| </term> of the <term> discourse </term> aggregate | into | <term> segments </term> , recognizing the <term> | #14337 Discourse processing requires recognizing how the utterances of the discourse aggregate into segments, recognizing the intentions expressed in the discourse and the relationships among intentions, and tracking the discourse through the operation of the mechanisms associated with attentional state. | |
| transformed by a <term> planning algorithm </term> | into | efficient <term> Prolog </term> , cf. <term> | #12920 The resulting logical expression is then transformed by a planning algorithminto efficient Prolog, cf. query optimisation in a relational database. | |
| sequences </term> . We incorporate this analysis | into | a <term> diagnostic tool </term> intended for | #7648 We incorporate this analysis into a diagnostic tool intended for developers of machine translation systems, and demonstrate how our application can be used by developers to explore patterns in machine translation output. | |
| integrating <term> automatic Q/A </term> applications | into | real-world environments . <term> FERRET </term> | #11650 This paper describes FERRET, an interactive question-answering (Q/A) system designed to address the challenges of integrating automatic Q/A applications into real-world environments. | |
| <term> general-purpose NLP components </term> | into | a <term> machine translation pipeline </term> | #11799 The LOGON MT demonstrator assembles independently valuable general-purpose NLP componentsinto a machine translation pipeline that capitalizes on output quality. | |
| </term> . The <term> board </term> plugs directly | into | the <term> VME bus </term> of the <term> SUN4 | #16952 The board plugs directly into the VME bus of the SUN4, which controls the system and contains the natural language system and application back end. | |
| of segments of the <term> discourse </term> | into | which the <term> utterances </term> naturally | #14165 The linguistic structure consists of segments of the discourseinto which the utterances naturally aggregate. | |
| of transforming a <term> disposition </term> | into | a <term> proposition </term> is referred to | #13591 The process of transforming a dispositioninto a proposition is referred to as explicitation or restoration. | |
| </term> which takes these <term> features </term> | into | account . We introduce a new <term> method | #8756 The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these featuresinto account. | |
| </term> and the main <term> dictionary </term> fits | into | the standard <term> 360K floppy </term> , whereas | #16831 The speed of the resulting program lies somewhere in the middle of the scale of existing spelling-checkers for English and the main dictionary fits into the standard 360K floppy, whereas the number of recognized word forms exceeds 6 million (for Czech). | |
| practically implemented and incorporated | into | the <term> English-Japanese MT system </term> | #19865 This model was practically implemented and incorporated into the English-Japanese MT system, and provided about 75% accuracy in the practical translation use. | |
| the results of which will be incorporated | into | a <term> natural language generation system | #15233 This research is part of a larger study of anaphoric expressions, the results of which will be incorporated into a natural language generation system. | |
| scruffy texts </term> has been incorporated | into | a working <term> computer program </term> called | #13114 This method of using expectations to aid the understanding of scruffy texts has been incorporated into a working computer program called NOMAD, which understands scruffy texts in the domain of Navy messages. | |
| </term> can be incrementally incorporated | into | the <term> dictionary </term> after the interaction | #18249 Detected unknown words can be incrementally incorporated into the dictionary after the interaction with the user. | |
| take <term> contextual information </term> | into | account . We evaluate our <term> paraphrase | #9748 We define a paraphrase probability that allows paraphrases extracted from a bilingual parallel corpus to be ranked using translation probabilities, and show how it can be refined to take contextual informationinto account. | |
| different amounts and types of information | into | its <term> lexicon </term> according to its | #15934 Although every natural language system needs a computational lexicon, each system puts different amounts and types of information into its lexicon according to its individual needs. | |
| clusters </term> , offering us a good insight | into | the potential and limitations of <term> semantically | #3961 A novel evaluation scheme is proposed which accounts for the effect of polysemy on the clusters, offering us a good insight into the potential and limitations of semantically classifying undisambiguated SCF data. | |
| Our work aims at providing useful insights | into | the the <term> computational complexity </term> | #9996 Our work aims at providing useful insights into the the computational complexity of those problems. | |
| create a <term> word-trie </term> , transform it | into | a <term> minimal DFA </term> , then identify | #3199 We create a word-trie, transform it into a minimal DFA, then identify hubs. | |
| basics of <term> SMT </term> : Theory will be put | into | practice . <term> STTK </term> , a <term> statistical | #8117 Theory will be put into practice. |
