This article presents a hybrid methodology for building a multilingual corpus designed to support the study of emerging concepts in the humanities and social sciences (HSS), illustrated here through the case of ``non-technological innovation''. The corpus relies on two complementary sources: (1) textual content automatically extracted from company websites, cleaned for French and English, and (2) annual reports collected and automatically filtered according to documentary criteria (year, format, duplication). The processing pipeline includes automatic language detection, filtering of non-relevant content, extraction of relevant segments, and enrichment with structural metadata. From this initial corpus, a derived dataset in English is created for machine learning purposes. For each occurrence of a term from the expert lexicon, a contextual block of five sentences is extracted (two preceding and two following the sentence containing the term). Each occurrence is annotated with the thematic category associated with the term, enabling the construction of data suitable for supervised classification tasks. This approach results in a reproducible and extensible resource, suitable both for analyzing lexical variability around emerging concepts and for generating datasets dedicated to natural language processing applications.

In this thesis, we developed a comprehensive framework for sentiment analysis that takes its many aspects into account mainly for Turkish. We have also proposed several approaches specific to sentiment analysis in English only. We have accordingly made five major and three minor contributions. We generated a novel and effective feature set by combining unsupervised, semi-supervised, and supervised metrics. We then fed them as input into classical machine learning methods, and outperformed neural network models for datasets of different genres in both Turkish and English. We created a polarity lexicon with a semi-supervised domain-specific method, which has been the first approach applied for corpora in Turkish. We performed a fine morphological analysis for the sentiment classification task in Turkish by determining the polarities of morphemes. This can be adapted to other morphologically-rich or agglutinative languages as well. We have built a novel neural network architecture, which combines recurrent and recursive neural network models for English. We built novel word embeddings that exploit sentiment, syntactic, semantic, and lexical characteristics for both Turkish and English. We also redefined context windows as subclauses in modelling word representations in English. This can also be applied to other linguistic fields and natural language processing tasks. We have achieved state-of-the-art and significant results for all these original approaches. Our minor contributions include methods related to aspect-based sentiment in Turkish, parameter redefinition in the semi-supervised approach, and aspect term extraction techniques for English. This thesis can be considered the most detailed and comprehensive study made on sentiment analysis in Turkish as of July, 2020. Our work has also contributed to the opinion classification problem in English.

Neural Information Processing Systems http://nips.cc/

We investigate tokenization strategies for Kurdish word embeddings by comparing word-level, morpheme-based, and BPE approaches on morphological similarity preservation tasks. We develop a BiLSTM-CRF morphological segmenter using bootstrapped training from minimal manual annotation and evaluate Word2Vec embeddings across comprehensive metrics including similarity preservation, clustering quality, and semantic organization. Our analysis reveals critical evaluation biases in tokenization comparison. While BPE initially appears superior in morphological similarity, it evaluates only 28.6\% of test cases compared to 68.7\% for morpheme model, creating artificial performance inflation. When assessed comprehensively, morpheme-based tokenization demonstrates superior embedding space organization, better semantic neighborhood structure, and more balanced coverage across morphological complexity levels. These findings highlight the importance of coverage-aware evaluation in low-resource language processing and offers different tokenization methods for low-resourced language processing.

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New York's new mayor Zohran Mamdani tells Trump'I have four words for you' in blistering victory speech quoting his socialist hero, bragging about'toppling a dynasty' and promising a'new dawn' Driver screaming'Allahu Akbar' ploughs in to pedestrians'trying to hit everyone he encountered' on French holiday island leaving ten injured This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. Why Mamdani's socialist revolution in New York has sparked a civil war for Democrats... and Trump is secretly loving it Simone Biles details all the plastic surgery she's had after her boob job this summer Kim Kardashian's new TV show All's Fair is SAVAGED by critics as it's branded'the worst drama ever', 'existentially terrible' and'a crime against television' while debuting at 0% on Rotten Tomatoes Putin orders increased drone'incursions' in Europe as Russia ramps up'hybrid war' - with Belgium's biggest airport forced to close overnight Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look REVEALED: Fattest states in America ranked... including region where three-quarters of residents are obese I was so desperate for a baby I stole sperm from my husband's condom: It's the most shocking confession. Now for the first time LIZ JONES tells what happened next... and the consequence no one saw New footage reveals the moments before football manager collapsed and died mid-match, leaving his players in disbelief, as it emerges he'complained about fish he had eaten' hours before Hollywood A-listers may be blacklisted for'antisemitism' under Paramount's new anti-woke leadership Texas teen'tears masterpiece from wall at the Met in unhinged meltdown' before being handed in by his MOTHER Bizarre TikTok trend sees Gen Z'rawdogging boredom' to fix their attention spans - so, does it really work? READ MORE: Mark Zuckerberg's'rawdog' routine that made him a billionaire A bizarre new trend has emerged on TikTok, in which Gen Z put themselves in timeout to try to fix their attention spans. Dubbed'rawdogging boredom', users set a timer and simply sit there without any distractions.

New York's new mayor Zohran Mamdani tells Trump'I have four words for you' in blistering victory speech quoting his socialist hero, bragging about'toppling a dynasty' and promising a'new dawn' This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. Why Mamdani's socialist revolution in New York has sparked a civil war for Democrats... and Trump is secretly loving it Simone Biles details all the plastic surgery she's had after her boob job this summer Hollywood A-listers may be blacklisted for'antisemitism' under Paramount's new anti-woke leadership Prince Harry issues defiant statement as he denies claims he was trying to upstage William by announcing pseudo-royal Canada trip at same time as his brother's five-day tour of Brazil Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look REVEALED: Fattest states in America ranked... including region where three-quarters of residents are obese I was so desperate for a baby I stole sperm from my husband's condom: It's the most shocking confession. Now for the first time LIZ JONES tells what happened next... and the consequence no one saw Texas teen'tears masterpiece from wall at the Met in unhinged meltdown' before being handed in by his MOTHER Amazon signals it's finally fed up with Whole Foods' sluggish sales - and is making sweeping, controversial changes READ MORE: Was IKEA right about the kitchen of 2025? The beloved kettle is among the kitchen appliances that will have vanished in 50 years' time thanks to Gen Z, new research suggests. Today's youngsters will fuel the move away from bulky, wired kitchen appliances favoured by older generations, according to experts.

New York's new mayor Zohran Mamdani tells Trump'I have four words for you' in blistering victory speech quoting his socialist hero, bragging about'toppling a dynasty' and promising a'new dawn' This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. Why Mamdani's socialist revolution in New York has sparked a civil war for Democrats... and Trump is secretly loving it Simone Biles details all the plastic surgery she's had after her boob job this summer Hollywood A-listers may be blacklisted for'antisemitism' under Paramount's new anti-woke leadership Prince Harry issues defiant statement as he denies claims he was trying to upstage William by announcing pseudo-royal Canada trip at same time as his brother's five-day tour of Brazil Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look REVEALED: Fattest states in America ranked... including region where three-quarters of residents are obese I was so desperate for a baby I stole sperm from my husband's condom: It's the most shocking confession. Now for the first time LIZ JONES tells what happened next... and the consequence no one saw Texas teen'tears masterpiece from wall at the Met in unhinged meltdown' before being handed in by his MOTHER Amazon signals it's finally fed up with Whole Foods' sluggish sales - and is making sweeping, controversial changes The biggest supermoon of the year will light up skies around the world this evening - and you don't want to miss it. At its peak, our lunar satellite will appear eight per cent larger and 16 per cent brighter than usual. The phenomenon occurs because the moon's orbit is not perfectly circular around Earth, meaning at times it is slightly closer or slightly further away.