Abstract: A quality-prediction engine predicts a trust level associated with translational accuracy of a machine-generated translation. Training a quality-prediction may include translating a document in a source language to a target language by executing a machine-translation engine stored in memory to obtain a machine-generated translation. The training may further include comparing the machine-generated translation with a human-generated translation of the document. The human-generated translation is in the target language. Additionally, the training may include generating a mapping between features of the machine-generated translation and features of the human-generated translation based on the comparison.

Abstract: A translation training device which extracts from two nonparallel Corpora a set of parallel sentences. The system finds parameters between different sentences or phrases, in order to find parallel sentences. The parallel sentences are then used for training a data-driven machine translation system. The process can be applied repetitively until sufficient data is collected or until the performance of the translation system stops improving.

Abstract: A machine translation system may use non-parallel monolingual corpora to generate a translation lexicon. The system may identify identically spelled words in the two corpora, and use them as a seed lexicon. The system may use various clues, e.g., context and frequency, to identify and score other possible translation pairs, using the seed lexicon as a basis. An alternative system may use a small bilingual lexicon in addition to non-parallel corpora to learn translations of unknown words and to generate a parallel corpus.

Abstract: A method and system for determining text coherence in an essay is disclosed. A method of evaluating the coherence of an essay includes receiving an essay having one or more discourse elements and text segments. The one or more discourse elements are annotated either manually or automatically. A text segment vector is generated for each text segment in a discourse element using sparse random indexing vectors. The method or system then identifies one or more essay dimensions and measures the semantic similarity of each text segment based on the essay dimensions. Finally, a coherence level is assigned to the essay based on the measured semantic similarities.

Abstract: A special notation that extends the notion of IDL by weighted operators. The Weighted IDL or WIDL can be intersected with a language model, for example an n-gram language model or a syntax-based language model. The intersection is carried out by converting the IDL to a graph, and unfolding the graph in a way which maximizes its compactness.

Abstract: A quality-prediction engine predicts a trust level associated with translational accuracy of a machine-generated translation. Training a quality-prediction may include translating a document in a source language to a target language by executing a machine-translation engine stored in memory to obtain a machine-generated translation. The training may further include comparing the machine-generated translation with a human-generated translation of the document. The human-generated translation is in the target language. Additionally, the training may include generating a mapping between features of the machine-generated translation and features of the human-generated translation based on the comparison. The mapping may allow determination of trust levels associated with translational accuracy of future machine-generated translations that lack corresponding human-generated translations.

Abstract: Customers having a translation project to select a translation method from a variety of options, ranging from a completely human translation to a completely automated translation. For human translations, translation job information may be communicated through one or more network service modules which execute within a network service application, such as a web-based networking application. A network service module may register a user having an account with the network service application as a translator and communicate translation jobs to the user. One or more users who express interest in performing the translation are selected to perform a translation job, each job comprising at least a portion of the translation project. After a user provides a translation for the translation job, the translation is analyzed to generate a trust level prediction for the translation. A user translation profile may be updated after each translation to reflect the user's performance.

Abstract: A document containing text in a source language may be translated into a target language based on content associated with that document, in conjunction with the present technology. An indication to perform an optimal translation of a document into a target language may be received via a user interface. The document may then be accessed by a computing device. The optimal translation is executed by a preferred translation engine of a plurality of available translation engines. The preferred translation engine is the most likely to produce the most accurate translation of the document among the plurality of available translation engines. Additionally, the preferred translation engine may be identified based on content associated with the document. The document is translated into the target language using the preferred translation engine to obtain a translated document, which may then be outputted by a computing device.

Abstract: To automatically annotate an essay, a sentence of the essay is identified and a feature associated with the sentence is determined. In addition, a probability of the sentence being a discourse element is determined by mapping the feature to a model. The model having been generated by a machine learning application based on at least one annotated essay. Furthermore, the essay is annotated based on the probability.

Abstract: A training system for text to text application. The training system finds groups of documents, and identifies automatically similar documents in the groups which are similar. The automatically identified documents can then be used for training of the text to text application. The comparison uses reduced size versions of the documents in order to minimize the amount of processing.

Abstract: An essay is analyzed automatically by accepting the essay and determining whether each of a predetermined set of features is present or absent in each sentence of the essay. For each sentence in the essay a probability that the sentence is a member of a certain discourse element category is calculated. The probability is based on the determinations of whether each feature in the set of features is present or absent. Furthermore, based on the calculated probabilities, a sentence is chosen as the choice for the discourse element category.

Abstract: To automatically annotate an essay, a sentence of the essay is identified and a feature associated with the sentence is determined. In addition, a probability of the sentence being a discourse element is determined by mapping the feature to a model. The model having been generated by a machine learning application based on at least one annotated essay. Furthermore, the essay is annotated based on the probability.

Abstract: A method and system for determining text coherence in an essay is disclosed. A method of evaluating the coherence of an essay includes receiving an essay having one or more discourse elements and text segments. The one or more discourse elements are annotated either manually or automatically. A text segment vector is generated for each text segment in a discourse element using sparse random indexing vectors. The method or system then identifies one or more essay dimensions and measures the semantic similarity of each text segment based on the essay dimensions. Finally, a coherence level is assigned to the essay based on the measured semantic similarities.

Abstract: The present invention relates to compounds of formula (I), or salts or solvates thereof, their use in the manufacture of medicaments for treating neurological and neuropsychiatric disorders, in particular psychoses, dementia or attention deficit disorder. The invention further comprises processes to make these compounds and pharmaceutical formulations thereof.

Abstract: The present invention relates to compounds of formula (I), or to salts or solvates thereof, their use in the manufacture of medicaments for treating neurological and neuropsychiatric disorders, in particular psychoses, dementia or attention deficit disorder. The invention further comprises processes to make these compounds and pharmaceutical formulations thereof.

Abstract: A patient's image data and associated metadata describing the image data and characteristics of the patient are received and stored in a memory area. Information that describes one or more imaging analysis services and associated service criteria is accessed and compared to the metadata. Based on matches between the image metadata and service criteria, services are selected and executed, producing an output. Iterative rounds of service matching based on the metadata and the output of previous services and service execution proceed. The output of the services is associated with the image data and metadata, stored in the memory area, and made available to the user. A data repository, including the original images, metadata, and service output is accessible via the services for use in comparative analyses. Such services can utilize the repository to dynamically update indices and algorithms used in the comparative analyses.

Abstract: Compounds of formula (I), and salts and solvates thereof are provided: Processes for preparation, pharmaceutical compositions, and uses thereof as a medicament, for example in the treatment of a disease or condition mediated by a reduction or imbalance in glutamate receptor function, such as schizophrenia or cognition impairment, are also disclosed.

Abstract: An essay is analyzed automatically by accepting the essay and determining whether each of a predetermined set of features is present or absent in each sentence of the essay. For each sentence in the essay a probability that the sentence is a member of a certain discourse element category is calculated. The probability is based on the determinations of whether each feature in the set of features is present or absent. Furthermore, based on the calculated probabilities, a sentence is chosen as the choice for the discourse element category.

Abstract: A method and system for determining text coherence in an essay is disclosed. A method of evaluating the coherence of an essay includes receiving an essay having one or more discourse elements and text segments. The one or more discourse elements are annotated either manually or automatically. A text segment vector is generated for each text segment in a discourse element using sparse random indexing vectors. The method or system then identifies one or more essay dimensions and measures the semantic similarity of each text segment based on the essay dimensions. Finally, a coherence level is assigned to the essay based on the measured semantic similarities.

Abstract: A test socket comprises a test socket body with a central opening configured to receive a device under test (DUT) including at least one arm opening in the test socket body; and at least one rotating arm disposed in the arm opening.