The Experts below are selected from a list of 105 Experts worldwide ranked by ideXlab platform
J.-p. Nadal - One of the best experts on this subject based on the ideXlab platform.
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Frequency patterns of semantic change: corpus-based evidence of a near-critical dynamics in language change
Royal Society Open Science, 2017Co-Authors: Quentin Feltgen, Benjamin Fagard, J.-p. NadalAbstract:It is generally believed that when a Linguistic Item acquires a new meaning, its overall frequency of use rises with time with an S-shaped growth curve. Yet, this claim has only been supported by a limited number of case studies. In this paper, we provide the first corpus-based large-scale confirmation of the S-curve in language change. Moreover, we uncover another generic pattern, a latency phase preceding the S-growth, during which the frequency remains close to constant. We propose a usage-based model which predicts both phases, the latency and the S-growth. The driving mechanism is a random walk in the space of frequency of use. The underlying deterministic dynamics highlights the role of a control parameter which tunes the system at the vicinity of a saddle-node bifurcation. In the neighbourhood of the critical point, the latency phase corresponds to the diffusion time over the critical region, and the S-growth to the fast convergence that follows. The durations of the two phases are computed as specific first-passage times, leading to distributions that fit well the ones extracted from our dataset. We argue that our results are not specific to the studied corpus, but apply to semantic change in general.
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Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change
2017Co-Authors: Quentin Feltgen, Benjamin Fagard, J.-p. NadalAbstract:It is generally believed that, when a Linguistic Item acquires a new meaning, its overall frequency of use in the language rises with time with an S-shaped growth curve. Yet, this claim has only been supported by a limited number of case studies. In this paper, we provide the first corpus-based quantitative confirmation of the genericity of the S-curve in language change. Moreover, we uncover another generic pattern, a latency phase of variable duration preceding the S-growth, during which the frequency of use of the semantically expanding word remains low and more or less constant. We also propose a usage-based model of language change supported by cognitive considerations, which predicts that both phases, the latency and the fast S-growth, take place. The driving mechanism is a stochastic dynamics, a random walk in the space of frequency of use. The underlying deterministic dynamics highlights the role of a control parameter, the strength of the cognitive impetus governing the onset of change, which tunes the system at the vicinity of a saddle-node bifurcation. In the neighborhood of the critical point, the latency phase corresponds to the diffusion time over the critical region, and the S-growth to the fast convergence that follows. The duration of the two phases is computed as specific first passage times of the random walk process, leading to distributions that fit well the ones extracted from our dataset. We argue that our results are not specific to the studied corpus, but apply to semantic change in general.
Roberto Navigli - One of the best experts on this subject based on the ideXlab platform.
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Cross level semantic similarity: an evaluation framework for universal measures of similarity
Language Resources and Evaluation, 2016Co-Authors: Daniel Jürgens, Mohammad Taher Pilehvar, Roberto NavigliAbstract:Semantic similarity has typically been measured across Items of approximately similar sizes. As a result, similarity measures have largely ignored the fact that different types of Linguistic Item can potentially have similar or even identical meanings, and therefore are designed to compare only one type of Linguistic Item. Furthermore, nearly all current similarity benchmarks within NLP contain pairs of approximately the same size, such as word or sentence pairs, preventing the evaluation of methods that are capable of comparing different sized Items. To address this, we introduce a new semantic evaluation called cross-level semantic similarity (CLSS), which measures the degree to which the meaning of a larger Linguistic Item, such as a paragraph, is captured by a smaller Item, such as a sentence. Our pilot CLSS task was presented as part of SemEval-2014, which attracted 19 teams who submitted 38 systems. CLSS data contains a rich mixture of pairs, spanning from paragraphs to word senses to fully evaluate similarity measures that are capable of comparing Items of any type. Furthermore, data sources were drawn from diverse corpora beyond just newswire, including domain-specific texts and social media. We describe the annotation process and its challenges, including a comparison with crowdsourcing, and identify the factors that make the dataset a rigorous assessment of a method’s quality. Furthermore, we examine in detail the systems participating in the SemEval task to identify the common factors associated with high performance and which aspects proved difficult to all systems. Our findings demonstrate that CLSS poses a significant challenge for similarity methods and provides clear directions for future work on universal similarity methods that can compare any pair of Items.
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From senses to texts
Artificial Intelligence, 2015Co-Authors: Mohammad Taher Pilehvar, Roberto NavigliAbstract:Quantifying semantic similarity between Linguistic Items lies at the core of many applications in Natural Language Processing and Artificial Intelligence. It has therefore received a considerable amount of research interest, which in its turn has led to a wide range of approaches for measuring semantic similarity. However, these measures are usually limited to handling specific types of Linguistic Item, e.g., single word senses or entire sentences. Hence, for a downstream application to handle various types of input, multiple measures of semantic similarity are needed, measures that often use different internal representations or have different output scales. In this article we present a unified graph-based approach for measuring semantic similarity which enables effective comparison of Linguistic Items at multiple levels, from word senses to full texts. Our method first leverages the structural properties of a semantic network in order to model arbitrary Linguistic Items through a unified probabilistic representation, and then compares the Linguistic Items in terms of their representations. We report state-of-the-art performance on multiple datasets pertaining to three different levels: senses, words, and texts.
Quentin Feltgen - One of the best experts on this subject based on the ideXlab platform.
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Frequency patterns of semantic change: corpus-based evidence of a near-critical dynamics in language change
Royal Society Open Science, 2017Co-Authors: Quentin Feltgen, Benjamin Fagard, J.-p. NadalAbstract:It is generally believed that when a Linguistic Item acquires a new meaning, its overall frequency of use rises with time with an S-shaped growth curve. Yet, this claim has only been supported by a limited number of case studies. In this paper, we provide the first corpus-based large-scale confirmation of the S-curve in language change. Moreover, we uncover another generic pattern, a latency phase preceding the S-growth, during which the frequency remains close to constant. We propose a usage-based model which predicts both phases, the latency and the S-growth. The driving mechanism is a random walk in the space of frequency of use. The underlying deterministic dynamics highlights the role of a control parameter which tunes the system at the vicinity of a saddle-node bifurcation. In the neighbourhood of the critical point, the latency phase corresponds to the diffusion time over the critical region, and the S-growth to the fast convergence that follows. The durations of the two phases are computed as specific first-passage times, leading to distributions that fit well the ones extracted from our dataset. We argue that our results are not specific to the studied corpus, but apply to semantic change in general.
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Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change
2017Co-Authors: Quentin Feltgen, Benjamin Fagard, J.-p. NadalAbstract:It is generally believed that, when a Linguistic Item acquires a new meaning, its overall frequency of use in the language rises with time with an S-shaped growth curve. Yet, this claim has only been supported by a limited number of case studies. In this paper, we provide the first corpus-based quantitative confirmation of the genericity of the S-curve in language change. Moreover, we uncover another generic pattern, a latency phase of variable duration preceding the S-growth, during which the frequency of use of the semantically expanding word remains low and more or less constant. We also propose a usage-based model of language change supported by cognitive considerations, which predicts that both phases, the latency and the fast S-growth, take place. The driving mechanism is a stochastic dynamics, a random walk in the space of frequency of use. The underlying deterministic dynamics highlights the role of a control parameter, the strength of the cognitive impetus governing the onset of change, which tunes the system at the vicinity of a saddle-node bifurcation. In the neighborhood of the critical point, the latency phase corresponds to the diffusion time over the critical region, and the S-growth to the fast convergence that follows. The duration of the two phases is computed as specific first passage times of the random walk process, leading to distributions that fit well the ones extracted from our dataset. We argue that our results are not specific to the studied corpus, but apply to semantic change in general.
Mohammad Taher Pilehvar - One of the best experts on this subject based on the ideXlab platform.
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Cross level semantic similarity: an evaluation framework for universal measures of similarity
Language Resources and Evaluation, 2016Co-Authors: Daniel Jürgens, Mohammad Taher Pilehvar, Roberto NavigliAbstract:Semantic similarity has typically been measured across Items of approximately similar sizes. As a result, similarity measures have largely ignored the fact that different types of Linguistic Item can potentially have similar or even identical meanings, and therefore are designed to compare only one type of Linguistic Item. Furthermore, nearly all current similarity benchmarks within NLP contain pairs of approximately the same size, such as word or sentence pairs, preventing the evaluation of methods that are capable of comparing different sized Items. To address this, we introduce a new semantic evaluation called cross-level semantic similarity (CLSS), which measures the degree to which the meaning of a larger Linguistic Item, such as a paragraph, is captured by a smaller Item, such as a sentence. Our pilot CLSS task was presented as part of SemEval-2014, which attracted 19 teams who submitted 38 systems. CLSS data contains a rich mixture of pairs, spanning from paragraphs to word senses to fully evaluate similarity measures that are capable of comparing Items of any type. Furthermore, data sources were drawn from diverse corpora beyond just newswire, including domain-specific texts and social media. We describe the annotation process and its challenges, including a comparison with crowdsourcing, and identify the factors that make the dataset a rigorous assessment of a method’s quality. Furthermore, we examine in detail the systems participating in the SemEval task to identify the common factors associated with high performance and which aspects proved difficult to all systems. Our findings demonstrate that CLSS poses a significant challenge for similarity methods and provides clear directions for future work on universal similarity methods that can compare any pair of Items.
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From senses to texts
Artificial Intelligence, 2015Co-Authors: Mohammad Taher Pilehvar, Roberto NavigliAbstract:Quantifying semantic similarity between Linguistic Items lies at the core of many applications in Natural Language Processing and Artificial Intelligence. It has therefore received a considerable amount of research interest, which in its turn has led to a wide range of approaches for measuring semantic similarity. However, these measures are usually limited to handling specific types of Linguistic Item, e.g., single word senses or entire sentences. Hence, for a downstream application to handle various types of input, multiple measures of semantic similarity are needed, measures that often use different internal representations or have different output scales. In this article we present a unified graph-based approach for measuring semantic similarity which enables effective comparison of Linguistic Items at multiple levels, from word senses to full texts. Our method first leverages the structural properties of a semantic network in order to model arbitrary Linguistic Items through a unified probabilistic representation, and then compares the Linguistic Items in terms of their representations. We report state-of-the-art performance on multiple datasets pertaining to three different levels: senses, words, and texts.
Benjamin Fagard - One of the best experts on this subject based on the ideXlab platform.
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Frequency patterns of semantic change: corpus-based evidence of a near-critical dynamics in language change
Royal Society Open Science, 2017Co-Authors: Quentin Feltgen, Benjamin Fagard, J.-p. NadalAbstract:It is generally believed that when a Linguistic Item acquires a new meaning, its overall frequency of use rises with time with an S-shaped growth curve. Yet, this claim has only been supported by a limited number of case studies. In this paper, we provide the first corpus-based large-scale confirmation of the S-curve in language change. Moreover, we uncover another generic pattern, a latency phase preceding the S-growth, during which the frequency remains close to constant. We propose a usage-based model which predicts both phases, the latency and the S-growth. The driving mechanism is a random walk in the space of frequency of use. The underlying deterministic dynamics highlights the role of a control parameter which tunes the system at the vicinity of a saddle-node bifurcation. In the neighbourhood of the critical point, the latency phase corresponds to the diffusion time over the critical region, and the S-growth to the fast convergence that follows. The durations of the two phases are computed as specific first-passage times, leading to distributions that fit well the ones extracted from our dataset. We argue that our results are not specific to the studied corpus, but apply to semantic change in general.
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Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change
2017Co-Authors: Quentin Feltgen, Benjamin Fagard, J.-p. NadalAbstract:It is generally believed that, when a Linguistic Item acquires a new meaning, its overall frequency of use in the language rises with time with an S-shaped growth curve. Yet, this claim has only been supported by a limited number of case studies. In this paper, we provide the first corpus-based quantitative confirmation of the genericity of the S-curve in language change. Moreover, we uncover another generic pattern, a latency phase of variable duration preceding the S-growth, during which the frequency of use of the semantically expanding word remains low and more or less constant. We also propose a usage-based model of language change supported by cognitive considerations, which predicts that both phases, the latency and the fast S-growth, take place. The driving mechanism is a stochastic dynamics, a random walk in the space of frequency of use. The underlying deterministic dynamics highlights the role of a control parameter, the strength of the cognitive impetus governing the onset of change, which tunes the system at the vicinity of a saddle-node bifurcation. In the neighborhood of the critical point, the latency phase corresponds to the diffusion time over the critical region, and the S-growth to the fast convergence that follows. The duration of the two phases is computed as specific first passage times of the random walk process, leading to distributions that fit well the ones extracted from our dataset. We argue that our results are not specific to the studied corpus, but apply to semantic change in general.