The Experts below are selected from a list of 20094 Experts worldwide ranked by ideXlab platform

Mikko Kurimo - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised morpheme analysis evaluation by a comparison to a linguistic gold standard morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Matti Varjokallio
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 1 (linguistic gold standard). The Competition 2 (information retrieval) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling The choice of a meaningful evaluation for the submitted morpheme analysis was not straight-forward, because in unsupervised morpheme analysis the morphemes can have arbitrary names. Two complementary ways were developed for the evaluation: Competition 1: The proposed morpheme analyses were compared to a linguistic morpheme analysis gold standard by matching the morpheme-sharing word pairs. Competition 2: Information retrieval (IR) experiments were performed, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. Data sets for Competition 1 were provided for four languages: Finnish, German, English, and Turkish and the participants were encouraged to apply their algorithm to all of them. The results show significant variance between the methods and languages, but the best methods seem to be useful in all tested languages and match quite well with the linguistic gold standard. The Morpho Challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

  • unsupervised morpheme analysis evaluation by ir experiments morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Ville T Turunen
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 2 (information retrieval). The Competition 1 (linguistic gold standard) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling In this paper the morpheme analysis submitted by the Challenge participants were evaluated by performing information retrieval (IR) experiments, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. The IR evaluations were provided for three languages: Finnish, German, and English and the participants were encouraged to apply their algorithm to all of them. The challenge organizers performed the IR experiments using the queries, texts, and relevance judgments available in CLEF forum and morpheme analysis methods submitted by the challenge participants. The results show that the morpheme analysis has a significant eect in IR performance in all languages, and that the performance of the best unsupervised methods can be superior to the supervised reference methods. The challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

  • unsupervised morpheme analysis evaluation by ir experiments morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Ville T Turunen
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 2 (information retrieval). The Competition 1 (linguistic gold standard) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling In this paper the morpheme analysis submitted by the Challenge participants were evaluated by performing information retrieval (IR) experiments, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. The IR evaluations were provided for three languages: Finnish, German, and English and the participants were encouraged to apply their algorithm to all of them. The challenge organizers performed the IR experiments using the queries, texts, and relevance judgments available in CLEF forum and morpheme analysis methods submitted by the challenge participants. The results show that the morpheme analysis has a significant eect in IR performance in all languages, and that the performance of the best unsupervised methods can be superior to the supervised reference methods. The challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

  • unsupervised morpheme analysis evaluation by a comparison to a linguistic gold standard morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Matti Varjokallio
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 1 (linguistic gold standard). The Competition 2 (information retrieval) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling The choice of a meaningful evaluation for the submitted morpheme analysis was not straight-forward, because in unsupervised morpheme analysis the morphemes can have arbitrary names. Two complementary ways were developed for the evaluation: Competition 1: The proposed morpheme analyses were compared to a linguistic morpheme analysis gold standard by matching the morpheme-sharing word pairs. Competition 2: Information retrieval (IR) experiments were performed, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. Data sets for Competition 1 were provided for four languages: Finnish, German, English, and Turkish and the participants were encouraged to apply their algorithm to all of them. The results show significant variance between the methods and languages, but the best methods seem to be useful in all tested languages and match quite well with the linguistic gold standard. The Morpho Challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

Matti Varjokallio - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised morpheme analysis evaluation by a comparison to a linguistic gold standard morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Matti Varjokallio
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 1 (linguistic gold standard). The Competition 2 (information retrieval) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling The choice of a meaningful evaluation for the submitted morpheme analysis was not straight-forward, because in unsupervised morpheme analysis the morphemes can have arbitrary names. Two complementary ways were developed for the evaluation: Competition 1: The proposed morpheme analyses were compared to a linguistic morpheme analysis gold standard by matching the morpheme-sharing word pairs. Competition 2: Information retrieval (IR) experiments were performed, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. Data sets for Competition 1 were provided for four languages: Finnish, German, English, and Turkish and the participants were encouraged to apply their algorithm to all of them. The results show significant variance between the methods and languages, but the best methods seem to be useful in all tested languages and match quite well with the linguistic gold standard. The Morpho Challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

  • unsupervised morpheme analysis evaluation by a comparison to a linguistic gold standard morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Matti Varjokallio
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 1 (linguistic gold standard). The Competition 2 (information retrieval) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling The choice of a meaningful evaluation for the submitted morpheme analysis was not straight-forward, because in unsupervised morpheme analysis the morphemes can have arbitrary names. Two complementary ways were developed for the evaluation: Competition 1: The proposed morpheme analyses were compared to a linguistic morpheme analysis gold standard by matching the morpheme-sharing word pairs. Competition 2: Information retrieval (IR) experiments were performed, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. Data sets for Competition 1 were provided for four languages: Finnish, German, English, and Turkish and the participants were encouraged to apply their algorithm to all of them. The results show significant variance between the methods and languages, but the best methods seem to be useful in all tested languages and match quite well with the linguistic gold standard. The Morpho Challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

Mathias Creutz - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised morpheme analysis evaluation by a comparison to a linguistic gold standard morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Matti Varjokallio
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 1 (linguistic gold standard). The Competition 2 (information retrieval) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling The choice of a meaningful evaluation for the submitted morpheme analysis was not straight-forward, because in unsupervised morpheme analysis the morphemes can have arbitrary names. Two complementary ways were developed for the evaluation: Competition 1: The proposed morpheme analyses were compared to a linguistic morpheme analysis gold standard by matching the morpheme-sharing word pairs. Competition 2: Information retrieval (IR) experiments were performed, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. Data sets for Competition 1 were provided for four languages: Finnish, German, English, and Turkish and the participants were encouraged to apply their algorithm to all of them. The results show significant variance between the methods and languages, but the best methods seem to be useful in all tested languages and match quite well with the linguistic gold standard. The Morpho Challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

  • unsupervised morpheme analysis evaluation by ir experiments morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Ville T Turunen
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 2 (information retrieval). The Competition 1 (linguistic gold standard) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling In this paper the morpheme analysis submitted by the Challenge participants were evaluated by performing information retrieval (IR) experiments, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. The IR evaluations were provided for three languages: Finnish, German, and English and the participants were encouraged to apply their algorithm to all of them. The challenge organizers performed the IR experiments using the queries, texts, and relevance judgments available in CLEF forum and morpheme analysis methods submitted by the challenge participants. The results show that the morpheme analysis has a significant eect in IR performance in all languages, and that the performance of the best unsupervised methods can be superior to the supervised reference methods. The challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

  • unsupervised morpheme analysis evaluation by ir experiments morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Ville T Turunen
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 2 (information retrieval). The Competition 1 (linguistic gold standard) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling In this paper the morpheme analysis submitted by the Challenge participants were evaluated by performing information retrieval (IR) experiments, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. The IR evaluations were provided for three languages: Finnish, German, and English and the participants were encouraged to apply their algorithm to all of them. The challenge organizers performed the IR experiments using the queries, texts, and relevance judgments available in CLEF forum and morpheme analysis methods submitted by the challenge participants. The results show that the morpheme analysis has a significant eect in IR performance in all languages, and that the performance of the best unsupervised methods can be superior to the supervised reference methods. The challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

  • unsupervised morpheme analysis evaluation by a comparison to a linguistic gold standard morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Matti Varjokallio
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 1 (linguistic gold standard). The Competition 2 (information retrieval) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling The choice of a meaningful evaluation for the submitted morpheme analysis was not straight-forward, because in unsupervised morpheme analysis the morphemes can have arbitrary names. Two complementary ways were developed for the evaluation: Competition 1: The proposed morpheme analyses were compared to a linguistic morpheme analysis gold standard by matching the morpheme-sharing word pairs. Competition 2: Information retrieval (IR) experiments were performed, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. Data sets for Competition 1 were provided for four languages: Finnish, German, English, and Turkish and the participants were encouraged to apply their algorithm to all of them. The results show significant variance between the methods and languages, but the best methods seem to be useful in all tested languages and match quite well with the linguistic gold standard. The Morpho Challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

Ville T Turunen - One of the best experts on this subject based on the ideXlab platform.

  • unsupervised morpheme analysis evaluation by ir experiments morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Ville T Turunen
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 2 (information retrieval). The Competition 1 (linguistic gold standard) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling In this paper the morpheme analysis submitted by the Challenge participants were evaluated by performing information retrieval (IR) experiments, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. The IR evaluations were provided for three languages: Finnish, German, and English and the participants were encouraged to apply their algorithm to all of them. The challenge organizers performed the IR experiments using the queries, texts, and relevance judgments available in CLEF forum and morpheme analysis methods submitted by the challenge participants. The results show that the morpheme analysis has a significant eect in IR performance in all languages, and that the performance of the best unsupervised methods can be superior to the supervised reference methods. The challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

  • unsupervised morpheme analysis evaluation by ir experiments morpho challenge 2007
    CLEF (Working Notes), 2007
    Co-Authors: Mikko Kurimo, Mathias Creutz, Ville T Turunen
    Abstract:

    This paper presents the evaluation of Morpho Challenge Competition 2 (information retrieval). The Competition 1 (linguistic gold standard) is described in a companion paper. In Morpho Challenge 2007, the objective was to design statistical machine learning algorithms that discover which morphemes (smallest individually meaningful units of language) words consist of. Ideally, these are Basic Vocabulary units suitable for dierent tasks, such as text understanding, machine translation, information retrieval, and statistical language modeling In this paper the morpheme analysis submitted by the Challenge participants were evaluated by performing information retrieval (IR) experiments, where the words in the documents and queries were replaced by their proposed morpheme representations and the search was based on morphemes instead of words. The IR evaluations were provided for three languages: Finnish, German, and English and the participants were encouraged to apply their algorithm to all of them. The challenge organizers performed the IR experiments using the queries, texts, and relevance judgments available in CLEF forum and morpheme analysis methods submitted by the challenge participants. The results show that the morpheme analysis has a significant eect in IR performance in all languages, and that the performance of the best unsupervised methods can be superior to the supervised reference methods. The challenge was part of the EU Network of Excellence PASCAL Challenge Program and organized in collaboration with CLEF.

Soren Wichmann - One of the best experts on this subject based on the ideXlab platform.

  • cultural phylogenetics of the tupi language family in lowland south america
    PLOS ONE, 2012
    Co-Authors: Robert S Walker, Soren Wichmann, Thomas Mailund, Curtis Atkisson
    Abstract:

    Background Recent advances in automated assessment of Basic Vocabulary lists allow the construction of linguistic phylogenies useful for tracing dynamics of human population expansions, reconstructing ancestral cultures, and modeling transition rates of cultural traits over time.

  • sound symbolism in Basic Vocabulary
    Entropy, 2010
    Co-Authors: Soren Wichmann, Eric W Holman, Cecil H Brown
    Abstract:

    The relationship between meanings of words and their sound shapes is to a large extent arbitrary, but it is well known that languages exhibit sound symbolism effects violating arbitrariness. Evidence for sound symbolism is typically anecdotal, however. Here we present a systematic approach. Using a selection of Basic Vocabulary in nearly one half of the world’s languages we find commonalities among sound shapes for words referring to same concepts. These are interpreted as due to sound symbolism. Studying the effects of sound symbolism cross-linguistically is of key importance for the understanding of language evolution.