A Dictionary- and Corpus-Independent Statistical Lemmatizer for Information Retrieval in Low Resource Languages

TestitiivistelmäThe original publication is available at www.springerlink.com

Putting Successor Variety Stemming to Work

Abstract. Stemming algorithms find canonical forms for inflected words, e. g. for declined nouns or conjugated verbs. Since such a unification of words with respect to gender, number, time, and case is a language-specific issue, stemming algorithms operationalize a set of linguistically motivated rules for the language in question. The most well-known rule-based algorithm for the English language is from Porter [14]. The paper presents a statistical stemming approach which is based on the analysis of the distribution of word prefixes in a document collection, and which thus is widely language-independent. In particular, our approach addresses the problem of index construction for multi-lingual documents. Related work for statistical stemming focuses either on stemming quality [2,3] or on runtime performance [11], but neither provides a reasonable tradeoff between both. For selected retrieval tasks under vector-based document models we report on new results related to stemming quality and collection size dependency. Interestingly, successor variety stemming has neither been investigated under similarity concerns for index construction nor is it applied as a technology in current retrieval applications. As our results will show, this disregard is not justified.

An efficient mechanism for stemming and tagging: the case of Greek language

Abstract. In an era that, searching the WWW for information becomes a tedious task, it is obvious that mainly search engines and other data mining mechanisms need to be enhanced with characteristics such as NLP in order to better analyze and recognize user queries and fetch data. We present an efficient mechanism for stemming and tagging for the Greek language. Our system is constructed in such a way that can be easily adapted to any existing system and support it with recognition and analysis of Greek words. We examine the accuracy of the system and its ability to support peRSSonal a medium constructed for offering meta-portal news services to internet users. We present experimental evaluation of the system compared to already existing stemmers and taggers of the Greek language and we prove the higher efficiency and quality of results of our system

A.: Increasing recall of process model matching by improved activity label matching

Abstract. Comparing process models and matching similar activities has recently emerged as a research area of business process management. However, the problem is fundamentally hard when considering realistic scenarios: e.g., there is a huge variety of terms and various options for the grammatical structure of activity labels exist. While prior research has established important conceptual foundations, recall values have been fairly low (around 0.26) – arguably too low to be useful in practice. In this paper, we present techniques for activity label matching which improve current results (recall of 0.44, without sacrificing precision). Furthermore, we identify categories of matching challenges to guide future research

Report on CLEF-2003 Monolingual Tracks: Fusion of Probabilistic Models for Effective Monolingual Retrieval

Abstract. For our third participation in the CLEF evaluation campaign, our first objective was to propose more effective and general stopword lists for the Swedish, Finnish and Russian languages along with an improved, more efficient and simpler stemming procedure for these three languages. Our second goal was to suggest a combined search approach based on a data fusion strategy that would work with various European languages. Included in this combined approach is a decompounding strategy for the German, Dutch, Swedish and Finnish languages