An extensible multilingual open source lemmatizer

We present GATE DictLemmatizer, a multilingual open source lemmatizer for the GATE NLP framework that currently supports English, German, Italian, French, Dutch, and Spanish, and is easily extensible to other languages. The software is freely available under the LGPL license. The lemmatization is based on the Helsinki Finite-State Transducer Technology (HFST) and lemma dictionaries automatically created from Wiktionary. We evaluate the performance of the lemmatizers against TreeTagger, which is only freely available for research purposes. Our evaluation shows that DictLemmatizer achieves similar or even better results than TreeTagger for languages where there is support from HFST. The performance drops when there is no support from HFST and the entire lemmatization process is based on lemma dictionaries. However, the results are still satisfactory given the fact that DictLemmatizer isopen-source and can be easily extended to other languages. The software for extending the lemmatizer by creating word lists from Wiktionary dictionaries is also freely available as open-source software

Universal Lemmatizer: A sequence-to-sequence model for lemmatizing Universal Dependencies treebanks

In this paper, we present a novel lemmatization method based on a sequence-to-sequence neural network architecture and morphosyntactic context representation. In the proposed method, our context-sensitive lemmatizer generates the lemma one character at a time based on the surface form characters and its morphosyntactic features obtained from a morphological tagger. We argue that a sliding window context representation suffers from sparseness, while in majority of cases the morphosyntactic features of a word bring enough information to resolve lemma ambiguities while keeping the context representation dense and more practical for machine learning systems. Additionally, we study two different data augmentation methods utilizing autoencoder training and morphological transducers especially beneficial for low-resource languages. We evaluate our lemmatizer on 52 different languages and 76 different treebanks, showing that our system outperforms all latest baseline systems. Compared to the best overall baseline, UDPipe Future, our system outperforms it on 62 out of 76 treebanks reducing errors on average by 19% relative. The lemmatizer together with all trained models is made available as a part of the Turku-neural-parsing-pipeline under the Apache 2.0 license.</p

D4.1. Technologies and tools for corpus creation, normalization and annotation

The objectives of the Corpus Acquisition and Annotation (CAA) subsystem are the acquisition and processing of monolingual and bilingual language resources (LRs) required in the PANACEA context. Therefore, the CAA subsystem includes: i) a Corpus Acquisition Component (CAC) for extracting monolingual and bilingual data from the web, ii) a component for cleanup and normalization (CNC) of these data and iii) a text processing component (TPC) which consists of NLP tools including modules for sentence splitting, POS tagging, lemmatization, parsing and named entity recognition

Suomenkielisen geojäsentimen kehittäminen: kuinka hankkia sijaintitietoa jäsentelemättömistä tekstiaineistoista

Alati enemmän aineistoa tuotetaan ja jaetaan internetin kautta. Aineistot ovat vaihtelevia muodoiltaan, kuten verkkoartikkelien ja sosiaalisen media julkaisujen kaltaiset digitaaliset tekstit, ja niillä on usein spatiaalinen ulottuvuus. Teksteissä geospatiaalisuutta ilmaistaan paikannimien kautta, mutta tavanomaisilla paikkatietomenetelmillä ei kyetä käsittelemään tietoa epätäsmällisessä kielellisessä asussaan. Tämä on luonut tarpeen muuntaa tekstimuotoisen sijaintitiedon näkyvään muotoon, koordinaateiksi. Ongelmaa ratkaisemaan on kehitetty geojäsentimiä, jotka tunnistavat ja paikantavat paikannimet vapaista teksteistä, ja jotka oikein toimiessaan voisivat toimia paikkatiedon lähteenä maantieteellisessä tutkimuksessa. Geojäsentämistä onkin sovellettu katastrofihallinnasta kirjallisuudentutkimukseen. Merkittävässä osassa geojäsentämisen tutkimusta tutkimusaineiston kielenä on ollut englanti ja geojäsentimetkin ovat kielikohtaisia – tämä jättää pimentoon paitsi geojäsentimien kehitykseen vaikuttavat havainnot pienemmistä kielistä myös kyseisten kielten puhujien näkemykset. Maisterintutkielmassani pyrin vastaamaan kolmeen tutkimuskysymykseen: Mitkä ovat edistyneimmät geojäsentämismenetelmät? Mitkä kielelliset ja maantieteelliset monitulkintaisuudet vaikeuttavat tämän monitahoisen ongelman ratkaisua? Ja miten arvioida geojäsentimien luotettavuutta ja käytettävyyttä? Tutkielman soveltavassa osuudessa esittelen Fingerin, geojäsentimen suomen kielelle, ja kuvaan sen kehitystä sekä suorituskyvyn arviointia. Arviointia varten loin kaksi testiaineistoa, joista toinen koostuu Twitter-julkaisuista ja toinen uutisartikkeleista. Finger-geojäsennin, testiaineistot ja relevantit ohjelmakoodit jaetaan avoimesti. Geojäsentäminen voidaan jakaa kahteen alitehtävään: paikannimien tunnistamiseen tekstivirrasta ja paikannimien ratkaisemiseen oikeaan koordinaattipisteeseen mahdollisesti useasta kandidaatista. Molemmissa vaiheissa uusimmat metodit nojaavat syväoppimismalleihin ja -menetelmiin, joiden syötteinä ovat sanaupotusten kaltaiset vektorit. Geojäsentimien suoriutumista testataan aineistoilla, joissa paikannimet ja niiden koordinaatit tiedetään. Mittatikkuna tunnistamisessa on vastaavuus ja ratkaisemisessa etäisyys oikeasta sijainnista. Finger käyttää paikannimitunnistinta, joka hyödyntää suomenkielistä BERT-kielimallia, ja suoraviivaista tietokantahakua paikannimien ratkaisemiseen. Ohjelmisto tuottaa taulukkomuotoiseksi jäsenneltyä paikkatietoa, joka sisältää syötetekstit ja niistä mahdollisesti tunnistetut paikannimet koordinaattisijainteineen. Testiaineistot eroavat aihepiireiltään, mutta Finger suoriutuu niillä likipitäen samoin, ja suoriutuu englanninkielisillä aineistoilla tehtyihin arviointeihin suhteutettuna kelvollisesti. Virheanalyysi paljastaa useita virhelähteitä, jotka johtuvat kielten tai maantieteellisen todellisuuden luontaisesta epäselvyydestä tai ovat prosessoinnin aiheuttamia, kuten perusmuotoistamisvirheet. Kaikkia osia Fingerissä voidaan parantaa, muun muassa kehittämällä kielellistä käsittelyä pidemmälle ja luomalla kattavampia testiaineistoja. Samoin tulevaisuuden geojäsentimien tulee kyetä käsittelemään monimutkaisempia kielellisiä ja maantieteellisiä kuvaustapoja kuin pelkät paikannimet ja koordinaattipisteet. Finger ei nykymuodossaan tuota valmista paikkatietoa, jota kannattaisi kritiikittä käyttää. Se on kuitenkin lupaava ensiaskel suomen kielen geojäsentimille ja astinlauta vastaisuuden soveltavalle tutkimukselle.Ever more data is available and shared through the internet. The big data masses often have a spatial dimension and can take many forms, one of which are digital texts, such as articles or social media posts. The geospatial links in these texts are made through place names, also called toponyms, but traditional GIS methods are unable to deal with the fuzzy linguistic information. This creates the need to transform the linguistic location information to an explicit coordinate form. Several geoparsers have been developed to recognize and locate toponyms in free-form texts: the task of these systems is to be a reliable source of location information. Geoparsers have been applied to topics ranging from disaster management to literary studies. Major language of study in geoparser research has been English and geoparsers tend to be language-specific, which threatens to leave the experiences provided by studying and expressed in smaller languages unexplored. This thesis seeks to answer three research questions related to geoparsing: What are the most advanced geoparsing methods? What linguistic and geographical features complicate this multi-faceted problem? And how to evaluate the reliability and usability of geoparsers? The major contributions of this work are an open-source geoparser for Finnish texts, Finger, and two test datasets, or corpora, for testing Finnish geoparsers. One of the datasets consists of tweets and the other of news articles. All of these resources, including the relevant code for acquiring the test data and evaluating the geoparser, are shared openly. Geoparsing can be divided into two sub-tasks: recognizing toponyms amid text flows and resolving them to the correct coordinate location. Both tasks have seen a recent turn to deep learning methods and models, where the input texts are encoded as, for example, word embeddings. Geoparsers are evaluated against gold standard datasets where toponyms and their coordinates are marked. Performance is measured on equivalence and distance-based metrics for toponym recognition and resolution respectively. Finger uses a toponym recognition classifier built on a Finnish BERT model and a simple gazetteer query to resolve the toponyms to coordinate points. The program outputs structured geodata, with input texts and the recognized toponyms and coordinate locations. While the datasets represent different text types in terms of formality and topics, there is little difference in performance when evaluating Finger against them. The overall performance is comparable to the performance of geoparsers of English texts. Error analysis reveals multiple error sources, caused either by the inherent ambiguousness of the studied language and the geographical world or are caused by the processing itself, for example by the lemmatizer. Finger can be improved in multiple ways, such as refining how it analyzes texts and creating more comprehensive evaluation datasets. Similarly, the geoparsing task should move towards more complex linguistic and geographical descriptions than just toponyms and coordinate points. Finger is not, in its current state, a ready source of geodata. However, the system has potential to be the first step for geoparsers for Finnish and it can be a steppingstone for future applied research

Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages

The scarcity of parallel data is a major limitation for Neural Machine Translation (NMT) systems, in particular for translation into morphologically rich languages (MRLs). An important way to overcome the lack of parallel data is to leverage target monolingual data, which is typically more abundant and easier to collect. We evaluate a number of techniques to achieve this, ranging from back-translation to random token masking, on the challenging task of translating English into four typologically diverse MRLs, under low-resource settings. Additionally, we introduce Inflection Pre-Training (or PT-Inflect), a novelpre-training objective whereby the NMT system is pre-trained on the task of re-inflecting lemmatized target sentences before being trained on standard source-to-target language translation. We conduct our evaluation on four typologically diverse target MRLs, and find that PT-Inflect surpasses NMT systems trained only on parallel data. While PT-Inflect is outperformed by back-translation overall, combining the two techniques leads to gains in some of the evaluated language pairs

Towards an Indonesian-English SMT System: A Case Study of an Under-Studied and Under-Resourced Language, Indonesian

This paper describes a work on preparing an Indonesian-English Statistical Machine Translation (SMT) System. It includes the creation of Indonesian morphological analyzer, MorphInd, and the composing of an Indonesian-English parallel corpus, IDENTIC. We build an SMT system using the state-of-the-art phrase-based SMT system, MOSES. We show several scenarios where the morphological tool is used to incorporate morphological information in the SMT system trained with the composed parallel corpus