System and methods for inferring informational goals and preferred level of detail of results in response to questions posed to an automated information-retrieval or question-answering service
First Claim
1. An automated information retrieval system, comprising:
- A user interface for receiving a query;
an analyzer to process said query based upon at least one attribute related to a user, the analyzer infers one or more informational goals of the user from the query, the one or more informational goals of the user inferred by parsing the query into parts of speech and structural features and employing data obtained by parsing the query to access one or more decision trees in an inference model, the one or more decision trees store conditional probability distribution over the one or more informational goals given the parse data and a physical location of the user;
an answer generator that produces one or more responses to the query, the one or more responses are based, at least in part, on the one or more informational goals of the user inferred from the query, and the one or more responses vary in at least one of;
length, precision, and detail based on at least one of the inferred informational goals and the attribute; and
an inference clarifier that automatically clarifies an inference about at least one of the informational goals before producing the one or more responses by initiating a dialog with the user when the at least one of the inferred informational goals has a likelihood below a predefined probability threshold, wherein at least one of the user attribute or the inference model is refined upon an occurrence of the inference clarification.
2 Assignments
0 Petitions
Accused Products
Abstract
A system and method for inferring informational goals and preferred level of details in answers in response to questions posed to computer-based information retrieval or question-answering systems is provided. The system includes a query subsystem that can receive an input query and extrinsic data associated with the query and which can output an answer to the query, and/or rephrased queries or sample queries. The query subsystem accesses an inference model to infer a probability distribution over a user'"'"'s goals, age, and preferred level of detail of an answer. One application of the system includes determining a user'"'"'s likely informational goals and then accessing a knowledge data store to retrieve responsive information. The system includes a natural language processor that parses queries into observable linguistic features and embedded semantic components that can be employed to retrieve the conditional probabilities from the inference model. The inference model is built by employing supervised learning and statistical analysis on a set of queries suitable to be presented to a question-answering system. Such a set of queries can be manipulated to produce different inference models based on demographic and/or localized linguistic data.
277 Citations
43 Claims
-
1. An automated information retrieval system, comprising:
-
A user interface for receiving a query; an analyzer to process said query based upon at least one attribute related to a user, the analyzer infers one or more informational goals of the user from the query, the one or more informational goals of the user inferred by parsing the query into parts of speech and structural features and employing data obtained by parsing the query to access one or more decision trees in an inference model, the one or more decision trees store conditional probability distribution over the one or more informational goals given the parse data and a physical location of the user; an answer generator that produces one or more responses to the query, the one or more responses are based, at least in part, on the one or more informational goals of the user inferred from the query, and the one or more responses vary in at least one of;
length, precision, and detail based on at least one of the inferred informational goals and the attribute; andan inference clarifier that automatically clarifies an inference about at least one of the informational goals before producing the one or more responses by initiating a dialog with the user when the at least one of the inferred informational goals has a likelihood below a predefined probability threshold, wherein at least one of the user attribute or the inference model is refined upon an occurrence of the inference clarification. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
-
-
16. A method for automatically answering queries, comprising:
-
receiving a query from a user; employing language processing to parse the query into component parts of speech and logical forms; employing parse data produced by parsing the query to access a decision model, the decision model storing conditional probabilities associated with informational goals given the query within one or more decision trees; inferring one or more informational goals from the decision model and a physical location of the user; generating an answer related to the query and the one or more inferred informational goals; automatically clarifying at least one inference associated with the one or more informational goals prior to generating the answer, via a dialog with the user when the inference has a probability below a predefined threshold; and refining one or more of at least an attribute associated with the user or the decision model based upon occurrence of the automatic clarification of the at least one inference. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23)
-
-
24. A computer readable medium storing computer executable instructions operable to perform a method for answering questions, the method comprising:
-
inputting a question; employing language processing to parse the question into component parts of speech and logical forms; employing parse data produced by parsing the question to access a decision model, the decision model storing conditional probabilities associated with informational goals given the query within one or more decision trees; employing the decision model and a physical location of a user to infer one or more informational goals; clarifying one or more of the inferences about the informational goals when at least one of the inferences has a likelihood below a probability threshold by conducting a dialog with the user; determining that one or more of a user attribute or the decision model need refining based on occurrence of the dialog with the user; and subsequently producing an answer related to the question and the one or more inferred informational goals.
-
-
25. A system for answering a question posed to an automated question answerer comprising:
-
means for initializing a model representing the likelihood that a certain type of answer is desired, the model being generated from probability distributions over a set of goals given a question and a physical location of a user, and stored in one or more data repositories; means for parsing the question to identify parts of speech and structural features within the question, and employing the identified parts of speech and structural features to access one or more decision trees within the model to infer one or more of the goals; means for automatically adapting the model over time; means for determining one or more answers to the question based on likelihoods retrieved from accessing the model; and means for conducting a dialog with a user to clarify one or more inferences made regarding the set of goals prior to determining the one or more answers when the likelihoods of at least one of the inferences is below a predefined thresholds wherein an initiation of the clarifying dialog determines that at least one of a user attribute or the model should be refined. - View Dependent Claims (26)
-
-
27. A method to automatically clarify informational goals, comprising:
-
employing language processing to parse a query into observable linguistic features; performing a probabilistic analysis on observable linguistic data received from parsing the query by assessing at least a conditional probability distribution over a set of one or more informational goals given the parse data and a physical location of a user, the conditional probability distribution stored in one or more decision trees, the one or more decision trees make up an inference model; establishing a probability threshold related to an uncertainty in the analysis; invoking a dialog with a user when the probabilistic analysis of at least one of the informational goals yields a determination below the probability threshold; refining one or more of a user attribute or the decision model based on an occurrence of the dialog with the user; and searching for information based upon an answer selected in response to the dialog. - View Dependent Claims (28, 29, 30)
-
-
31. An automated information processing method, comprising:
-
processing inferences about a probability distribution over informational goals given a query, a physical location of a user, and parts of speech containing a focus of attention of the query in accordance with attributes of a user and most appropriate level of detail, wherein the probability distribution is stored in one or more data structures that are comprised in an inference model, wherein parsing the query into the parts of speech facilitates accessing the data structures in the inference model; employing the inferences in at least one of a post-filter process, a reformulation process, a process for dynamically crafting an answer to the query; and a process for driving a dialog in pursuit of refining the probability distribution, and a process for driving dialog in pursuit of a more appropriate query in order to satisfy the informational goals before crafting the answer when at least one of the inferences about the informational goals has a likelihood below a predefined probability threshold wherein one or more of at least an attribute associated with the user or the inference model is refined based upon occurrence of the dialog with the user. - View Dependent Claims (32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43)
-
Specification