Modern Irish is a minority language lacking sufficient computational resources for the task of accurate automatic syntactic parsing of user-generated content such as tweets. Although language technology for the Irish language has been developing in recent years, these tools tend to perform poorly on user-generated content. As with other languages, the linguistic style observed in Irish tweets differs, in terms of orthography, lexicon, and syntax, from that of standard texts more commonly used for the development of language models and parsers. We release the first Universal Dependencies treebank of Irish tweets, facilitating natural language processing of user-generated content in Irish. In this paper, we explore the differences between Irish tweets and standard Irish text, and the challenges associated with dependency parsing of Irish tweets. We describe our bootstrapping method of treebank development and report on preliminary parsing experiments. Irish is a minority language spoken mostly in small communities in Ireland called Gaeltachta' (CSO, 2016) but social media sites, such as Twitter, provide a platform for Irish speakers to communicate electronically from any location. Users may reach a wide audience quickly, unconstrained by the conventions of standard language upheld by editors in publications, revealing the orthographic, lexical, and syntactic variation abundant in informal Irish. Analysis of up-to-date, real-world language data can provide an insight into how Irish is used in everyday communication and how such informal texts compare to prescriptive norms of standardised language to which published texts tend to adhere. User-generated content (UGC), such as tweets, is a valuable, highly available resource for training syntactic parsers that can accurately process social media text. UGC is a genre with features different from those of both spoken language and standardised written language more traditionally found in natural language processing (NLP) corpora. Plank (2016) notes the advantages of utilising fortuitous data in order to create more adaptive, robust language technology. Given that the accuracy of syntactic parsing tools has been shown to decline when evaluated on noisy UGC data (Foster et al., 2011; Seddah et al., 2012) and that domain 1 adaptation has been shown to improve parser performance for dependency annotation of English tweets (Kong et al., 2014) and POS-tagging in Irish tweets (Lynn et al., 2015), the need for genre-specific resources is clear in order to reliably process this variety of data. The prerequisite, therefore, for research in this area is a data set of Irish UGC. This research attempts to fill this gap through the development of TwittIrish, a treebank of Irish tweets, within Universal Dependencies (UD) (Nivre et al., 2020), a cross-lingually consistent framework for dependency-based syntactic parsing. TwittIrish provides linguistic information for Irish in a digitally accessible format valuable for linguistic research and the development of NLP tools. Open-source projects such as UD facilitate collaboration and rapid evolution of ideas among linguists internationally. In order to maintain optimum consistency with other UD treebanks, the annotation methodology employed in this research closely follows the general UD guidelines and the language-specific guidelines for Irish while aiming to incorporate the most up-to-date recommendations (Sanguinetti et al., 2022) for UGC in this evolving area of NLP. UGC, especially social media text, has recently become a popular focus within UD and NLP research more broadly (Silveira et al., 2014; Luotolahti et al., 2015; Albogamy and Ram-1 The terms genre and domain are used interchangeably throughout this paper to refer to the category of text such as standard published text or Twitter text. 6869 say, 2017; Wang et al., 2017; Zeldes, 2017; Bhat et al., 2018; Blodgett et al., 2018; Van Der Goot and van Noord, 2018; Cignarella et al., 2019; Seddah et al., 2020) and has encouraged active conversation around how best to represent it within this framework among the UD community. We carry out preliminary parsing experiments with TwittIrish, investigating the following two questions: How effective is a parser trained on the Irish UD Treebank (Lynn and Foster, 2016), which contains only edited text and no UGC, when applied to tweets? And what difference do pretrained contextualised word embeddings make? We observe a difference of approximately 23 LAS points between TwittIrish and the IUDT test set and find that the use of monolingual BERT embeddings (Barry et al., 2021) improves performance by over 10 LAS points. The paper is structured as follows: Section 2 details the existing Irish NLP resources we use for our research, Section 3 outlines the development of the treebank, Section 4 describes the characteristics of UGC evident in Irish tweets, and Section 5 presents parsing experiments and error analysis. Indigenous Tweets (IT) 2 This project compiles statistics on social media data of 185 minority and indigenous languages including Irish. All tweets in the TwittIrish treebank were sourced via IT. Lynn Twitter Corpus (LTC) 3 (Lynn et al., 2015) A corpus of 1,493 lemmatised and POS-tagged Irish language tweets randomly sampled from 950k tweets by 8k users posted between 2006 and 2014, identified by IT. The LTC data also contains code-switching information (Lynn and Scannell, 2019). Irish Universal Dependencies Treebank (IUDT) 4 (Lynn and Foster, 2016) A UD treebank consisting of 4,910 sentences sampled from a balanced mixed-domain corpus for Irish. gaBERT (Barry et al., 2021) A monolingual Irish BERT model, trained on approximately 7.9 million sentences, which outperforms Multilingual 2 http://indigenoustweets.com/ 3 https://github.com/tlynn747/ IrishTwitterPOS 4 https://github.com/ UniversalDependencies/UD_Irish-IDT BERT (mBERT) (Devlin et al., 2019) and WikiBERT (Pyysalo et al., 2021) at the task of dependency parsing for Irish. We combined 700 POS-tagged tweets from the LTC with 166 tweets more recently crawled by IT in order to leverage previous linguistic annotations while also including newer tweets. This involved converting the LTC annotation scheme to that of the UD framework and then POS-tagging the new raw tweets. We provide further detail in Appendix A. LTC conversion With regard to tokenisation, multiword expressions were automatically split into separate tokens following UD conventions. Only minor manual adjustments were required for lemmatisation to ensure alignment with the IUDT (to enable bootstrapping  see Section 3). Finally, the POS tagset used in the LTC was automatically converted to the UD tagset. Appendix A.2 describes this process. Preprocessing of newly-crawled tweets Due to the lack of a tokeniser designed to deal specifically with UGC in Irish, we compared two tools for this task: UDPipe (Straka et al., 2016), 5 a language-agnostic trainable pipeline for tokenisation, tagging, lemmatisation and dependency parsing, and Tweettokenizer 6 from NLTK (Bird et al., 2009), a rule-based tokeniser designed for noisy UGC. The latter proved to be more effective for tokenising UGC phenomena such as emoticons, URLs, and meta language tags. Manual corrections were then applied in order to adhere to the Irish-specific tokenisation scheme within current UD guidelines. In order to establish the best system to use for automatic lemmatising and POS-tagging, two tools, Morfette (Chrupala et al., 2008) and UDPipe (Straka et al., 2016), were analysed with Morfette achieving higher scores on both tasks. Syntactic annotation As a method shown to reduce manual annotation efforts in syntactic annotation (Judge et al., 2006; Seraji et al., 2012), we carry out a bootstrapping approach to dependency parsing as recommended by UD. 7 The bootstrapping process is illustrated in Figure 1. After converting the LTC and new tweets 5 Trained on IUDT v2.8 with no pre-trained embeddings. to the CoNLL-U format, we manually annotated a small set of 166 tweets and began the bootstrapping cycle. 8 (Step 1) A parsing model 9 was trained on the IUDT in combination with the newly annotated tweets. (Step 2) The parsing model was used to automatically annotate the next batch of 100 tweets. (Step 3) These tweets were manually corrected. (Step 4) The corrected tweets were added to the training data. Steps 1 to 4 were repeated until all 866 tweets were fully parsed. This dataset represents the TwittIrish test set in the UD version 2.8 release. 10 4 Annotating Irish UGC This section describes the linguistic features that can create challenges when parsing Irish social media text. We provide Irish examples and discussion around the factors that influence these phenomena. Orthographic variation refers to deviation from the conventional spelling system of the language and is observed at the token level. Therefore, it can affect the lemmatisation of a token in an NLP pipeline, potentially affecting other downstream areas of annotation. In the TwittIrish dataset, 2.5% of tokens contained some orthographic variation. Table 1 exemplifies some frequently-occurring phenomena in Irish tweets that deviate from standard orthography. 8 Due to the limited funding available, all manual annotation and correction was performed by one linguist annotator. 9 Biaffine Parser (Dozat and Manning, 2017) with mBERT (Devlin et al., 2019) embeddings. Diacritic variation Diacritic marks are often omitted or incorrectly added to tweets. The acute accent or sneadh fada is used in Irish to indicate a long vowel and is necessary to disambiguate between certain words. Example 1 shows the most probable intended word lacht lecture' rendered as leacht liquid'. Abbreviation Predictable shorthand forms can occur in standard Irish texts e.g. lch as an abbreviated form of leathanach page'. While more unconventional, and thus less predictable, abbreviations are observed in Irish tweets, as per Example 2 in which the word seachtain week' is shortened to seacht seven'. Abbreviations are more common in tweets than standard text as the character limit and real-time, up-to-date nature of the platform encourages the user to be efficient with time and space. (2) Bm de ghnth ach sa bhaile an tseacht seo I usually am but home this week' Lengthening This refers to the elongation of a token by repeating one or more characters. This can be thought of as an encoding of sociophonetic information (Tatman, 2015) and is strongly linked to sentiment. Despite incentives to save time and space while tweeting, users often elongate certain words for expressive purposes (Brody and Diakopoulos, 2011). Example 3 demonstrates the lengthening of the word bu yellow'. Case variation Nonstandard use of upperand lowercase text is another method of encoding sociophonetic information by focusing attention or emotion on a particular word or phrase. Heath (2021) discusses the association between the use of all-caps and perceived shouting as in Example 4. (4) Nl todhcha na Gaeilge sa Ghaeltacht, ach in aon it AR DOMHAIN The future of Irish is not in the Gaeltacht but anywhere ON EARTH' 6871 Phenomenon Example Standard form Gloss Diacritic variation nior fhoghlaim tu nor fhoghlaim t you did not learn' Abbreviation fhoir rugba na hir fhoireann rugba na hireann Irish rugby team' Lengthening obairrrr obair work' Case variation ceolchoirm DEN SCOTH ceolchoirm den scoth excellent concert' Punctuation Variation ** folntas ** folntas vacancy' Transliteration go wil go bhfuil that is' Other spelling variation O ' Bama Obama Obama' Table 1: Examples of orthographic variation in Irish tweets. Transliteration The practice of transliteration, in which a word in one language is written using the writing system of another, is common within the language pair of Irish and English. In the TwittIrish treebank, the English language phrase fair play' occurs twice while variations fair pl', as shown in Example 5 and far pl' occur once each. Punctuation variation Punctuation is used creatively in UGC to format or emphasise strings of text. However, due to the lack of standardis-ation, occurrences of unconventional punctuation can make text difficult to parse for both human and machine, as in Example 6 which shows a phrase from an Irish tweet appended by two punctuation characters -)'. It is unclear whether this should be interpreted as some form of punctuation, creative formatting, or a smiley e.g. :-)'. Other spelling variation These are mostly slight variations very close to the intended word and may occur due to typographical error. Typos are very common in UGC due to lack of editing or proofreading and may occur via insertion, deletion, substitution, or transposition of characters. Example 7 shows sraith (season) rendered as *staith . Due to their phonetic dissimilarity and the fact that t' and r' are adjacent on the QWERTY keyboard layout, it is reasonable to infer that the substitution was unintentional. Less commonly, disguise or censorship of words or phrases may occur to encrypt profanity or taboo language. Just 38.32% of the set of unique lemmata that make up the vocabulary of the TwittIrish treebank occur Dialectal vocabulary Irish has three major dialects; Connaught, Munster, and Ulster. Distinctive features of these dialects in the form of lexical variation are evident in spoken language and informal text such as tweets. Example 8 shows the use of domh , the Ulster variant of dom to me'. Initialism Multiword phrases are frequently represented by the initial letter of each of their constituent tokens. Example 9 shows GRMA Thank you' used to represent its expanded form Go raibh maith agat . Pictogram Emojis, emoticons, etc. can be added to text to emulate gesture (Gawne and McCulloch, 2019) or they may play a syntactic role in a phrase, replacing a word as in Example 10, in which the symbol, , acts as the object of a verb. Pictograms tend not to have a one-to-one correspondence with natural language words. Conas a deireann t ? How do you say ' Truncation Due to the current limit of 280 characters per tweet, the end of a tweet may be unnaturally attenuated, sometimes mid-sentence as in Example 11 or even mid-word. Code-switching vs. borrowing 66.74% of tokens in the TwittIrish treebank are in Irish, 4.85% of tokens are in English and the remainder (con-sisting of punctuation, meta language tags, etc.) are classified as neither, or indeed both in the case of intraword code-switching or nonce borrowing in which the morphologies of two languages are combined in a single word. In Example 12 the English verb root happen' is used instead of the Irish equivalent tarlaigh . Insertional code-switching (Muysken et al., 2000) and borrowing are common in informal Irish. 74.71% of the tweets in the TwittIrish treebank were considered to be entirely in Irish, the remaining 25.29% of tweets being considered bior multilingual. Example 13 shows a section of an Irish tweet utilising the English word Dubs', a nickname for Dubliners', and Example 14 shows the use of an eclipse and an acute accent applied to the foreign proper noun Barcelona'. (12) Eachtra i ndiaidh Happenil An event (is) after happening' (13) Roimh na Dubs Before the Dubs' (14) T sin i mBarcelna That is in Barcelona' Other nonstandard lexical forms Other unfamiliar terms may occur in the form of hypercorrection and neologisms. Hypercorrection occurs when an autocorrection system is either not activated or available in a user's language of choice. As a result, their attempts to type a word are corrected to a word with a similar spelling in another language. Example 15 shows the Irish word coicse rendered as concise' probably due to automatic English spelling correction software. It is often difficult to distinguish between hypercorrection, neologisms, typos, or other spelling variations. Example 16 shows agus (and) rendered as agua which may have occurred due to automatic hypercorrection as agua' (water) is a frequent token in other languages such as Portuguese and Spanish. However, it could also be a simple typo. (15) Mhscail m i mo leaba fin ar maidin i ndiaidh concise I woke up in my own bed after a fortnight' (16) t an teanga ag fil bhis agua the language is dying and' 4.3 Syntactic Variation Grammatical phenomena observed in Irish tweets are described in this section. As these idiosyncrasies occur at the phrasal rather than token level, they may directly affect the structure of the parse tree. Some phenomena, such as contraction and over-splitting, cause difficulty during the tokenisation stage, potentially having a negative downstream effect on parsing. Table 3 exemplifies syntactic variation in Irish tweets. Contraction Much like abbreviation at the token level, contraction is defined here as the fusion of several tokens for the purpose of brevity, sometimes mimicking spoken pronunciation. Figure 2 shows the phrase go bhfuil siad that they are' reduced to gowil siad tokenised incorrectly. Figure 3 shows the same contraction tokenised correctly. Over-splitting The inclusion of extra white space within tokens is often observed in Irish tweets e.g. Nl m r chinnte . The prefix r(too') is conventionally fused with the adjective it precedes in standardised text and so such tokens are annotated with the goeswith label as shown in Figure 4. 6873 Phenomenon Example Standard form Gloss Contraction go dt'n go dt an until the' Over-splitting ana shuimiil an-suimiil very interesting' Syntax-level code-switching T an tweet machine r-tapa T inneall na tvute r-tapa The tweet machine is too fast' Dialectal grammar N fhacthas n fhaca m I did not see' Ellipsis jab iontach danta aige t jab iontach danta aige he has done a wonderful job' Meta language tags #sonas sonas happiness' Non-sentential segmentation haha:) t sil agam go raibh s ann ha ha! Syntax-level code-switching Alternational code-switching or congruent lexicalisation (Muysken et al., 2000) are likely to cause a change in the structure of the syntax tree, due to differing word orders of the languages involved, thus complicating the task of dependency parsing. In Irish, the adjectival modifier usually follows the noun it modifies whereas the inverse is true for English. Figure 5 exemplifies a case of congruent lexicalisation in which English adjective hippy-dippy' is positioned before an Irish noun rather than after as would be expected in classic' code-switching. Dialectal grammar Figures 6 and 7 show semantically equivalent statements rendered using the synthetic, more common to the Munster dialect of Irish, and analytic verb forms respectively. Ellipsis Example 17 shows a sentence fragment lacking a main verb. The probable inferred full phrase is t bisteach anseo rain is here'. Meta language tags Hashtags are used in tweets to render a topic searchable and at-mentions are used to address or refer to another user. Either can play a syntactic role as exemplified in Figure 8. Non-sentential structure In tweets, the sentence is not an appropriate unit of segmentation as frequently non-standard punctuation, or none at all, is used. Figure 9 exemplifies a tweet utilising an emoji instead of punctuation. Other grammatical variation Grammatical variation can also occur via unintentional deviation from conventional spelling or grammar by an L2 Irish speaker. Example 18 shows a grammatically incorrect phrase roughly translating to I have to *going'. In such cases, though the annotator may be able to infer the intended phrase Caithfidh m dul I have to go', no corrections are made by the 6874 annotator to the surface form, however this information can be represented in the annotation via the label CorrectForm as described by Sanguinetti et al. (2022). Additionally, Irish tweets contain extremely unconventional constructions. This can occur in the form of unnatural phrases that have been machine-translated or generated by bots. Example 19 shows an ungrammatical construction that appears to have been translated automatically word by word. A more natural construction might be conas tonna morgiste a fhil How to get a tonne of mortgage'. Some examples of this variety are easy to identify from surrounding context such as links to websites with similar content however, tweets may consist of text alone making it difficult to infer whether the author is human or machine. (18) Caithfidh m ag dul I have to *going' (19) Conas a Faigh tonna de Morgiste *How to get a tonne of mortgage' 5 Parsing Experiments We compare the performance of two widely used neural dependency parsers on the TwittIrish test set, and examine the effect of using pre-trained contextualised word embeddings from a monolingual Irish BERT model (gaBERT). We report parsing performance broken down by sentence/tweet length, UPOS tags, and dependency labels and carry out a manual error analysis. Further information is detailed in Appendix B. 5.1 Parser Comparison We experiment with two neural dependency parsing architectures: UDPipe (Straka et al., 2016), an NLP pipeline that includes a transition-based nonprojective parser, and AllenNLP (Gardner et al., 2018), a biaffine dependency parser with a BiLSTM encoder (Dozat and Manning, 2017). Both systems are trained on IUDT version 2.8 11 and tested on the IUDT and TwittIrish test sets for comparison. Gold standard tokenisation is provided to the models which then predict UPOS tags and dependency relations. As the TwittIrish test set is the only gold annotated treebank of Irish UGC, no UGC is used as training or development data in 11 Models were trained with and without XPOS and feature annotation. The results shown here are without XPOS and features. The addition of XPOS and features constituted a difference of approximately +/-1 LAS. these experiments. We opt to preserve it as a test set so that our results and results of future research in this area will be comparable. To leverage the substantial advances in accuracy achieved in dependency parsing by the use of pretrained contexualised word representations (Che et al., 2018; Kondratyuk and Straka, 2019; Kul-mizev et al., 2019), we use AllenNLP with token representations obtained from the last hidden layer of the gaBERT model (Barry et al., 2021) which are then passed to the biaffine parsing component. Table 4 shows that, when tested on the IUDT version 2.8 test set, UDPipe achieves 70.58 labelled attachment score (LAS). In comparison, UDPipe achieves a much lower LAS of 47.33 on the TwittIrish test set. Similarly to UDPipe, AllenNLP achieves 71.56 LAS on the IUDT test set with a similar decrease of 22.83 points on the TwittIrish test set. The highest accuracy of 84.25 LAS is achieved by gaBERT with a difference of 24.91 points when tested on the TwittIrish test set. The lower accuracy obtained by parsers on the TwittIrish test set is unsurprising given the linguistic differences between the training and test sets. The 10+ LAS improvement provided by the gaBERT embeddings is seen in both test sets. Analysis was carried out on the AllenNLP parser with gaBERT embeddings using Dependable (Choi et al., 2015). LAS by Number of Tokens per Sentence/Tweet The mean sentence length of the IUDT is 23.5 tokens, whereas the mean tweet length in TwittIrish is 17.8. Figure 10 shows that, when tested on the IUDT, parsing accuracy decreases as the length of the sentence increases. The highest accuracy of 87.92 LAS is associated with sentences of 10 6875 Figure 10: LAS broken down by number of tokens per tree achieved by AllenNLP Parser with gaBERT embeddings on the IUDT and TwittIrish test sets. tokens or fewer, and the lowest accuracy is observed in sentences of 40 tokens or more. This is an unsurprising trend as a higher number of tokens increases the probability of longer dependency distances and more complex constructions within a sentence. While the range of scores is smaller and trend less pronounced, the opposite effect is observed when the same parser is tested on TwittIrish, whereby LAS tends to increase as the length of the tweet increases. The highest LAS of 59.97 is associated with tweets of 31 to 40 tokens in length and the lowest accuracy of 53.47 LAS is associated with tweets of 10 tokens or less. This trend is also observed when gaBERT representations are not used, suggesting that, in this case, deep contextualised word embeddings do not cause this effect as observed in (Kulmizev et al., 2019). From manual inspection of the data, we observe that the genre-specific phenomena which challenge the parser such as ellipsis, meta language tags, and URLs, occur in higher proportions in shorter tweets, which would explain this trend. LAS by UPOS and dependency relation We observe a larger proportion of PROPN , SYM , and PUNCT tags in Irish tweets in comparison to standardised Irish text, which contains a higher proportion of NOUN , DET , and ADP tags. This reflects the observations of Rehbein et al. (2019), who compare the distribution of POS tags in four German treebanks. Additionally, we compare the POS tag distribution in treebanks of English (Liu et al., 2018) and Italian (Sanguinetti et al., 2018) tweets to treebanks of standard text in those languages. We similarly observe that symbols, punctuation, and pronouns are more frequent in tweets and that nouns, determiners, and prepositions are more frequent in Figure 11: LAS broken down by UPOS tag achieved by AllenNLP Parser with gaBERT embeddings on the IUDT and TwittIrish test sets. Figure 11 shows LAS associated with each UPOS tag when tested on the IUDT and TwittIrish. LAS is higher when tested on the IUDT for all UPOS tags except CCONJ , ADV , and SYM and in these cases the difference is small (<10 LAS). The most notable differences are X (71.6 LAS), INTJ (51.3 LAS), PROPN (43.5 LAS). These differences are due to 1) the divergent genres of the treebanks e.g. in the TwittIrish treebank the UPOS tag X is used for all non-syntactic hashtags, and PROPN is used for all at-mentions, neither of which occur in the IUDT and 2) differing annotation conventions e.g. in the IUDT, the tag X is used mostly for foreign-language tokens, whereas, in TwittIrish, due to the high proportion of English language tokens, non-Irish words are annotated with their true UPOS tag where the language is known to the annotator. The tag INTJ occurs very rarely in IUDT. However, due to the conversational nature of tweets, phatic expressions and emotional signifiers (not normally present in standard text) are frequent. Our analysis of the dependency relation distribution of standard English, German, and Italian text compared to that of tweets in those languages reveals that the parataxis , vocative , and advmod relations are more frequent in tweets and that the case , det , and nmod relations are more frequent in standard text. We observe that this same effect is present in Irish tweets. Figure 12 shows LAS broken down by dependency relation. The parser obtains higher scores on the IUDT for all dependency relations except xcomp for which it is just one point higher when tested on TwittIrish. The largest differences between the parsing performance on the two test sets are associated with the labels root , vocative , obl:tmod , csubj:cleft , conj , and punct . As regards root and punct , the difference in accuracy could be attributed to the non-sentential nature of tweets. In the IUDT each tree consists of a single sentence, whereas tweets may consist of sentence fragments or indeed several sentences, making root identification and establishing punctuation attachment more complex. csubj:cleft tends to be mislabelled in the absence of the copula which is often elided in standard text. This copula drop occurs even more frequently in tweets, negatively impacting on parsing accuracy. With regard to conj , both nonstandard forms of coordinating conjunctions (e.g. and', +', misspellings etc.) and differing annotation styles between IUDT and TwittIrish lead to attachment errors. As regards obl:tmod and vocative , the respective differences in accuracy are due to the infrequent occurrences in the IUDT of a speaker or author directly addressing someone in the text and references to time (e.g. 5pm), both of which are common occurrences in tweets. Error Analysis In order to assess the effect of the UGC phenomena present in Irish tweets, we analyse the most and least accurate parses as shown in Table 5. Seven tweets (76 tokens) were parsed with LAS between 0 and 5. On investigation, we observed fifteen occurrences of emojis that were most commonly incorrectly labelled punct . The ten English tokens were most commonly attached incorrectly via flat:foreign . The nine (two syntactic) usernames were most commonly mislabelled as root . There were five occurrences of ellipsis in the form of verb omission obfuscating the task of root selection. The three hashtags were most commonly mislabelled as nmod as were the three Phenomenon Easiest Tweets Hardest Tweets Emoji 0 15 English Token 1 9 Username 3 10 Ellipsis 2 5 Hashtag 1 3 RT 0 3 URL 0 3 Spelling variation 2 2 Table 5: Number of occurrences of UGC phenomena where Easiest Tweets' refers to the 7 tweets that were parsed well with LAS between 95 and 100 and Hardest Tweets' refers to the 7 tweets (76 tokens) that were badly parsed with LAS between 0 and 5. URLs. One occurrence of spelling variation in the form of diacritic omission caused the parser to misinterpret the token r our' as ar on' meaning it was mislabelled as case instead of nmod:poss . Seven tweets (89 tokens) were parsed with an accuracy between 95 and 100 LAS. All of these were grammatical, well-formed sentences. There were three usernames and one hashtag all of which were syntactically integrated and so they were parsed correctly. There was one of insertional single-word code-switch which was accurately parsed. There were two occurrences of spelling variation, both in the form of diacritic omission but, as these do not resemble any other words, they were parsed correctly. Presented in this paper is the novel resource, TwittIrish, the first Universal Dependencies treebank for Irish UGC. Analysis of this linguistic genre and anonymised examples of Irish tweets are presented. This research facilitates the development of NLP tools such as dependency parsers for Irish by providing a test set on which future Irish language technology can be tested. Future work will involve both further annotation and exploration of semi-supervised techniques. We warmly thank the anonymous reviewers, as well as Steven Bird and Kevin Scannell for their valuable feedback. This work was funded by the Irish Government Department of Tourism, Culture, Arts, Gaeltacht, Sport and Media under the Gael-Tech Project, and also supported by Science Foundation Ireland in the ADAPT Centre (Grant No. 13/RC/2106) at Dublin City University.
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