Browsing by Subject "language technology"

Sort by: Order: Results:

Now showing items 1-16 of 16
  • Koskenniemi, Kimmo (The Association for Computational Linguistics, 1984)
    A language independent model for recognition and production of word forms is presented. This "two-level model" is based on a new way of describing morphological alternations. All rules describing the morphophonological variations are parallel and relatively independent of each other. Individual rules are implemented as finite state automata, as in an earlier model due to Martin Kay and Ron Kaplan. The two-level model has been implemented as an operational computer programs in several places. A number of operational two-level descriptions have been written or are in progress (Finnish, English, Japanese, Rumanian, French, Swedish, Old Church Slavonic, Greek, Lappish, Arabic, Icelandic). The model is bidirectional and it is capable of both analyzing and synthesizing word-forms.
  • Jouste, Marko; Mettovaara, Jukka; Morottaja, Petter; Partanen, Niko (CEUR-WS.org, 2022)
    CEUR Workshop Proceedings
    This study presents the results of an Aanaar Saami pilot project in the Saami Culture Archive, University of Oulu. The project has established a set of conventions to transcribe and annotate Aanaar Saami recordings in the archive's collection and created a mechanism through which grammatically annotated but anonymous versions can be imported to the Korp search interface in the Language Bank of Finland. The practices include wide use of Saami language technology, the use of Finnish computational research infrastructure, and they can be extended later to other Saami languages in the archive.
  • Shao, Yan; Hardmeier, Christian; Tiedemann, Jörg; Nivre, Joakim (Asian Federation of Natural Language Processing, 2017)
    We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging.
  • Yli-Jyrä, Anssi Mikael; Koskenniemi, Kimmo; Linden, Krister (2006)
    Finite-state methods have been adopted widely in computational morphology and related linguistic applications. To enable efficient development of finite-state based linguistic descriptions, these methods should be a freely available resource for academic language research and the language technology industry. The following needs can be identified: (i) a registry that maps the existing approaches, implementations and descriptions, (ii) managing the incompatibilities of the existing tools, (iii) increasing synergy and complementary functionality of the tools, (iv) persistent availability of the tools used to manipulate the archived descriptions, (v) an archive for free finite-state based tools and linguistic descriptions. Addressing these challenges contributes to building a common research infrastructure for advanced language technology.
  • China-Kolehmainen, Elena (Helsingin yliopisto, 2021)
    Computer-Assisted Language Learning (CALL) is one of the sub-disciplines within the area of Second Language Acquisition. Clozes, also called fill-in-the-blank, are largely used exercises in language learning applications. A cloze is an exercise where the learner is asked to provide a fragment that has been removed from the text. For language learning purposes, in addition to open-end clozes where one or more words are removed and the student must fill the gap, another type of cloze is commonly used, namely multiple-choice cloze. In a multiple-choice cloze, a fragment is removed from the text and the student must choose the correct answer from multiple options. Multiple-choice exercises are a common way of practicing and testing grammatical knowledge. The aim of this work is to identify relevant learning constructs for Italian to be applied to automatic exercises creation based on authentic texts in the Revita Framework. Learning constructs are units that represent language knowledge. Revita is a free to use online platform that was designed to provide language learning tools with the aim of revitalizing endangered languages including several Finno-Ugric languages such as North Saami. Later non-endangered languages were added. Italian is the first majority language to be added in a principled way. This work paves the way towards adding new languages in the future. Its purpose is threefold: it contributes to the raising of Italian from its beta status towards a full development stage; it formulates best practices for defining support for a new language and it serves as a documentation of what has been done, how and what remains to be done. Grammars and linguistic resources were consulted to compile an inventory of learning constructs for Italian. Analytic and pronominal verbs, verb government with prepositions, and noun phrase agreement were implemented by designing pattern rules that match sequences of tokens with specific parts-of-speech, surfaces and morphological tags. The rules were tested with test sentences that allowed further refining and correction of the rules. Current precision of the 47 rules for analytic and pronominal verbs on 177 test sentences results in 100%. Recall is 96.4%. Both precision and recall for the 5 noun phrase agreement rules result in 96.0% in respect to the 34 test sentences. Analytic and pronominal verb, as well as noun phrase agreement patterns, were used to generate open-end clozes. Verb government pattern rules were implemented into multiple-choice exercises where one of the four presented options is the correct preposition and the other three are prepositions that do not fit in context. The patterns were designed based on colligations, combinations of tokens (collocations) that are also explained by grammatical constraints. Verb government exercises were generated on a specifically collected corpus of 29074 words. The corpus included three types of text: biography sections from Wikipedia, Italian news articles and Italian language matriculation exams. The last text type generated the most exercises with a rate of 19 exercises every 10000 words, suggesting that the semi-authentic text met best the level of verb government exercises because of appropriate vocabulary frequency and sentence structure complexity. Four native language experts, either teachers of Italian as L2 or linguists, evaluated usability of the generated multiple-choice clozes, which resulted in 93.55%. This result suggests that minor adjustments i.e., the exclusion of target verbs that cause multiple-admissibility, are sufficient to consider verb government patterns usable until the possibility of dealing with multiple-admissible answers is addressed. The implementation of some of the most important learning constructs for Italian resulted feasible with current NLP tools, although quantitative evaluation of precision and recall of the designed rules is needed to evaluate the generation of exercises on authentic text. This work paves the way towards a full development stage of Italian in Revita and enables further pilot studies with actual learners, which will allow to measure learning outcomes in quantitative terms
  • Tiedemann, Jörg (CEUR Workshop Proceedings, 2018)
    CEUR Workshop Proceedings
  • Koskenniemi, Kimmo; Linden, Krister; Nordgård, Torbjørn; Department of Modern Languages 2010-2017 (University of Helsinki. Department of General Linguistics, 2007)
    Publications
  • Nyholm, Sabine (Helsingin yliopisto, 2020)
    Universella meningsrepresentationer och flerspråkig språkmodellering är heta ämnen inom språkteknologi, specifikt området som berör förståelse för naturligt språk (natural language understanding). En meningsinbäddning (sentence embedding) är en numerisk skildring av en följd ord som motsvaras av en hel fras eller mening, speficikt som ett resultat av en omkodare (encoder) inom maskininlärning. Dessa representationer behövs för automatiska uppgifter inom språkteknologi som kräver förståelse för betydelsen av en hel mening, till skillnad från kombinationer av enskilda ords betydelser. Till sådana uppgifter kan räknas till exempel inferens (huruvida ett par satser är logiskt anknutna, natural language inference) samt åsiktsanalys (sentiment analysis). Med universalitet avses kodad betydelse som är tillräckligt allmän för att gynna andra relaterade uppgifter, som till exempel klassificering. Det efterfrågas tydligare samförstånd kring strategier som används för att bedöma kvaliteten på dessa inbäddningar, antingen genom att direkt undersöka deras lingvistiska egenskaper eller genom att använda dem som oberoende variabler (features) i relaterade modeller. På grund av att det är kostsamt att skapa resurser av hög kvalitet och upprätthålla sofistikerade system på alla språk som används i världen finns det även ett stort intresse för uppskalering av moderna system till språk med knappa resurser. Tanken med detta är så kallad överföring (transfer) av kunskap inte bara mellan olika uppgifter, utan även mellan olika språk. Trots att behovet av tvärspråkiga överföringsmetoder erkänns i forskningssamhället är utvärderingsverktyg och riktmärken fortfarande i ett tidigt skede. SentEval är ett existerande verktyg för utvärdering av meningsinbäddningar med speciell betoning på deras universalitet. Syftet med detta avhandlingsprojekt är ett försök att utvidga detta verktyg att stödja samtidig bedömning på nya uppgifter som omfattar flera olika språk. Bedömningssättet bygger på strategin att låta kodade meningar fungera som variabler i så kallade downstream-uppgifter och observera huruvida resultaten förbättras. En modern mångspråkig modell baserad på så kallad transformers-arkitektur utvärderas på en etablerad inferensuppgift såväl som en ny känsloanalyssuppgift (emotion detection), av vilka båda omfattar data på en mängd olika språk. Även om det praktiska genomförandet i stor utsträckning förblev experimentellt rapporteras vissa tentativa resultat i denna avhandling.
  • Koskenniemi, Kimmo Matti (The Association for Computational Linguistics, 2018)
    A practical method for interactive guessing of LEXC lexicon entries is presented. The method is based on describing groups of similarly inflected words using regular expressions. The patterns are compiled into a finite-state transducer (FST) which maps any word form into the possible LEXC lexicon entries which could generate it. The same FST can be used (1) for converting conventional headword lists into LEXC entries, (2) for interactive guessing of entries, (3) for corpus-assisted interactive guessing and (4) guessing entries from corpora. A method of representing affixes as a table is presented as well how the tables can be converted into LEXC format for several different purposes including morphological analysis and entry guessing. The method has been implemented using the HFST finite-state transducer tools and its Python embedding plus a number of small Python scripts for conversions. The method is tested with a near complete implementation of Finnish verbs. An experiment of generating Finnish verb entries out of corpus data is also described as well as a creation of a full-scale analyzer for Finnish verbs using the conversion patterns.
  • Lison, Pierre; Tiedemann, Jörg; Kouylekov, Milen (European Language Resources Association (ELRA), 2018)
  • Bozovic, Petar; Erjavec, Tomaz; Tiedemann, Jörg; Ljubesic, Nikola; Gorjanc, Vojko (Ljubljana University Press, 2018)
  • Koskenniemi, Kimmo (CSLI publications, 2019)
    CSLI Lecture Notes
  • Jauhiainen, Tommi; Lennes, Mietta; Marttila, Terhi; Digitaalisten ihmistieteiden osasto; Centre of Excellence in Ancient Near Eastern Empires (ANEE); Kieliteknologia (Vake Oy, 2019)
  • Partanen, Niko; Blokland, Rogier; Rießler, Michael; Rueter, Jack (CEUR-WS.org, 2022)
    CEUR Workshop Proceedings
    Transcriptions in different languages are a ubiquitous data format in linguistics and in many other fields in the humanities. However, the majority of these resources remain both under-used and under-studied. This may be the case even when the materials have been published in print, but is certainly the case for the majority of unpublished transcriptions. Our paper presents a workflow adapted in the research project Language Documentation Meets Language Technology, which combines text recognition, automatic transliteration and forced alignment into a process which allows us to convert earlier transcribed documents to a structure that is comparable with contemporary language documentation corpora. This has complex practical and methodological considerations.
  • Yli-Jyrä, Anssi (The Linguistic Association of Finland, 2006)
    The trees in the Penn Treebank have a standard representation that involves complete balanced bracketing. In this article, an alternative for this standard representation of the tree bank is proposed. The proposed representation for the trees is loss-less, but it reduces the total number of brackets by 28%. This is possible by omitting the redundant pairs of special brackets that encode initial and final embedding, using a technique proposed by Krauwer and des Tombe (1981). In terms of the paired brackets, the maximum nesting depth in sentences decreases by 78%. The 99.9% coverage is achieved with only five non-top levels of paired brackets. The observed shallowness of the reduced bracketing suggests that finite-state based methods for parsing and searching could be a feasible option for tree bank processing.
  • Bjerva, Johannes; Östling, Robert; Han Veiga, Maria; Tiedemann, Jörg; Augenstein, Isabelle (2019)
    A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just like it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, while genetic relationships---a convenient benchmark used for evaluation in previous work---appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.