As companies aim to enhance and expand their product portfolios, Technology Opportunity Discovery (TOD) has gained increasing interest. To comprehend the role of emerging technologies in innovation, we introduce a novel technology-market corpus in English and Japanese languages, and conduct a comprehensive empirical evaluation of the linkage between technology and the market. Our dataset comprises English patents extracted from the USPTO database and Japanese patents from the Japanese Patent Office (JPO), along with their associated products for each stock market company. We compare several static and contextualized word embedding methods to construct a technology-market space and propose an effective methodology based on a fine-tuned BERT model for linking technology to the market.

Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and an important basis for further natural language processing applications. Even with persistent improvements, ATE still exhibits weak results exacerbated by small training data inherent to specialized domain corpora. Recently, transformers-based deep neural models, such as BERT, have proven to be efficient in many downstream NLP tasks. However, no systematic evaluation of ATE has been conducted so far. In this paper, we run an extensive study on fine-tuning pre-trained BERT models for ATE. We propose strategies that empirically show BERT’s effectiveness using cross-lingual and cross-domain transfer learning to extract single and multi-word terms. Experiments have been conducted on four specialized domains in three languages. The obtained results suggest that BERT can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE.

In this paper, we address the segmentation of books of hours, Latin devotional manuscripts of the late Middle Ages, that exhibit challenging issues: a complex hierarchical entangled structure, variable content, noisy transcriptions with no sentence markers, and strong correlations between sections for which topical information is no longer sufficient to draw segmentation boundaries.We show that the main state-of-the-art segmentation methods are either inefficient or inapplicable for books of hours and propose a bottom-up greedy approach that considerably enhances the segmentation results.We stress the importance of such hierarchical segmentation of books of hours for historians to explore their overarching differences underlying conception about Church.

Narrow specialized comparable corpora are often small in size.This particularity makes it difficult to build efficient models to acquire translation equivalents, especially for less frequent and rare words.One way to overcome this issue is to enrich the specialized corpora with out-ofdomain resources.Although some recent studies have shown improvements using data augmentation, the enrichment method was roughly conducted by adding out-of-domain data with no particular attention given to how to enrich words and how to do it optimally.In this paper, we contrast several data selection techniques to improve bilingual lexicon induction from specialized comparable corpora.We first apply two well-established data selection techniques often used in machine translation that is: Tf-Idf and cross entropy.Then, we propose to exploit BERT for data selection.Overall, all the proposed techniques improve the quality of the extracted bilingual lexicons by a large margin.The best performing model is the cross entropy, obtaining a gain of about 4 points in MAP while decreasing computation time by a factor of 10.

The Book of Hours was the bestseller of the late Middle Ages and Renaissance. It is a historical invaluable treasure, documenting the devotional practices of Christians in the late Middle Ages. Up to now, its textual content has been scarcely studied because of its manuscript nature, its length and its complex content. At first glance, it looks too standardized. However, the study of book of hours raises important challenges: (i) in image analysis, its often lavish ornamentation (illegible painted initials, line-fillers, etc.), abbreviated words, multilingualism are difficult to address in Handwritten Text Recognition (HTR); (ii) its hierarchical entangled structure offers a new field of investigation for text segmentation; (iii) in digital humanities, its textual content gives opportunities for historical analysis. In this paper, we provide the first corpus of books of hours, which consists of Latin transcriptions of 300 books of hours generated by Handwritten Text Recognition (HTR) - that is like Optical Character Recognition (OCR) but for handwritten and not printed texts. We designed a structural scheme of the book of hours and annotated manually two books of hours according to this scheme. Lastly, we performed a systematic evaluation of the main state of the art text segmentation approaches.

Recent evaluations on bilingual lexicon extraction from specialized comparable corpora have shown contrasted performance while using word embedding models. This can be partially explained by the lack of large specialized comparable corpora to build efficient representations. Within this context, we try to answer the following questions: First, (i) among the state-of-the-art embedding models, whether trained on specialized corpora or pre-trained on large general data sets, which one is the most appropriate model for bilingual terminology extraction? Second (ii) is it worth it to combine multiple embeddings trained on different data sets? For that purpose, we propose the first systematic evaluation of different word embedding models for bilingual terminology extraction from specialized comparable corpora. We emphasize how the character-based embedding model outperforms other models on the quality of the extracted bilingual lexicons. Further more, we propose a new efficient way to combine different embedding models learned from specialized and general-domain data sets. Our approach leads to higher performance than the best individual embedding model.

Bilingual lexicon extraction from comparable corpora is constrained by the small amount of available data when dealing with specialized domains. This aspect penalizes the performance of distributional-based approaches, which is closely related to the reliability of word’s cooccurrence counts extracted from comparable corpora. A solution to avoid this limitation is to associate external resources with the comparable corpus. Since bilingual word embeddings have recently shown efficient models for learning bilingual distributed representation of words, we explore different word embedding models and show how a general-domain comparable corpus can enrich a specialized comparable corpus via neural networks

Comparable corpora are the main alternative to the use of parallel corpora to extract bilingual lexicons. Although it is easier to build comparable corpora, specialized comparable corpora are often of modest size in comparison with corpora issued from the general domain. Consequently, the observations of word co-occurrences which are the basis of context-based methods are unreliable. We propose in this article to improve word co-occurrences of specialized comparable corpora and thus context representation by using general-domain data. This idea, which has been already used in machine translation task for more than a decade, is not straightforward for the task of bilingual lexicon extraction from specific-domain comparable corpora. We go against the mainstream of this task where many studies support the idea that adding out-of-domain documents decreases the quality of lexicons. Our empirical evaluation shows the advantages of this approach which induces a significant gain in the accuracy of extracted lexicons.

Abstract The main work in bilingual lexicon extraction from comparable corpora is based on the implicit hypothesis that corpora are balanced in terms of size. However, the historical context-based projection method is relatively insensitive to the size of each part of the comparable corpus. Within this context, we have carried out a study on the influence of unbalanced specialized comparable corpora and on the quality of bilingual terminology extraction by doing different experiments. Moreover, we have introduced a strategy into the context-based projection method to re-estimate word co-occurrence observations. This is done by using smoothing or prediction techniques that boost the observations of word co-occurrences which are mainly useful for the smallest part of an unbalanced comparable corpus. Our results show that the use of unbalanced specialized comparable corpora results in a significant improvement in the quality of extracted lexicons.

Frédéric Blain, Fethi Bougares, Amir Hazem, Loïc Barrault, Holger Schwenk. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

The main work in bilingual lexicon extraction from comparable corpora is based on the implicit hypothesis that corpora are balanced. However, the historical contextbased projection method dedicated to this task is relatively insensitive to the sizes of each part of the comparable corpus. Within this context, we have carried out a study on the influence of unbalanced specialized comparable corpora on the quality of bilingual terminology extraction through different experiments. Moreover, we have introduced a regression model that boosts the observations of word cooccurrences used in the context-based projection method. Our results show that the use of unbalanced specialized comparable corpora induces a significant gain in the quality of extracted lexicons.