{"title":"Tracker Text Segmentation Approach: Integrating Complex Lexical and Conversation Cue Features","authors":"C. Chibelushi, B. Sharp","doi":"10.5220/0001740501040113","DOIUrl":"https://doi.org/10.5220/0001740501040113","url":null,"abstract":"While text segmentation is a topic which has received a great attention since 9/11, most of current research projects remain focused on expository texts, stories and broadcast news. Current segmentation methods are well suited for written and structured texts making use of their distinctive macro-level structures. Text segmentation of transcribed multi-party conversation presents a different challenge given the lack of linguistic features such as headings, paragraph, and well formed sentences. This paper describes an algorithm suited for transcribed meeting conversations combining semantically complex lexical relations with conversational cue phrases to build lexical chains in determining topic boundaries.","PeriodicalId":378427,"journal":{"name":"International Workshop on Natural Language Processing and Cognitive Science","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133297772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Experiments with Single-class Support Vector Data Descriptions as a Tool for Vocabulary Grounding","authors":"Aneesh Chauhan, L. Lopes","doi":"10.5220/0003028000700078","DOIUrl":"https://doi.org/10.5220/0003028000700078","url":null,"abstract":"This paper explores support vectors as a tool for vocabulary acquisition in robots. The intention is to investigate the language grounding process at the single-word stage. A social language grounding scenario is designed, where a robotic agent is taught the names of the objects by a human instructor. The agent grounds the names of these objects by associating them with their respective sensor-based category descriptions. A system for grounding vocabulary should be incremental, adaptive and support gradual evolution. A novel learning model based on single-class support vector data descriptions (SVDD), which conforms to these requirements, is presented. For robustness and flexibility, a kernel based implementation of support vectors was realized. For this purpose, a sigmoid kernel using histogram pyramid matching has been developed. The support vectors are trained based on an original approach using genetic algorithms. The model is tested over a series of semi-automated experiments and the results are reported.","PeriodicalId":378427,"journal":{"name":"International Workshop on Natural Language Processing and Cognitive Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132913390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}