In this paper we discuss natural language watermarking, which uses the structure of the sentence constituents in natural language text in order to insert a watermark. This approach is different from techniques, collectively referred to as “text watermarking,” which embed information by modifying the appearance of text elements, such as lines, words, or characters. We provide a survey of the cur...

The method herein proposed detects text lines on handwritten pages which may include either lines oriented in several directions, erasures, or annotations between main lines. The method has a hypothesis-validation strategy which is iteratively activated until the end of the segmentation is reached. At each stage of the process, the best text-line hypothesis is generated in the Hough domain. tak...

This work outlines system and usage principles of the ontology-based information extraction system iDocument. Ontology-based information extraction reuses existing domain knowledge for extracting and annotating relevant information from domain-related text. iDocument provides an architecture, an API, and a user interface for supporting users and developers in ontology based knowledge annotation...

Text mining has an important role to play in aiding experts to construct domain specific ontologies by highlighting the important classes, properties and relations that occur within large text collections. In this paper we propose a systematic framework for discovery of ontological types using typing information complemented with statistical filtering. Preliminary experiments are conducted on t...

The approach described in this paper explores the use of semantic structured representation of sentences extracted from texts for multi-domain sentiment analysis purposes. The presented algorithm is built upon a domain-based supervised approach using index-like structured for representing information extracted from text. The algorithm extracts dependency parse relationships from the sentences c...

Abstract The Query-Focused Text Summarization (QFTS) task aims at building systems that generate the summary of text document(s) based on given query. A key challenge in addressing this is lack large labeled data for training summarization model. In article, we address by exploring a series domain adaptation techniques. Given recent success pre-trained transformer models wide range natural lang...

Domain-generalized few-shot text classification (DG-FSTC) is a new setting for (FSTC). In DG-FSTC, the model meta-trained on multi-domain dataset, and meta-tested unseen datasets with different domains. However, previous methods mostly construct semantic representations by learning from words directly, which limited in domain adaptability. this study, we enhance adaptability of utilizing distri...

We investigate the use of textual Internet conversations for detecting questions in spoken conversations. We compare the text-trained model with models trained on manuallylabeled, domain-matched spoken utterances with and without prosodic features. Overall, the text-trained model achieves over 90% of the performance (measured in Area Under the Curve) of the domain-matched model including prosod...