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Word Sense Disambiguation Based on Large Scale Polish CLARIN Heterogeneous Lexical ResourcesLexical resources can be applied in many different Natural Language Engineering tasks, but the most fundamental task is the recognition of word senses used in text contexts. The problem is difficult, not yet fully solved and different lexical resources provided varied support for it. Polish CLARIN lexical semantic resources are based on the plWordNet — a very large wordnet for Polish — as a central structure which is a basis for linking together several resources of different types. In this paper, several Word Sense Disambiguation (henceforth WSD) methods developed for Polish that utilise plWordNet are discussed. Textual sense descriptions in the traditional lexicon can be compared with text contexts using Lesk’s algorithm in order to find best matching senses. In the case of a wordnet, lexico-semantic relations provide the main description of word senses. Thus, first, we adapted and applied to Polish a WSD method based on the Page Rank. According to it, text words are mapped on their senses in the plWordNet graph and Page Rank algorithm is run to find senses with the highest scores. The method presents results lower but comparable to those reported for English. The error analysis showed that the main problems are: fine grained sense distinctions in plWordNet and limited number of connections between words of different parts of speech. In the second approach plWordNet expanded with the mapping onto the SUMO ontology concepts was used. Two scenarios for WSD were investigated: two step disambiguation and disambiguation based on combined networks of plWordNet and SUMO. In the former scenario, words are first assigned SUMO concepts and next plWordNet senses are disambiguated. In latter, plWordNet and SUMO are combined in one large network used next for the disambiguation of senses. The additional knowledge sources used in WSD improved the performance. The obtained results and potential further lines of developments were discussed.

Automatic Prompt System in the Process of Mapping plWordNet on Princeton WordNetThe paper offers a critical evaluation of the power and usefulness of an automatic prompt system based on the extended Relaxation Labelling algorithm in the process of (manual) mapping plWordNet on Princeton WordNet. To this end the results of manual mapping – that is inter-lingual relations between plWN and PWN synsets – are juxtaposed with the automatic prompts that were generated for the source language synsets to be mapped. We check the number and type of inter-lingual relations introduced on the basis of automatic prompts and the distance of the respective prompt synsets from the actual target language synsets.

This paper presents an supervised approach to the recognition of Cross-document Structure Theory (CST) relations in Polish texts. In the proposed, graph-based representation is constructed for sentences. Graphs are built on the basis of lexicalised syntactic-semantic relation extracted from text. Similarity between sentences is calculated from graph, and the similarity values are input to classifiers trained by Logistic Model Tree. Several different configurations of graph, as well as graph similarity methods were analysed for this tasks. The approach was evaluated on a large open corpus annotated manually with 17 types of selected CST relations. The configuration of experiments was similar to those known from SEMEVAL and we obtained very promising results.

Feature extraction from text corpora is an important step in Natural Language Processing (NLP), especially for Machine Learning (ML) techniques. Various NLP tasks have many common steps, e.g. low level act of reading a corpus and obtaining text windows from it. Some high-level processing steps might also be shared, e.g. testing for morpho-syntactic constraints between words. An integrated feature extraction framework removes wasteful redundancy and helps in rapid prototyping.

Ambiguity is an inherent problem for many tasks in Natural Language Processing. Unsupervised and semi-supervised approaches to ambiguity resolution are appealing as they lower the cost of manual labour. Typically, those methods struggle with estimation of number of senses without supervision. This paper shows research on using stopping functions applied to clustering algorithms for estimation of number of senses. The experiments were performed for Polish and English. We found that estimation based on PK2 stopping functions is encouraging, but only when using coarse-grained distinctions between senses.

In the paper we present an extended version of the graph-based unsupervised Word Sense Disambiguation algorithm. The algorithm is based on the spreading activation scheme applied to the graphs dynamically built on the basis of the text words and a large wordnet. The algorithm, originally proposed for English and Princeton WordNet, was adapted to Polish and plWordNet. An extension based on the knowledge acquired from the corpus-derived Measure of Semantic Relatedness was proposed. The extended algorithm was evaluated against the manually disambiguated corpus. We observed improvement in the case of the disambiguation performed for shorter text contexts. In addition the algorithm application expressed improvement in document clustering task.