right to act with respect to subsequent or similar breaches.
    Only you and LYRICAL KOMBAT shall be entitled to enforce these Terms. No third party shall be entitled to enforce any of these Terms whether by virtue of the Contracts (Rights of Third Parties) Act 1999 or otherwise.
    We reserve the right to change these Terms from time to time, and post the new version on LYRICAL KOMBAT. The new version of these Terms will take effect:
    *commencing 28 days after the date of posting (or such later date as we indicate in the 
    relevant posting), if any of the changes is to an operative provision of these Terms which is capable of adversely affecting you
    *immediately upon the date of posting (or such later date as we indicate in the relevant posting), if the changes are not reasonably capable of adversely affecting you - 
    examples of which would include, without limitation, (i) changing the name of, and/or the web-address that you use to access, LYRICAL KOMBAT, and (ii) the refinement of 
    provisions that are already included or referred to in these Terms
    In either case, if you do not wish to be governed by the new version of these Terms, you must cease to use the Services and LYRICAL KOMBAT. For the avoidance of doubt, we will not have any liability to you in that event.
    These Terms, the Services and each order and purchase of a product shall be governed by English law.
    You and we each submit to the non-exclusive jurisdiction of the English courts in relation to disputes arising in connection with these Terms, the Services and any order for or purchase of a product.
    We don't separately file the Terms entered into by Users when they use our site. Please make a durable copy of these Terms by printing and/or saving a downloaded copy on your own computer. They are offered in English only.
    These Terms, together with the LYRICAL KOMBAT Ts & Cs, are intended to contain your entire agreement with us relating to the subject-matter of these Terms. 
    If any of these Terms is found to be invalid this shall not affect the validity of the remaining provisions which shall remain fully enforceable. Our failure to act with respect to a breach of these Terms by you or others does not waive our right to act with respect to subsequent or similar breaches. 
    We reserve the right to change these Terms from time to time, and post the new version on the Website. The new version of these Terms will take effect on a date that we will specify at the time. 
    You and we each submit to the non-exclusive jurisdiction of the English courts in relation to disputes arising in connection with these Terms, LYRICAL KOMBAT Rewards or any of the subject-matter of these Terms. 
    These Terms take effect on 28th of February, 2019