Désambiguïsation lexicale

Résumé

La désambiguïsation lexicale ou désambigüisation lexicale est la détermination du sens d'un mot dans une phrase lorsque ce mot peut avoir plusieurs sens possibles. Dans la linguistique informatique, la désambiguïsation lexicale est un problème non résolu dans le traitement des langues naturelles et de l'ontologie informatique. La résolution de ce problème permettrait des avancées importantes dans d'autres champs de la linguistique informatique comme l'analyse du discours, l'amélioration de la pertinence des résultats des moteurs de recherche, la résolution des anaphores, la cohérence, l'inférence, etc. Le cerveau humain est très habile pour désambiguïser les sens des mots. Le fait que les langues naturelles demandent souvent l'emploi de cette faculté est un reflet de l'habilité du cerveau dans ce domaine. Autrement dit, la mode de développement des langues humaines reflète (et a contribué à former) la capacité innée des réseaux de neurones du cerveau. Par contre, depuis longtemps,

Source officielle

https://fr.wikipedia.org/wiki/D%C3%A9sambigu%C3%AFsation_lexicale

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Patents have traditionally been used in the history of technology as an indication of the thinking process of the inventors, of the challenges or “reverse salients” they faced, or of the social groups influencing the construction of technology. More recently, historians of science and technology also read them to interpret the way people described technology and how the specific inscriptions of inventions mattered for the justification and operation of the patent system. The digitization of historical patents opens up unique opportunities to assess the feasibility of unsupervised machine learning and natural language methods for such explorations. In this project, we analyze over a million US historical patents from 1830-1930 using a variety of text-based methods, with two major aims: 1) categorizing patents into coherent technical categories, 2) identifying discourses of safety, reflexivity, and environmental concern in technological innovation. We use both frequency-based and context-based methods, and find that a bag of words-based methods such as TDF-IDF and topic modeling do not perform well on semantic categorization due to the linguistic peculiarities of patent specifications. This suggests that a successful approach to categorizing patents would require contextual semantic representations such as Transformers-based methods (e.g. BERT), or static embedding based methods (e.g Word2Vec, Doc2Vec) which have relatively low computational costs but less expressive in some scenarios. We run early experiments using these methods and find that word embedding models are effective in learning semantics from the descriptions of the patents. In this poster, we will describe our early results, as well as exploratory data analysis on this massive historical patents dataset.

2022

Chargement

Word Sense Consistency in Statistical and Neural Machine Translation

Xiao Pu

Different senses of source words must often be rendered by different words in the target language when performing machine translation (MT). Selecting the correct translation of polysemous words can be done based on the contexts of use. However, state-of-the-art MT algorithms generally work on a sentence-by-sentence basis that ignores information across sentences. In this thesis, we address this problem by studying novel contextual approaches to reduce source word ambiguity in order to improve translation quality. The thesis consists of two parts: the first part is devoted to methods for correcting ambiguous word translations by enforcing consistency across sentences, and the second part investigates sense-aware MT systems that address the ambiguity problem for each word. In the first part, we propose to reduce word ambiguity by using lexical consistency, starting from the one-sense-per-discourse hypothesis. If a polysemous word appears multiple times in a discourse, it is likely that occurrences will share the same sense. We first improve the translation of polysemous nouns (Y) in the case when a previous occurrence of a noun as the head of a compound noun phrase (XY) is available in a text. Experiments on two language pairs show that the translations of the targeted polysemous nouns are significantly improved. As compound pairs X Y /Y appear quite infrequently in texts, we extend our work by analysing the repetition of nouns which are not compounds. We propose a method to decide whether two occurrences of the same noun in a source text should be translated consistently. We design a classifier to predict translation consistency based on syntactic and semantic features. We integrate the classifiersâ output into MT. We experiment on two language pairs and show that our method closes up to 50% of the gap in BLEU scores between the baseline and an oracle classifier. In the second part of the thesis, we design sense-aware MT systems that (automatically) select the correct translations of ambiguous words by performing word sense disambiguation (WSD). We demonstrate that WSD can improve MT by widening the source context considered when modeling the senses of potentially ambiguous words. We first design three adaptive clustering algorithms, respectively based on k-means, Chinese restaurant process and random walk. For phrase-based statistical MT, we integrate the sense knowledge as an additional feature through a factored model and show that the combination improves the translation from English to five other languages. As the sense integration appears promising for SMT, we also transfer this approach to the newer neural MT models, which are now state of the art. However, unlike SMT, for which it is easier to use linguistic features, NMT uses vectors for word generation and traditional feature incorporation does not work here. We design a sense-aware NMT model that jointly learns the sense knowledge using an attention-based sense selection mechanism and concatenates the learned sense vectors with word vectors during encoding . Such a concatenation outperforms several baselines. The improvements are significant over both overall and analysed ambiguous words over the same language pairs we experiment with SMT. Overall, the thesis proves that lexical consistency and WSD are practical and workable solutions that lead to global improvements in translation in ranges of 0.2 to 1.5 BLEU score.

EPFL2018

Chargement

Short-term spectral features – and most notably Mel-Frequency Cepstral Coefficients (MFCCs) – are the most widely used descriptors of audio signals and are deployed in a majority of state-of-the-art Music Information Retrieval (MIR) systems. These descriptors have however demonstrated their limitations in the context of speech processing when training and testing conditions of the system do not match, like e.g. in noisy conditions or under a channel mismatch. A related problem has been observed in the context of music processing. It has indeed been hypothesized that MIR algorithms relying on the use of short-term spectral features were unexpectedly picking up on similarities in the production/mastering qualities of music albums. This problem has been referred to as the album-effect in the literature though it has never been studied in depth. It is showed in this thesis how the album-effect relates to the problem of channel mismatch. A measure of robustness to the album-effect is proposed and channel normalization techniques borrowed from the speech processing community are evaluated to help at improving the robustness of short-term spectral features. Alternatively, longer-term features describing critical-band specialized temporal patterns (TRAPs) are adapted to the context of music processing. It is shown how such features can help at describing either timbre or rhythm content depending on the scale considered for analysis and how robust they are to the album-effect. Contrarily to more classic short-term spectral descriptors, TRAP-based features encode some form of prior knowledge of the problem considered through a trained feature extraction chain. The lack of appropriately annotated datasets raises however some new issues when it comes to training the feature extraction chain. Advanced unsupervised learning strategies are considered in this thesis and evaluated against more traditional supervised approaches relying on coarse-grained annotations such as music genres. Specialized learning strategies and specialized architectures are also proposed to compensate for some inherent variability of the data due either to album-related factors or to the dependence of music signals to the tempo of the performance.

EPFL2009

Chargement

Word Sense Consistency in Statistical and Neural Machine Translation

Xiao Pu

EPFL2018

EPFL2009