Abstract: A language processing system identifies first command input sentences that do not successfully parse by any parsing rule in a set of parsing rules. Each of the parsing rules is associated with an action, and a user device performs the action associated with a parsing rule in response to an input sentence being successfully parsed by the parsing rule. For each of these identified first sentences, the system determines whether the first input sentence has an underserving signal that is indicative of one or more actions being underserved. If the first sentence has the underserving signal, then the first sentence is selected as a candidate input sentence. Each candidate input sentence is provided to an action analysis processes that determines whether a candidate input sentence is to be associated with one action, and upon a positive determination generates a parsing rule for the candidate input sentence.

Abstract: A language processing system identifies first command input sentences that do not successfully parse by any parsing rule in a set of parsing rules. Each of the parsing rules is associated with an action, and a user device performs the action associated with a parsing rule in response to an input sentence being successfully parsed by the parsing rule. For each of these identified first sentences, the system determines whether the first input sentence has an underserving signal that is indicative of one or more actions being underserved. If the first sentence has the underserving signal, then the first sentence is selected as a candidate input sentence. Each candidate input sentence is provided to an action analysis processes that determines whether a candidate input sentence is to be associated with one action, and upon a positive determination generates a parsing rule for the candidate input sentence.

Abstract: Systems, methods and apparatus for learning parsing rules and argument identification from crowdsourcing of proposed command inputs. Crowdsourcing techniques to generate rules for parsing input sentences. A parse is used to determine whether the input sentence invokes a specific action, and if so, what arguments are to be passed to the invocation of the action.

Abstract: Systems, methods and apparatus for learning parsing rules and argument identification from crowdsourcing of proposed command inputs are disclosed. Crowdsourcing techniques are used to generate rules for parsing input sentences. A parse is used to determine whether the input sentence invokes a specific action, and if so, what arguments are to be passed to the invocation of the action.

Abstract: A language processing system identifies sequential command inputs in user session data stored in logs. Each sequence command input is a first command input followed by a second command input. The system determines user actions in response to each command input. For the second command input, an action was taken at the user device in response to the command input, and there is no parsing rule associated with the action that parses to the first command input. If there is a sufficient co-occurrence of the first and second command inputs and the resulting action in the logs, then a parsing rule for the action may be augmented with a rule for the first command input.

Abstract: A language processing system uses a lattice parser that semantically parses a command input represented by a lattice. The parser receives a hypotheses space of outputs as encoded in a lattice. Annotations of the input are projected back into the lattice and then lattice parsing is performed to rectify with the annotations. Parsing rules are applied to path fragments in the lattice. The rules that successfully parse from the start node to the end node of the lattice are used to determine whether the command input sentence invokes a specific action, and if so, what arguments are to be passed to the invocation of the action.

Abstract: A language processing system identifies, from log data, command inputs that parsed to a parsing rule associated with an action. If the command input has a signal indicative of user satisfaction, where the signal is derived from data that is not generated from performance of the action (e.g., user interactions with data provided in response to the performance of another, different action; resources identified in response to the performance of another, different action having a high quality score; etc.), then exception data is generated for the parsing rule. The exception data specifies the particular instance of the sentence parsed by the parsing rule, and precludes invocation of the action associated with the rule.

Abstract: A system determines search hypotheses for a search query, each search hypothesis defining a search type and respectively corresponding to a resource corpus of a type that matches the search type; for each search hypothesis, generate a hypothesis search query based on the search query and the search type and submits the hypothesis search query to a search service to determine a search hypothesis score, and for each search hypothesis having a search hypothesis score meeting a search hypothesis threshold, providing search results for the search operation performed for the hypothesis search query determined for the search hypothesis; and for each search hypothesis not having a search hypothesis score meeting a search hypothesis threshold, not providing search results for the search operation performed for the hypothesis search query determined for the search hypothesis.

Abstract: A language processing system uses annotation services that are external to the language processing system to identify n-grams that identify entities in an input sentence. The n-grams are annotated by the annotation services. The annotations are used to determine which n-grams, if any, correspond to instances of an entity type (e.g., values for a variable or terminals for a non-terminal). After determining which n-grams correspond to entity types, parse initializations are generated for parsing rules and parses for each rule are attempted. The rules that successfully parse are used to determine whether the input sentence invokes a specific action, and if so, what arguments are to be passed to the invocation of the action.

Abstract: A language processing system identifies first command input sentences that do not successfully parse by any parsing rule in a set of parsing rules. Each of the parsing rules is associated with an action, and a user device performs the action associated with a parsing rule in response to an input sentence being successfully parsed by the parsing rule. For each of these identified first sentences, the system determines whether the first input sentence has an underserving signal that is indicative of one or more actions being underserved. If the first sentence has the underserving signal, then the first sentence is selected as a candidate input sentence. Each candidate input sentence is provided to an action analysis processes that determines whether a candidate input sentence is to be associated with one action, and upon a positive determination generates a parsing rule for the candidate input sentence.

Abstract: Systems, methods and apparatus for learning parsing rules and argument identification from crowdsourcing of proposed command inputs are disclosed. Crowdsourcing techniques are used to generate rules for parsing input sentences. A parse is used to determine whether the input sentence invokes a specific action, and if so, what arguments are to be passed to the invocation of the action.

Abstract: A language processing system identifies, from log data, command inputs that parsed to a parsing rule associated with an action. If the command input has a signal indicative of user satisfaction, where the signal is derived from data that is not generated from performance of the action (e.g., user interactions with data provided in response to the performance of another, different action; resources identified in response to the performance of another, different action having a high quality score; etc.), then exception data is generated for the parsing rule. The exception data specifies the particular instance of the sentence parsed by the parsing rule, and precludes invocation of the action associated with the rule.

Abstract: Grammatical parsing is utilized to parse structured layouts that are modeled as grammars. This type of parsing provides an optimal parse tree for the structured layout based on a grammatical cost function associated with a global search. Machine learning techniques facilitate in discriminatively selecting features and setting parameters in the grammatical parsing process. In one instance, labeled examples are parsed and a chart is generated. The chart is then converted into a subsequent set of labeled learning examples. Classifiers are then trained utilizing conventional machine learning and the subsequent example set. The classifiers are then employed to facilitate scoring of succedent sub-parses. A global reference grammar can also be established to facilitate in completing varying tasks without requiring additional grammar learning, substantially increasing the efficiency of the structured layout analysis techniques.

Abstract: A system, method and program product for interactively scheduling and negotiating meetings wherein an active agent program accepts meeting criteria from a meeting requester and interacts with invitees to resolve availability according to the meeting criteria. The agent transmits the negotiated meeting schedule to invitees and optionally requires confirmation from invitees.