We propose ThaiLMCut, a semi-supervised approach for Thai word segmentation which utilizes a bi-directional character language model (LM) as a way to leverage useful linguistic knowledge from unlabeled data. After the language model is trained on substantial unlabeled corpora, the weights of its embedding and recurrent layers are transferred to a supervised word segmentation model which continues fine-tuning them on a word segmentation task. Our experimental results demonstrate that applying the LM always leads to a performance gain, especially when the amount of labeled data is small. In such cases, the F1 Score increased by up to 2.02%. Even on abig labeled dataset, a small improvement gain can still be obtained. The approach has also shown to be very beneficial for out-of-domain settings with a gain in F1 Score of up to 3.13%. Finally, we show that ThaiLMCut can outperform other open source state-of-the-art models achieving an F1 Score of 98.78% on the standard benchmark, InterBEST2009.

In this paper, we present Dijkstra-WSA, a novel graph-based algorithm for word sense alignment. We evaluate it on four different pairs of lexical-semantic resources with different characteristics (WordNet-OmegaWiki, WordNet-Wiktionary, GermaNet-Wiktionary and WordNet-Wikipedia) and show that it achieves competitive performance on 3 out of 4 datasets. Dijkstra-WSA outperforms the state of the art on every dataset if it is combined with a back-off based on gloss similarity. We also demonstrate that Dijkstra-WSA is not only flexibly applicable to different resources but also highly parameterizable to optimize for precision or recall.

We present UBY-LMF, an LMF-based model for large-scale, heterogeneous multilingual lexical-semantic resources (LSRs). UBY-LMF allows the standardization of LSRs down to a fine-grained level of lexical information by employing a large number of Data Categories from ISOCat. We evaluate UBY-LMF by converting nine LSRs in two languages to the corresponding format: the English WordNet, Wiktionary, Wikipedia, OmegaWiki, FrameNet and VerbNet and the German Wikipedia, Wiktionary and GermaNet. The resulting LSR, UBY (Gurevych et al., 2012), holds interoperable versions of all nine resources which can be queried by an easy to use public Java API. UBY-LMF covers a wide range of information types from expert-constructed and collaboratively constructed resources for English and German, also including links between different resources at the word sense level. It is designed to accommodate further resources and languages as well as automatically mined lexical-semantic knowledge.

This paper describes the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN). The OWLG is an initiative concerned with linguistic data by scholars from diverse fields, including linguistics, NLP, and information science. The primary goal of the working group is to promote the idea of open linguistic resources, to develop means for their representation and to encourage the exchange of ideas across different disciplines. This paper summarizes the progress of the working group, goals that have been identified, problems that we are going to address, and recent activities and ongoing developments. Here, we put particular emphasis on the development of a Linked Open Data (sub-)cloud of linguistic resources that is currently being pursued by several OWLG members.