Six Sonatas for Violin solo op. Henle Verlag Urtext Study Scores. Die elementare Streichermethode: Band 1. Belle Epoque Russe. Sonate in h-Moll: Violoncello. Schwierigkeitgrad: 4.
Akkordeon; 2. Stimme ad libitum. Wanderlust: Eine Sammlung deutscher Volkslieder. Mondschein-Sonate: Erster Satz. Lemare pour orgue. Ride of the Valkeries arr. Having completed a doctorate in Law from the university of Toulouse, he moved to Paris. Le Rocher, , soon followed by his first collection of poems, Le chauffeur est toujours seul'' , to critical acclaim. He published the first French translation of a collection of poetry by F. The result — as in all literature, and even in all works of art — is thought.
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A selection of his poems was published in with the title En souvenir des long-courriers. As an editor at Grasset's, he published many award-winning literary authors. He's also a producer on the cultural public radio France Culture, where he created "Secret professionnel", on the artistic creation, one of the 10 best radio shows , now followed by "Personnages en personne", a weekly portrait of a fictional character.
In he re-created the famous Stendhal Club, which he presides. The Stendhal Club has published two issues of his "possibly annual review".
In , he landed a new literary collection under his name, "Le Courage". It is the only multilingual review in the world, presenting every text in its original language and with its french traduction. In , the review has received the Paris Rive gauche award of the Best literary review. It recounted the life of a poet elected president of France, who went on to start a war.
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The third, Un film d'amour , was published in It was a choral novel with a scholarly structure that supposedly drew on a TV documentary on the death of a young film-maker by the name of Birbillaz. It leaves behind its ostensible subject, the portrait of the absent figure, Birbillaz, to focus on his brother — his double, his mirror image — like something out of Robert Louis Stevenson: a failure in life, bitter, rotten to the core, who says no to everything, to the point of obstinacy and pain.
No to love, to talent, to creativity, to goodness, to beauty. It was inspired by the real-life crimes of Christophe Rocancourt, which the author transformed and transfigured into a new fictional destiny. In August appears "Dans un avion pour Caracas", a novel entirely happening in a plane flight between Paris and Caracas. In , Grasset published Charles Dantzig's fifth novel, Histoire de l'amour et de la haine , which tells about the turmoil that occurred in France in on the occasion of the voting of the law for same-sex marriage.
Named Entity Recognition NER has become reasonably well developed, driven notably by the needs for machine translation and automatic information extraction out of flows of news articles Hirschberg and Manning, However, only a small number of systems perform well on languages other than English, and in particular, as in our case, on French contents Azpeitia et al. Previous initiatives targeted specifically old French newspapers corpora Mosallam et al. Therefore, solutions that can achieve good results without the need of training beforehand are highly relevant.
Our solution 4 combines a set of different standalone unsupervised recognition strategies feeding a meta-recognition system. This approach, whose concept was first described aiming for improved accuracy needs in biometrics, has proven to be effective in various other fields Scheirer et al. The rationale behind meta-recognition is to reach out for a set of independent classifiers, each looking at different features and possibly using different strategies. One would then expect that although none of the standalone method clearly outperforms the others, their combined judgment offers a robust solution to the tackled recognition problem, as their individual errors will tend to cancel out.
The next sections describe the performances obtained with state of the art tools compared to each of our classifiers on a 5M tokens test corpus, and the overall gain brought by averaging their results using a fixed weight and a meta-recognition algorithm. The conclusion outlines some applications for analyses of novels that can be performed on the outputted categorization. We built a test corpus made out of 35 classic works 5M tokens in French, stemming from digitized books part of Project Gutenberg.
They include 3 sagas, 6 classical books that were translated from other languages and 25 French classical novels. For each piece of work, we produced a corresponding reference file by extracting the proper nouns occurring more than five times and manually labeling each of them with the category they fall in character, place, or other. The resulting database references about 3, categorized named entities, along with the work they come from and their number of occurrences.
Given our further needs, we considered names referring to pets, companies, and groups of persons as regular characters whenever they serve the same narrative purpose people mostly interacting with corporations or groups of people. This differs a lot from most knowledge databases, as the very bounds between characters and places might be fuzzy and context dependent.
For the actual test procedure, we ran each classifier on our corpus and computed the averagely yield precision positive predictive value , recall true positive rate and F 1 score of the classification task.
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Section 2 shortly discusses state of the art of existing tools under the same constraints, and sections 3. The field of NER is evolving quickly, as big data approaches and access to increasingly large corpora brought up several projects promising interesting results. However, these tools remain mainly purposed to summarize, categorize, and extract meaning out of short texts that relate to the outside world e. Analyses on big portions of self-contained texts like entire chapters or full fiction books, whose named entities are difficult to look up and interpret out of their context, remain a challenging endeavor.
Novels in particular are different from traditional applications for NER techniques for at least two reasons:. Many traditional NER methods do not exploit this characteristic as they on the contrary rely on external databases to identify and classify named entities Jovanovic et al. This phenomenon is obvious enough that it was shown to allow to confidently attribute texts to their authors in many contexts Stamatatos, This intrinsic diversity makes a one-size-fits-all approach to NER difficult and is problematic for pattern-inducing machine learning algorithms. In order to establish a baseline for our study, we conducted an initial evaluation with one of the current widely used NER systems.
Even if it was not designed to handle big amounts of text, 7 its processing routine is efficient and produces XML result files that can easily be processed and compared to our reference data. Since OpeNER is extracting a wider range of entities than the ones we considered such as date and time information , we only considered the relevantly tagged words and manually resolved naming differences in order to keep the comparison fair and accurate.
In general, spotting proper nouns in French texts is a rather easy task due to capitalization rules, that are quite similar to English if not simpler due to less false positives e. Still, the task is not entirely trivial, because we need to filter out capitalization due to sentences starts and miscellaneous stylistic effects such as subtitles, quotes, and verses.
Additionally, we want full names extracted as single entities and remove false positives, like honorific titles or named time periods Grevisse and Lenoble-Pinson, In order to do that, we designed a two-pass method:. To do this, we compute all 1, 2, and 3 g of each sentence while letting out the first word. We then keep all isolated proper nouns i. For each of them, we store an index of the sentences they appear in for further processing.
This approach allows to recover all occurrences of each capitalized word, as long as they are not systematically at the start of sentences. In practice, the resulting list needs to be refined thereafter, as some capitalized common nouns still happen to end up in it.
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The reasons for this may be multiple, ranging from sentence tokenization errors, typos in the source text or other stylistic effects that may influcence punctuation and case. This refining can be done accurately and efficiently by combining three strategies:. Figure 1 illustrates that the typical distributions often allow for easy separation, with very few outliers. Figure 1. Typical mean positions of uppercased words in their respective tokenized sentences vs.
Once identified, proper nouns usually fall in three main categories that serve different purposes to narration: they can namely be characters, places , or others brands, abstract concepts, acronyms…. We designed and evaluated six independent classifiers.
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Each classifier gets one word at a time as an input as well as the context that is necessary and relevant for its way of processing data, and returns the predicted category namely character, place , or other. We first present the implementation characteristics of each component before looking in details at the resulting scores. When one encounters a proper noun in a sentence, a good guess on its nature can sometimes easily be taken due to the immediate context.
The simplest case, which we will refer here as obvious context , would be if the noun is immediately preceded by a title or a predicate that hints at what it refers to. For this classifier, we compiled a simple list of obvious context classifiers that allow to make good guesses about the nature of the immediately or next to following proper noun:. In French, like in many other languages, the grammatical structure makes it more likely for sentences to follow a pattern that puts the subject of the action at the beginning, and the location toward the end.
This characteristic can be used when one looks at enough examples to make a simple, yet quite powerful guess about the global roles of the proper nouns. The accuracy of this classifier is indeed strongly dependent on the writing style of the author, as the frequent use of specific figures of speech may break its work hypothesis, and longer sentences may narrow the gap between the categories or blurry the bounds.
This can be seen clearly in Figure 2 , where we show the relative positions of identified classes of names for three different stories. Figure 2. Relative mean position of characters and places names for three classical French novels. This approach is very different from section 3. For this implementation, we compiled lists of words that are more likely but not exclusively to appear, respectively, nearby characters, places, or abstract concepts.
For instance, we expect names of characters to be more often surrounded by words related to emotions, body functions, speech, or professions, whereas names of places would be more closely related to motion verbs, place features, and prepositions. Starting from common nouns that are unambiguously related to one of the categories we are interested in, we used a French synonyms dictionary service 10 to put up a list aiming to be as extensive as possible.
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