Figure 2 - uploaded by Yorick Wilks
Content may be subject to copyright.
Source publication
This paper describes an initial prototype demonstrator of a Companion, designed as a platform for novel approaches to the following: 1) The use of Information Extraction (IE) techniques to extract the content of incoming dialogue utterances after an Automatic Speech Recognition (ASR) phase, 2) The conversion of the input to Resource Descriptor Form...
Contexts in source publication
Context 1
... second phase of the decoding pipeline (de- picted in Figure 2) computes a value from the rescored forest: 1-or k-best derivations, feature expectations, or intersection with a target language reference (sentence or lattice). The last option generates an alignment forest, from which a word alignment or feature expectations can be extracted. ...
Context 2
... system architecture is shown in Figure 2. The system uses a standard interpretation pipeline, with domain-independent parsing and generation components supported by domain specific reason- ers for decision making. ...
Context 3
... these lists, very detailed filters can be applied: e.g., filtering the lexical units or synsets by parts of their orthographical forms. Figure 2 shows a list of lexical units to which a detailed filter has been applied: verbs have been chosen (see the chosen tab) whose orthographical forms start with an a-(see starts with check box and corre- sponding text field) and end with the suffix -ten (see ends with check box and corresponding text field). Only verbs that have a frame that contains NN are chosen (see Frame contains check box and corresponding text field). ...
Context 4
... lemmas postags -tagset: stts sem_lex_rels -source: GermaNet 26 Figure 2 shows a screenshot of the WebLicht web interface, developed and hosted in Tübin- gen. Area 1 shows a list of all WebLicht web services along with a subset of metadata (author, URL, description etc.). ...
Context 5
... a second analysis, we report the efficiency of reference implementations by varying the cor- pus size and number of threads. Figure 2 reports the total amount of time each algorithm needs for processing increasingly larger segments of a web- gathered corpus when using 8 threads. In all cases, only the top 100,000 words were counted as fea- tures. ...
Context 6
... limitations ac- count for the largest efficiency constraint, espe- cially as the corpus size and number of features grow. Several algorithms lack data points for larger corpora and show a sharp increase in run- ning time in Figure 2, reflecting the point at which the models no longer fit into 8GB of memory. ...
Context 7
... instance, Tree Edit Distance will manipulate nodes in a dependency tree representation, whereas To- ken Edit Distance and similarity algorithms will manipulate words. Figure 2 shows an example of <scheme> <insertion><cost>10</cost></insertion> <deletion><cost>10</cost></deletion> <substitution> <condition>(equals A B)</condition> <cost>0</cost> </substitution> <substitution> <condition>(not (equals A B))</condition> <cost>20</cost> </substitution> </scheme> In the distance-based framework adopted by EDITS, the interaction between algorithms and cost schemes plays a central role. Given a T-H pair, in fact, the distance score returned by an al- gorithm directly depends on the cost of the opera- tions applied to transform T into H (edit distance algorithms), or on the cost of mapping words in H with words in T (similarity algorithms). ...
Context 8
... Simple Cost Scheme -the one shown in Fig- ure 2, setting fixed costs for each edit opera- tion. ...
Context 9
... describe our system, which is divided in two main parts: (a) an offline Risk Miner that facili- tates the risk identification step of the risk manage- ment process, and an online (b) Risk Monitor that supports the risk monitoring step (cf. Figure 2). In addition, a Risk Mapper can aggregate and visu- alize the evidence in the form of a risk map. ...
Context 10
... overall system is divided into two core parts: (a) Risk Mining and (b) Risk Monitoring (cf. Fig- ure 2). For demonstration purposes, we add a (c) Risk Mapper, a visualization component. ...
Context 11
... this prototype demonstra- tion, we describe a speech-driven question-answer application. The system architecture is shown in Figure 2. The user of this application provides a spoken language query to a mobile device intending to find an answer to the question. ...
Context 12
... it does not suggest all exist- ing words, but only those words that are grammatically correct in the context. Figure 2 shows an example of the parser at work. The author has started a sentence as la femme qui remplit le formulaire est co ("the woman who fills the form is co"), and a menu shows a list of words beginning with co that are given in the French grammar and possible in the context at hand; all these words are adjectives in the feminine form. ...
Context 13
... author has started a sentence as la femme qui remplit le formulaire est co ("the woman who fills the form is co"), and a menu shows a list of words beginning with co that are given in the French grammar and possible in the context at hand; all these words are adjectives in the feminine form. Notice that the very example shown in Figure 2 is one that is diffi- cult for n-gram-based statistical translators: the adjec- tive is so far from the subject with which it agrees that it cannot easily be related to it. ...
Context 14
... translation tool snapshot in Figure 2 is from an actual web-based prototype. It shows a slot in an HTML page, built by using JavaScript via the Google Web Toolkit (Bringert & al. 2009). ...
Context 15
... this section we will review the components of the SC architecture. As can be seen from Figure 2, the architecture contains three abstract level components -Connectors, Input Handlers and Application Services -together with the Dialogue Manager and the Natural Language Understander (NLU). ...
Citations
... Berger (2014) summarized the following preprocessing tasks of dialog systems: sentence detection, co-resolution, tokenization, lemmatization, POS-tagging, dependency parsing, named entity recognition, semantic role labeling. We found that the dialog systems mostly deployed the following natural language preprocessing tasks: Tokenization (Veselov, 2010;Wilks et al., 2010;Eugene, 2014;Bogatu et al., 2015;Amilon, 2015), POS-Tagging ( Lasguido et al., 2013;Dingli et al., 2013;Higashinaka et al., 2014;Ravichandran et al., 2015), sentence detection or chunking ( Latorre-Navarro et al., 2015), Named Entity Recognition ( Wilks et al., 2010;Lasguido et al., 2013). Natural Language Understanding. ...
... Berger (2014) summarized the following preprocessing tasks of dialog systems: sentence detection, co-resolution, tokenization, lemmatization, POS-tagging, dependency parsing, named entity recognition, semantic role labeling. We found that the dialog systems mostly deployed the following natural language preprocessing tasks: Tokenization (Veselov, 2010;Wilks et al., 2010;Eugene, 2014;Bogatu et al., 2015;Amilon, 2015), POS-Tagging ( Lasguido et al., 2013;Dingli et al., 2013;Higashinaka et al., 2014;Ravichandran et al., 2015), sentence detection or chunking ( Latorre-Navarro et al., 2015), Named Entity Recognition ( Wilks et al., 2010;Lasguido et al., 2013). Natural Language Understanding. ...
... The quantitative method makes use of dialog protocols generated by conversations between the user and the system. Examples of conversational systems that have been evaluated using this method include RAILTEL ( Bennacef et al., 1996), Max ( Kopp et al., 2005), HumoristBot ( Augello et al., 2008), Senior Companion ( Wilks et al. 2010;, SimStudent ( MacLellan et al., 2014), Betty's Brain ( Leelawong et al., 2008;Biswas et al., 2005), CALMsystem ( Kerly et al., 2007), Discussion-Bot ( Feng et al., 2007), the dialogue system of Planells et al. (2013), or Albert ( Latorre-Navarro et al., 2015). The third evaluation method deploys pre-and post-tests. ...
During the last 50 years, since the development of ELIZA by Weizenbaum, technologies for developing conversational systems have made a great stride. The number of conversational systems is increasing. Conversational systems emerge almost in every digital device in many application areas. In this paper, we present the review of the development of conversational systems regarding technologies and their special features including language tricks.
... The quantitative method makes use of dialog protocols generated by conversations between the user and the system. Examples of conversational systems that have been evaluated using this method include RAILTEL ( Bennacef et al. 1996), Max ( Kopp et al. 2005), HumoristBot ( Augello et al. 2008), Senior Companion ( Wilks et al. 2010Wilks et al. , 2008), SimStudent ( MacLellan et al. 2014), Betty's Brain ( Leelawong et al. 2008;Biswas et al. 2005), CALMsystem (Kerly et al. 2007), Discussion-Bot ( Feng et al. 2007), the dialogue system of Planells et al. (2013), or Albert (Latorre- ). ...
These proceedings consist of 19 papers, which have been peer-reviewed by international program committee and selected for the 5th International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2017), which was held on June 30–July 1, 2017 in Berlin, Germany. The respective chapters discuss both theoretical and practical issues in connection with computational methods and optimization methods for knowledge engineering. The broad range of application areas discussed includes network computing, simulation, intelligent and adaptive e-learning, information retrieval, sentiment analysis, autonomous underwater vehicles, social media analysis, natural language processing, biomimetics in organizations, and cash management.
In addition to pure content, the book offers many inspiring ideas and suggests new research directions, making it a valuable resource for graduate students, Ph.D. students, and researchers in Computer Science and Applied Mathematics alike.
In recent years, considerable amount of research has been dedicated to the integration of artificial cognitive functionalities into informatics. With the immense growth in volume of cognitive content handled by both artificial and natural cognitive systems, the scientific treatment of new and efficient communication forms between such cognitive systems is inevitable. In this paper, we provide the first definition of cognitive infocommunications, a multidisciplinary field which aims to expand the information space between communicating cognitive systems (artificial or otherwise). Following this definition, we specify the modes and types of communication which make up cognitive infocommunications. Through a number of examples, we describe what is expected from this new discipline in further detail.
