Journal articles on the topic 'Tokenisation'

In this paper, we inspect the theoretical problem of counting the number of analogies between sentences contained in a text. Based on this, we measure the analogical density of the text. We focus on analogy at the sentence level, based on the level of form rather than on the level of semantics. Experiments are carried on two different corpora in six European languages known to have various levels of morphological richness. Corpora are tokenised using several tokenisation schemes: character, sub-word and word. For the sub-word tokenisation scheme, we employ two popular sub-word models: unigram language model and byte-pair-encoding. The results show that the corpus with a higher Type-Token Ratio tends to have higher analogical density. We also observe that masking the tokens based on their frequency helps to increase the analogical density. As for the tokenisation scheme, the results show that analogical density decreases from the character to word. However, this is not true when tokens are masked based on their frequencies. We find that tokenising the sentences using sub-word models and masking the least frequent tokens increase analogical density.

AbstractThis paper details the development of a multidialectical text-to-speech (TTS) application, Sintéiseoir, for the Irish language. This work is being carried out in the context of Irish as a lesser-used language, where learners and other L2 speakers have limited direct exposure to L1 speakers and speech communities, and where native sound systems and vocabularies can be seen to be receding even among L1 speakers – particularly the young.Sintéiseoir essentially implements the diphone concatenation model, albeit augmented to include phones, half-phones and, potentially, other phonic units. It is based on a platform-independent framework comprising a user interface, a set of dialect-specific tokenisation engines, a concatenation engine and a playback device.The tokenisation strategy is entirely rule-based and does not refer to dictionary look-ups. Provision has been made for prosodic processing in the framework but has not yet been implemented. Concatenation units are stored in the form of WAV files on the local file system.Sintéiseoir’s user interface (UI) provides a text field that allows the user to submit a grapheme string for synthesis and a prompt to select a dialect. It also filters input to reject graphotactically invalid strings, restrict input to alphabetic and certain punctuation marks found in Irish orthography, and ensure that a dialect has, indeed, been selected.The UI forwards the filtered grapheme string to the appropriate tokenisation engine. This searches for specified substrings and maps them to corresponding tokens that themselves correspond to concatenation units.The resultant token string is then forwarded to the concatenation engine, which retrieves the relevant concatenation units, extracts their audio data and combines them in a new unit. This is then forwarded to the playback device.The terms of reference for the initial development of Sintéiseoir specified that it should be capable of uttering, individually, the 99 most common Irish lemmata in the dialects of An Spidéal, Músgraí Uí Fhloínn and Gort a’ Choirce, which are internally consistent dialects within the Connacht, Munster and Ulster regions, respectively, of the dialect continuum. Audio assets to satisfy this requirement have already been prepared, and have been found to produce reasonably accurate output. The tokenisation engine is, however, capable of processing a wider range of input strings and when required concatenation units are found to be unavailable, returns a report via the user interface.

This paper discusses the process of part-of-speech tagging the Corpus of Early English Correspondence Extension (CEECE), as well as the end result. The process involved normalisation of historical spelling variation, conversion from a legacy format into TEI-XML, and finally, tokenisation and tagging by the CLAWS software. At each stage, we had to face and work around problems such as whether to retain original spelling variants in corpus markup, how to implement overlapping hierarchies in XML, and how to calculate the accuracy of tagging in a way that acknowledges errors in tokenisation. The final tagged corpus is estimated to have an accuracy of 94.5 per cent (in the C7 tagset), which is circa two percentage points (pp) lower than that of present-day corpora but respectable for Late Modern English. The most accurate tag groups include pronouns and numerals, whereas adjectives and adverbs are among the least accurate. Normalisation increased the overall accuracy of tagging by circa 3.7pp. The combination of POS tagging and social metadata will make the corpus attractive to linguists interested in the interplay between language-internal and -external factors affecting variation and change.

Word embeddings are representations of words in a vector space that models semantic relationships between words by means of distance and direction. In this study, we adapted two existing methods, word2vec and fastText, to automatically learn Welsh word embeddings taking into account syntactic and morphological idiosyncrasies of this language. These methods exploit the principles of distributional semantics and, therefore, require a large corpus to be trained on. However, Welsh is a minoritised language, hence significantly less Welsh language data are publicly available in comparison to English. Consequently, assembling a sufficiently large text corpus is not a straightforward endeavour. Nonetheless, we compiled a corpus of 92,963,671 words from 11 sources, which represents the largest corpus of Welsh. The relative complexity of Welsh punctuation made the tokenisation of this corpus relatively challenging as punctuation could not be used for boundary detection. We considered several tokenisation methods including one designed specifically for Welsh. To account for rich inflection, we used a method for learning word embeddings that is based on subwords and, therefore, can more effectively relate different surface forms during the training phase. We conducted both qualitative and quantitative evaluation of the resulting word embeddings, which outperformed previously described word embeddings in Welsh as part of larger study including 157 languages. Our study was the first to focus specifically on Welsh word embeddings.

The aim of the paper is to provide a quantitative description of legal Chinese. This study adopts the approach of corpus-based analyses and it shows basic statistical parameters of legal texts in Chinese, namely the length of a sentence, the proportion of part of speech etc. The research is conducted on the Chinese monolingual corpus Hanku. The paper also discusses the issues of statistical data processing from various corpora, e.g. the tokenisation and part of speech tagging and their relevance to study of registers variation.

AbstractWe describe an opinion mining system which classifies the polarity of Spanish texts. We propose an NLP approach that undertakes pre-processing, tokenisation and POS tagging of texts to then obtain the syntactic structure of sentences by means of a dependency parser. This structure is then used to address three of the most significant linguistic constructions for the purpose in question: intensification, subordinate adversative clauses and negation. We also propose a semi-automatic domain adaptation method to improve the accuracy of our system in specific application domains, by enriching semantic dictionaries using machine learning methods in order to adapt the semantic orientation of their words to a particular field. Experimental results are promising in both general and specific domains.

Using part-of-speech (POS) tagged corpora, Hudson (1994) reports that approximately 37% of English tokens are nouns, where ‘noun’ is a superordinate category including nouns, pronouns and other word-classes. It is argued here that difficulties relating to the boundaries of Hudson’s ‘noun’ category demonstrate that there is no uncontroversial way to derive such a superordinate category from POS tagging. Decisions regarding the boundary of the ‘noun’ category have small but statistically significant effects on the ratio that emerges for ‘nouns’ as a whole. Tokenisation and categorisation differences between tagging schemes make it problematic to compare the ratio of ‘nouns’ across different tagsets. The precise figures for POS ratios are therefore effectively artefacts of the tagset. However, these objections to the use of POS ratios do not apply to their use as a metric of variation for comparing datasets tagged with the same tagging scheme.

Much like initial public offerings produce publicly traded securities, Initial Coin Offerings (icos) produce crypto tokens tradeable on crypto exchanges. Despite an apparent need for investor protection the ico and the tokenisation phenomenon have yet to be addressed by legislative action on the EU level. The paper studies the suitability of the EU regulatory framework to capture tokenised financial instruments and utility tokens based on the views of the EU supervisory and national competent authorities. It is argued that EU regulators shall first ensure legal certainty by defining the scope of tokenised financial instruments subject to MiFID. Further, authorisation and ongoing requirements shall be adapted to address the risks posed by distributed technology and direct global access of investors to crypto markets. Finally, there is no immediate need for a bespoke EU-wide regime governing utility tokens; fragmentation of the market is a positive development providing a testing field for future supranational initiatives.

This article argues that whilst our recent economic models are dependent on the overall ecosystem, they do not reflect this fact. As a result of this, we are facing Anthropocene mass extinction. The paper presents a collaborative regenerative region (COLreg) co-creation and tokenisation, involving multiple human and non-human, living and non-living stakeholders. It unfolds different stages of multicentred, systemic co-design via collaborative gigamapping. In the first steps, certain stakeholders are present and certain are represented, whilst in the final stages of generative development, all stakeholders, even those who were previously just potential stakeholders, take an active role. The ‘COLreg’ project represents a holistic approach that reflects today’s most burning issues, such as biodiversity decrease, unsustainable food production, unsustainable economic models, and social systems. It combines top-down and bottom-up approaches to co-create to achieve regional social and environmental justice for the coming symbiotic post-Anthropocene era.

The PARC 700 dependency bank is a potentially very useful resource for parser evaluation that has, so to speak, a high barrier to entry, because of tokenisation that is quite different from the source of the data, the Penn Treebank, and because there is no representation of word order, producing an uncertainty factor of some 15%. There is also a small, but perhaps not insignificant, number of errors. When using the dependency bank for evaluation, it seems likely that these things will cause inflated counts for mismatches, so to obtain more accurate measurements, it is desirable to eliminate them. The work reported here consists of an automatic conversion of the dependency bank into a Prolog representation where the word order is explicit, as well as graphical representations of the dependency trees for all 700 sentences, automatically generated from the Prolog data. As a side effect of the transformation, errors were detected and corrected. It is hoped that this work will lead to more widespread use of the PARC 700 dependency bank for parser evaluation.

The performance evaluation of an information retrieval system is a decisive aspect of the measure of the improvements in search technology. The Google search engine, as a tool for retrieving information on the Web, is used by almost 92% of Iranian users. The purpose of this paper is to study Google’s performance in retrieving relevant information from Persian documents. The information retrieval effectiveness is based on the precision measures of the search results done to a website that we have built with the documents of a TREC standard corpus. We asked Google for 100 topics available on the corpus and we compared the retrieved webpages with the relevant documents. The obtained results indicated that the morphological analysis of the Persian language is not fully taken into account by the Google search engine. The incorrect text tokenisation, considering the stop words as the content keywords of a document and the wrong ‘variants encountered’ of words found by Google are the main reasons that affect the relevance of the Persian information retrieval on the Web for this search engine.

Summary Information Retrieval (IR) plays a central role in the exploration and interpretation of integrated biological datasets that represent the heterogeneous ecosystem of life sciences. Here, keyword based query systems are popular user interfaces. In turn, to a large extend, the used query phrases determine the quality of the search result and the effort a scientist has to invest for query refinement. In this context, computer aided query expansion and suggestion is one of the most challenging tasks for life science information systems. Existing query front-ends support aspects like spelling correction, query refinement or query expansion. However, the majority of the front-ends only make limited use of enhanced IR algorithms to implement comprehensive and computer aided query refinement workflows. In this work, we present the design of a multi-stage query suggestion workflow and its implementation in the life science IR system LAILAPS. The presented workflow includes enhanced tokenisation, word breaking, spelling correction, query expansion and query suggestion ranking. A spelling correction benchmark with 5,401 queries and manually selected use cases for query expansion demonstrate the performance of the implemented workflow and its advantages compared with state-of-the-art systems.

The election of Governor is an election event for the Regional Head for the future of the region and the country. The Central Java Governor election in 2018 was held jointly on 27 June 2018, which was followed by 2 candidate pairs of the governor. Its many responses from people through twitter's social media to bring up opinions from the public. Sentiment analysis of 2 research objects of Central Java Governor 2018 candidates with a total of 400 tweets with each candidate being 200 tweets. The used of tweets are divided into 3 classes: positive class, neutral class and negative class. In this study the classification process used the Naive Bayes Classifier (NBC) method, while for data preprocessing is using Cleansing, Punctuation Removal, Stopword Removal, and Tokenisation, to determine the sentiment class with the Lexicon Based method produces the highest accuracy in the Ganjar Pranowo dataset with an accuracy of 87,9545%, Precision value is 0.891%, Recall value is 0.88% and F-Measure is 0.851% while Sudirman Said dataset has an accuracy rate of 84.322%, Precision value of 0.867%, Recall value of 0.843% and F-Measure of 0.815%. From these results, we can conclude that the Ganjar Pranowo dataset was higher compared to Sudirman Said's dataset.

AbstractThe study of speech timing, i.e. the duration and speed or tempo of speech events, has increased in importance over the past twenty years, in particular in connection with increased demands for accuracy, intelligibility and naturalness in speech technology, with applications in language teaching and testing, and with the study of speech timing patterns in language typology. H owever, the methods used in such studies are very diverse, and so far there is no accessible overview of these methods. Since the field is too broad for us to provide an exhaustive account, we have made two choices: first, to provide a framework of paradigmatic (classificatory), syntagmatic (compositional) and functional (discourse-oriented) dimensions for duration analysis; and second, to provide worked examples of a selection of methods associated primarily with these three dimensions. Some of the methods which are covered are established state-of-the-art approaches (e.g. the paradigmatic Classification and Regression Trees, CART , analysis), others are discussed in a critical light (e.g. so-called ‘rhythm metrics’). A set of syntagmatic approaches applies to the tokenisation and tree parsing of duration hierarchies, based on speech annotations, and a functional approach describes duration distributions with sociolinguistic variables. Several of the methods are supported by a new web-based software tool for analysing annotated speech data, the Time Group Analyser.

<span>Novel coronavirus (COVID-19) is a newly discovered infectious disease that has received much attention in the literature because of its rapid spread and daily global deaths attributable to such disease. The White House, together with a coalition of leading research groups, has published the freely available COVID-19 Open Research Dataset to help the global research community apply the recent advances in natural language processing and other AI techniques in generating novel insights that can support the ongoing fight against this disease. In this paper, the hierarchical and k-means clustering techniques are used to create a tool for identifying similar articles on COVID-19 and filtering them based on their titles. These articles are classified by applying three data mining techniques, namely, random forest (RF), decision tree (DT) and bagging. By using this tool, specialists can limit the number of articles they need to study and pre-process these articles via data framing, tokenisation, normalisation and term frequency-inverse document frequency. Given its 2D nature, the dimensionality of this dataset is reduced by applying t-SNE. The aforementioned data mining techniques are then cross validated to test the accuracy, precision and recall performance of the proposed tool. Results show that the proposed tool effectively extracts the keywords for each cluster, with RF, DT and bagging achieving optimal accuracies of 98.267%, 97.633% and 97.833%, respectively.</span>

Idioms are multi-word expressions whose meaning cannot always be deduced from the literal meaning of constituent words. A key feature of idioms that is central to this paper is their peculiar mixture of fixedness and variability, which poses challenges for their retrieval from large corpora using traditional search approaches. These challenges hinder insights into idiom usage, affecting users who are conducting linguistic research as well as those involved in language education. To facilitate access to idiom examples taken from real-world contexts, we introduce an information retrieval system designed specifically for idioms. Given a search query that represents an idiom, typically in its canonical form, the system expands it automatically to account for the most common types of idiom variation including inflection, open slots, adjectival or adverbial modification and passivisation. As a by-product of query expansion, other types of idiom variation captured include derivation, compounding, negation, distribution across multiple clauses as well as other unforeseen types of variation. The system was implemented on top of Elasticsearch, an open-source, distributed, scalable, real-time search engine. Flexible retrieval of idioms is supported by a combination of linguistic pre-processing of the search queries, their translation into a set of query clauses written in a query language called Query DSL, and analysis, an indexing process that involves tokenisation and normalisation. Our system outperformed the phrase search in terms of recall and outperformed the keyword search in terms of precision. Out of the three, our approach was found to provide the best balance between precision and recall. By providing a fast and easy way of finding idioms in large corpora, our approach can facilitate further developments in fields such as linguistics, language education and natural language processing.

Communities of lesser resourced languages like North Sámi benefit from language tools such as spell checkers and grammar checkers to improve literacy. Accurate error feedback is dependent on well-tokenised input, but traditional tokenisation as shallow preprocessing is inadequate to solve the challenges of real-world language usage. We present an alternative where tokenisation remains ambiguous until we have linguistic context information available. This lets us accurately detect sentence boundaries, multiwords and compound error detection. We describe a North Sámi grammar checker with such a tokenisation system, and show the results of its evaluation.

In everybody’s life including the organisations, database plays a very important role, since today everything is connected via the Internet. There is a need for a database that helps organisations to organise, sort and manage the data and ensure that the data a user receives and sends via the database mean is secure, since the database stores almost everything such as banking details including user ID and password. Make this data really valuable and confidential for us and therefore security is really important for the database. In this age, SQL Injection database attacks are increasingly common. The hackers attempt to steal an individual’s valuable data through the SQL Injection Attack mean by using malicious query on the application, hence revealing an efficient individual data. Therefore the best SQL Injection Prevention technique is needed to safeguard individual data against hackers being stolen. This paper compares two types of SQL Injection using the SQL pattern matching database system attack (SQLPMDS) and a SQL injection union query attacks prevention using tokenisation technique (SIUQAPTT) that allows Database Administrator to select the best and most effective SQL Injection Prevention method for their organisation. Preventing SQL Injection Attack from occurring that would ultimately lead to no user data loss. The results were obtained by comparing it to the results of the SQL injection attack query on whether the attack was blocked or not by two prevention techniques, SQL pattern matching database system attacks and SQL injecting union query attacks prevention using website tokenisation techniques. The conclusion is that the best method of prevention is the SQL pattern that matches database system attacks.

AbstractThe COVID-19 pandemic has made clear the extreme needs of the public health workforce. As societies discuss how to build up the capacity and infrastructure of their systems, it is crucial that young professionals are involved. Previous attempts to incorporate young professionals into the public health workforce have wrestled with inaccessibility, tokenisation, and a lack of mentorship, leading to a loss of potential workforce members and a non-representative workforce that reinforces systemic societal exclusion of diverse young people. These barriers must be addressed through robust mentorship structures, intentional recruitment and continuous support, as well as genuine recognition of the contributions of young professionals to build the sustainable, interdisciplinary, unified public health that is necessary for the future.

Abstract The present study aims to explore the applicability of automatic analysis to L2-Korean learner corpora, with a special focus on learners’ use of a clause-level construction. For this purpose, we investigate L1-Mandarin L2-Korean learners’ written production of two passive construction types in Korean – suffixal and periphrastic – by devising a pattern-extraction process through NLP techniques. We focus on reporting how the passive constructions are identified and extracted from learner writing automatically, given language-specific features involving the passive. A total of 72 essays were analysed by adapting an existing pipeline (developed by Shin, forthcoming), with enhanced tokenisation and annotation through manual revision of the data. Results showed that our automatic pattern-finder identified more instances than manual extraction for the suffixal passive and yielded a perfect match with manual extraction for the periphrastic passive. Implications of the findings are discussed in regard to strengths and drawbacks of the automatic analysis of learner writing, with suggestions for improving currently available tools for learner corpus research in Korean.

The L Word: Generation Q is the reboot of The L Word, a long running series about a group of lesbians and bisexuals in Los Angeles in the early 2000s. Both programmes are unique in their positioning of lesbian characters and have been well received by audiences and critics alike. These programmes present a range of characters and narratives, previously excluded from mainstream film and television, bringing a refreshing change from the destructive images typically presented before. We argue that the reboot Generation Q now offers more meaningful representation of the broader lesbian and transgender communities, and discuss its relevance in the changing portrayals of gay representation. Gay visibility has never really been an issue in the movies. Gays have always been visible. It is how they have been visible that has remained offensive for almost a century. (Russo 66) In 2004 The L Word broke new ground as the very first television series written and directed by predominantly queer women. This set it apart from previous representations of lesbians by Hollywood because it portrayed a community rather than an isolated or lone lesbian character, that was extraneous to a cast of heterosexuals (Moore and Schilt). The series brought change, and where Hollywood was more often “reluctant to openly and non-stereotypically engage with gay subjects and gay characters” (Baker 41), the L Word offered an alternative to the norm in media representation. “The L Word’s significance lies in its very existence” according to Chambers (83), and this article serves to consider this significance in conjunction with its 2019 reboot, the L Word: Generation Q, to ascertain if the enhanced visibility and gay representation influences the system of representation that has predominantly been excluding and misrepresentative of gay life. The exclusion of authentic representation of lesbians and gays in Hollywood film is not new. Over time, however, there has been an increased representation of gay characters in film and television. However, beneath the positive veneer remains a morally disapproving undertone (Yang), where lesbians and gays are displayed as the showpiece of the abnormal (Gross, "Out of the Mainstream"). Gross ("Out of the Mainstream") suggests that through the ‘othering’ of lesbians and gays within media, a means of maintaining the moral order is achieved, and where being ‘straight’ results in a happy ending. Lesbians and gays in film thus achieve what Gerbner referred to as symbolic annihilation, purposefully created in a bid to maintain the social inequity. This form of exclusion often saw controversial gay representation, with a history of portraying these characters in a false, excluding, and pejorative way (Russo; Gross, "What Is Wrong"; Hart). The history of gay representation in media had at times been monstrous, playing out the themes of gay sexuality as threatening to heterosexual persons and communities (Juárez). Gay people were incorrectly stereotyped, and gay lives were seen through the slimmest of windows. Walters (15) argued that it was “too often” that film and television images would narrowly portray gays “as either desexualized or over sexualized”, framing their sexuality as the sole identity of the character. She also contested that gay characters were “shown as nonthreatening and campy 'others' or equally comforting and familiar boys (and they are usually boys, not girls) next door” (Walters 15). In Russo’s seminal text, The Celluloid Closet, he demonstrated that gay characters were largely excluded from genuine and thoughtful presentation in film, while the only option given to them was how they died. Gay activists and film makers in the 1980s and beyond built on the momentum of AIDS activism (Streitmatter) to bring films that dealt with gay subject matter more fairly than before, with examples like The Birdcage, Philadelphia, To Wong Foo, Thanks for Everything! Julie Newmar, and In and Out. Walters argues that while “mainstream films like Brokeback Mountain and The Kids are Alright entertain moviegoers with their forthright gay themes and scenes” (12), often the roles have been more of tokenisation, representing the “surprisingly gay characters in a tedious romcom, the coyly queer older man in a star-studded indie hit, the incidentally gay sister of the lead in a serious drama” (Walters 12). This ambivalence towards the gay role model in the media has had real world effects on those who identify themselves as lesbian or gay, creating feelings of self-hatred or of being ‘unacceptable’ citizens of society (Gamson), as media content “is an active component in the cultural process of shaping LGBT identities” (Sarkissian 147). The stigmatisation of gays was further identified by the respondents to a study on media and gay identity, where “the prevailing sentiment in these discussions was a sense of being excluded from traditional society” (Gomillion and Guiliano 343). Exclusion promotes segregation and isolation, and since television media are ever-present via conventional and web-based platforms, their messages are increasingly visible and powerful. The improved portrayal of gay characters was not just confined to the area of film and television however, and many publications produced major stories on bi-sexual chic, lesbian chic, the rise of gay political power and gay families. This process of greater inclusion, however, has not been linear, and in 2013 the media advocacy group known as the Gay and Lesbian Alliance against Defamation (GLAAD) mapped the quantity, quality, and diversity of LGBT people depicted in films, finding that there was still much work to be done to fairly include gay characters (GLAAD Studio Responsibility Index). In another report made in 2019, which examined cable and streaming media, GLAAD found that of the 879 regular characters expected to appear on broadcast scripted primetime programming, 10.2% were identified as gay, lesbian, bisexual, transgender and or queer (GLAAD Where Are We on TV). This was the highest number of queer characters recorded since the start of their reporting. In January 2004, Showtime launched The L Word, the first scripted cable television to focus chiefly on lesbians. Over the course of six seasons it explored the deep bonds that linked the members of an evolving lesbian friendship circle. The central themes of the programme were the love and friendship between the women, and it was a television programme structured by its own values and ideologies. The series offered a moral argument against the widespread sexism and anti-gay prejudice that was evident in media. The cast, however, were conventionally beautiful, gender normative, and expensively attired, leading to fears that the programme would appeal more to straight men, and that the sex in the programme would be exploitative and pornographic. The result, however, was that women’s sex and connection were foregrounded, and appeared as a central theme of the drama. This was, however, ground-breaking television. The showrunner of the original L Word, Ilene Chaiken, was aware of the often-damning account of lesbians in Hollywood, and the programme managed to convey an indictment of Hollywood (Mcfadden). The L Word increased lesbian visibility on television and was revolutionary in countering some of the exclusionary and damaging representation that had taken place before. It portrayed variations of lesbians, showing new positive representations in the form of power lesbians, sports lesbians, singles, and couples. Broadly speaking, gay visibility and representation can be marked and measured by levels of their exclusion and inclusion. Sedgwick said that the L Word was particularly important as it created a “lesbian ecology—a visible world in which lesbians exist, go on existing, exist in forms beyond the solitary and the couple, sustain and develop relations among themselves of difference and commonality” (xix). However, as much as this programme challenged the previous representations it also enacted a “Faustian bargain because television is a genre which ultimately caters to the desires and expectations of mainstream audiences” (Wolfe and Roripaugh 76). The producers knew it was difficult to change the problematic and biased representation of queer women within the structures of commercial media and understood the history of queer representation and its effects. Therefore, they had to navigate between the legitimate desire to represent lesbians as well as being able to attract a large enough mainstream audience to keep the show commercially viable. The L Word: Generation Q is the reboot of the popular series, and includes some of the old cast, who have also become the executive producers. These characters include Bette Porter, who in 2019 is running for the office of the Mayor of Los Angeles. Shane McCutchen returns as the fast-talking womanising hairdresser, and Alice Pieszecki in this iteration is a talk show host. When interviewed, Jennifer Beals (executive producer and Bette Porter actor) said that the programme is important, because there have been no new lesbian dramas to follow after the 2004 series ended (Beals, You Tube). Furthermore, the returning cast members believe the reboot is important because of the increased attacks that queer people have been experiencing since the election of Donald Trump in 2016. Between the two productions there have been changes in the film and television landscape, with additional queer programmes such as Pose, Orange Is the New Black, Euphoria, RuPaul’s Drag Race, and Are You the One, for example. The new L Word, therefore, needed to project a new and modern voice that would reflect contemporary lesbian life. There was also a strong desire to rectify criticism of the former show, by presenting an increased variation of characters in the 2019 series. Ironically, while the L Word had purposefully aimed to remove the negativity of exclusion through the portrayal of a group of lesbians in a more true-to-life account, the limited character tropes inadvertently marginalised other areas of lesbian and queer representation. These excluded characters were for example fully representative trans characters. The 2000s television industry had seemingly returned to a period of little interest in women’s stories generally, and though queer stories seeped into popular culture, there was no dedicated drama with a significant focus on lesbian story lines (Vanity Fair). The first iteration of The L Word was aimed at satisfying lesbian audiences as well as creating mainstream television success. It was not a tacky or pornographic television series playing to male voyeuristic ideals, although some critics believed that it included female-to-female sex scenes to draw in an additional male viewership (Anderson-Minshall; Graham). There was also a great emphasis on processing the concept of being queer. However, in the reboot Generation Q, the decision was made by the showrunner Marja-Lewis Ryan that the series would not be about any forms of ‘coming out stories’, and the characters were simply going about their lives as opposed to the burdensome tropes of transitioning or coming out. This is a significant change from many of the gay storylines in the 1990s that were seemingly all focussed on these themes. The new programme features a wider demographic, too, with younger characters who are comfortable with who they are. Essentially, the importance of the 2019 series is to portray healthy, varied representations of lesbian life, and to encourage accurate inclusion into film and television without the skewed or distorted earlier narratives. The L Word and L Word: Generation Q then carried the additional burden of countering criticisms The L Word received. Roseneil explains that creating both normalcy and belonging for lesbians and gays brings “cultural value and normativity” (218) and removes the psychosocial barriers that cause alienation or segregation. This “accept us” agenda appears through both popular culture and “in the broader national discourse on rights and belongings” (Walters 11), and is thus important because “representations of happy, healthy, well integrated lesbian and gay characters in film or television would create the impression that, in a social, economic, and legal sense, all is well for lesbians and gay men” (Schacter 729). Essentially, these programmes shouldered the burden of representation for the lesbian community, which was a heavy expectation. Critiques of the original L Word focussed on how the original cast looked as if they had all walked out of a high-end salon, for example, but in L Word: Generation Q this has been altered to have a much more DIY look. One of the younger cast members, Finlay, looks like someone cut her hair in the kitchen while others have styles that resemble YouTube tutorials and queer internet celebrities (Vanity Fair). The recognisable stereotypes that were both including and excluding have also altered the representation of the trans characters. Bette Porter’s campaign manager, for example, determines his style through his transition story, unlike Max, the prominent trans character from the first series. The trans characters of 2019 are comfortable in their own skins and supported by the community around them. Another important distinction between the representation of the old and new cast is around their material wealth. The returning cast members have comfortable lives and demonstrate affluence while the younger cast are less comfortable, expressing far more financial anxiety. This may indeed make a storyline that is closer to heterosexual communities. The L Word demonstrated a sophisticated awareness of feminist debates about the visual representation of women and made those debates a critical theme of the programme, and these themes have been expanded further in The L Word: Generation Q. One of the crucial areas that the programme/s have improved upon is to denaturalise the hegemonic straight gaze, drawing attention to the ways, conventions and techniques of reproduction that create sexist, heterosexist, and homophobic ideologies (McFadden). This was achieved through a predominantly female, lesbian cast that dealt with stories amongst their own friend group and relationships, serving to upend the audience position, and encouraging an alternative gaze, a gaze that could be occupied by anyone watching, but positioned the audience as lesbian. In concluding, The L Word in its original iteration set out to create something unique in its representation of lesbians. However, in its mission to create something new, it was also seen as problematic in its representation and in some ways excluding of certain gay and lesbian people. The L Word: Generation Q has therefore focussed on more diversity within a minority group, bringing normality and a sense of ‘realness’ to the previously skewed narratives seen in the media. In so doing, “perhaps these images will induce or confirm” to audiences that “lesbians and gay men are already ‘equal’—accepted, integrated, part of the mainstream” (Schacter 729). 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