Joint Morphological and Syntactic Disambiguation

In morphologically rich languages, should morphological and syntactic disambiguation be treated sequentially or as a single problem? We describe several efficient, probabilistically interpretable ways to apply joint inference to morphological and syntactic disambiguation using lattice parsing. Joint inference is shown to compare favorably to pipeline parsing methods across a variety of component models. State-of-the-art performance on Hebrew Treebank parsing is demonstrated using the new method. The benefits of joint inference are modest with the current component models, but appear to increase as components themselves improve

Methods for Amharic part-of-speech tagging

The paper describes a set of experiments involving the application of three state-of- the-art part-of-speech taggers to Ethiopian Amharic, using three different tagsets. The taggers showed worse performance than previously reported results for Eng- lish, in particular having problems with unknown words. The best results were obtained using a Maximum Entropy ap- proach, while HMM-based and SVM- based taggers got comparable results

Mimicking Word Embeddings using Subword RNNs

Word embeddings improve generalization over lexical features by placing each word in a lower-dimensional space, using distributional information obtained from unlabeled data. However, the effectiveness of word embeddings for downstream NLP tasks is limited by out-of-vocabulary (OOV) words, for which embeddings do not exist. In this paper, we present MIMICK, an approach to generating OOV word embeddings compositionally, by learning a function from spellings to distributional embeddings. Unlike prior work, MIMICK does not require re-training on the original word embedding corpus; instead, learning is performed at the type level. Intrinsic and extrinsic evaluations demonstrate the power of this simple approach. On 23 languages, MIMICK improves performance over a word-based baseline for tagging part-of-speech and morphosyntactic attributes. It is competitive with (and complementary to) a supervised character-based model in low-resource settings.Comment: EMNLP 201

An improved neural network model for joint POS tagging and dependency parsing

We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 "big" Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakov\'a, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: https://github.com/datquocnguyen/jPTDPComment: 11 pages; In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, to appea

CoDiAJe - the Annotated Diachronic Corpus of Judeo-spanish : Description of a Multi-alphabetic Corpus and its Textual and Linguistic Annotations

Judeo-Spanish differs from late 15th-century Spanish and modern Spanish in several respects, such as its morphology, syntax, and semantics, but the most visible difference is in the alphabet. From the end of the 19th century, Judeo-Spanish has been written in various alphabets -Greek, Cyrillic, and especially Latin-. However, the Hebrew alphabet had been used since ancient times, before it was abandoned finally only in the 1940s. This means that the majority of Judeo-Spanish texts are written in Hebrew characters. CoDiAJe is an annotated diachronic corpus that includes documents produced from the 16th century up to the present day, developed in TEITOK. The significance of its development is that this tool processes linguistic data in the alphabets mentioned above, allowing users to visualize each text in five orthographic forms (the original version in which it was written, its transcription in Latin characters, an expanded form to complete abbreviations or to correct defective writing, a version in modern Judeo-Spanish, and a version in orthographic modern Spanish). CoDiAJe enables the user to conduct searches not only for a specific word, but also for all its linguistic and orthographic variants in the different alphabets. During the annotation process, tags from the EAGLES tagset for Spanish were modified, and others were created: these are simply steps towards the creation of an accurate tagset for Judeo-Spanish. The digitized texts are also enriched with semantic-conceptual information and information on the affiliation of all non-Romance elements.El judeoespañol se diferencia del español de finales del siglo XV y del español moderno en varios aspectos que afectan a la fonética y fonología, morfología, sintaxis y semántica. Sin embargo, la diferencia más fácilmente apreciable está en el alfabeto. A finales del siglo XIX se comenzó a escribir con diferentes alfabetos: griego, cirílico y, sobre todo, latino en diferentes versiones. Sin embargo, desde tiempos remotos se utilizó el alfabeto hebreo, y su abandono definitivo solo ocurrió en la década de los cuarenta del siglo pasado, por lo que la mayor parte de los textos escritos en esta lengua están en caracteres hebreos. CoDiAJe es un corpus diacrónico anotado que incluye documentos creados desde el siglo XVI hasta nuestros días, desarrollado en TEITOK. La importancia de su desarrollo está en que procesa datos lingüísticos en los alfabetos mencionados anteriormente, da al usuario la opción de visualizar cada texto en cinco formas gráficas (la versión original independientemente del alfabeto en el que fue escrita, su transcripción en caracteres latinos, una forma expandida para completar las abreviaturas o corregir la escritura defectuosa, una versión en judeoespañol moderno y una versión en la ortografía del español moderno), y permite realizar búsquedas no solo de una palabra específica sino de todas sus variantes lingüísticas y ortográficas en textos escritos en los diferentes alfabetos. Durante el proceso de anotación se fueron modificando las etiquetas de EAGLES para el español y se crearon algunas nuevas. Significa que, a medida que se van anotando los textos, vamos creando un etiquetador para el judeoespañol. Los textos digitalizados también se enriquecen con información semántico-conceptual e información sobre la filiación de todos los elementos no románicos que se detectan en los textos