The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated.We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus.

We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty ''trace'' categories of the treebank.

The Proposition Bank: An Annotated Corpus of Semantic Roles

We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Given an input sentence and a target word and frame, the system labels constituents with either abstract semantic roles, such as AGENT or PATIENT, or more domain-specific semantic roles, such as SPEAKER, MESSAGE, and TOPIC.The system is based on statistical classifiers trained on roughly 50,000 sentences that were hand-annotated with semantic roles by the FrameNet semantic labeling project. We then parsed each training sentence into a syntactic tree and extracted various lexical and syntactic features, including the phrase type of each constituent, its grammatical function, and its position in the sentence. These features were combined with knowledge of the predicate verb, noun, or adjective, as well as information such as the prior probabilities of various combinations of semantic roles. We used various lexical clustering algorithms to generalize across possible fillers of roles. Test sentences were parsed, were annotated with these features, and were then passed through the classifiers.Our system achieves 82% accuracy in identifying the semantic role of presegmented constituents. At the more difficult task of simultaneously segmenting constituents and identifying their semantic role, the system achieved 65% precision and 61% recall.Our study also allowed us to compare the usefulness of different features and feature combination methods in the semantic role labeling task. We also explore the integration of role labeling with statistical syntactic parsing and attempt to generalize to predicates unseen in the training data.

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Automatic labeling of semantic roles

1. Introduction to text mining 2. Core text mining operations 3. Text mining preprocessing techniques 4. Categorization 5. Clustering 6. Information extraction 7. Probabilistic models for Information extraction 8. Preprocessing applications using probabilistic and hybrid approaches 9. Presentation-layer considerations for browsing and query refinement 10. Visualization approaches 11. Link analysis 12. Text mining applications Appendix Bibliography.

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The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A method and system to search a variety of types of documents for material related to concepts expressed in a natural language text. The invention includes analyzing the natural language text to determine an underlying concept (420) and identifying one or more categories (430) of searchable material in a knowledge base that are related to the underlying concept. The invention includes intelligently providing documents from the knowledge base (480) in these categories, both automatically and with the assistance of a customer service agent.

Intelligent data search

A natural language information querying system includes an indexing facility configured to automatically generate indices of updated textual sources based on one or more predefined grammars and a database coupled to the indexing facility to store the indices for subsequent searching.

Information retrieval by natural language querying

Approaches to text processing that rely on parsing the text with a context-free grammar tend to be slow and error-prone because of the massive ambiguity of long sentences. In contrast, FASTUS employs a nondeterministic finite-state language model that produces a phrasal decomposition of a sentence into noun groups, verb groups and particles. Another finite-state machine recognizes domain-specific phrases based on combinations of the heads of the constituents found in the first pass. FASTUS has been evaluated on several blind tests that demonstrate that state-of-the-art performance on information-extraction tasks is obtainable with surprisingly little computational effort.

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FASTUS: A Finite-state Processor for Information Extraction from Real-world Text.

FASTUS is a system for extracting information from natural language text for entry into a database and for other applications. It works essentially as a cascaded, nondeterministic finite-state automaton. There are five stages in the operation of FASTUS. In Stage 1, names and other fixed form expressions are recognized. In Stage 2, basic noun groups, verb groups, and prepositions and some other particles are recognized. In Stage 3, certain complex noun groups and verb groups are constructed. Patterns for events of interest are identified in Stage 4 and corresponding ``event structures'' are built. In Stage 5, distinct event structures that describe the same event are identified and merged, and these are used in generating database entries. This decomposition of language processing enables the system to do exactly the right amount of domain-independent syntax, so that domain-dependent semantic and pragmatic processing can be applied to the right larger-scale structures. FASTUS is very efficient and effective, and has been used successfully in a number of applications.

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FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text

Gemini is a natural language understanding system developed for spoken language applications. This paper describes the details of the system, and includes relevant measurements of size, efficiency, and performance of each of its sub-components in detail.

Gemini: a natural language system for spoken-language understanding

: The authors have analyzed 607 sentences of spontaneous human-computer speech data containing repairs that were drawn from a total corpus of 10,718 sentences. In this paper, they present criteria and techniques for automatically detecting the presence of a repair, its location, and making the appropriate correction. The criteria involve integration of knowledge from several sources: pattern matching, syntactic and semantic analysis, and acoustics. In summary, disfluencies occur at high enough rates in human-computer dialog to merit consideration. In contrast to earlier approaches, the authors have made it their goal to detect and correct repairs automatically, without assuming an explicit edit signal. Without such an edit signal, however, repairs are easily confused both with false positives and with other repairs. Preliminary results show that pattern matching is effective at detecting repairs without excessive overgeneration. Their syntactic/semantic approaches are quite accurate at detecting repairs and correcting them. Acoustics is a third source of information that can be tapped to provide evidence about the existence of a repair. While none of these knowledge sources by itself is sufficient, they propose that by combining them, and possibly others, one can greatly enhance one's ability to detect and correct repairs. As a next step, they intend to explore additional aspects of the syntax and semantics of repairs, analyze further acoustic patterns, and pursue the question of how best to integrate information from these multiple knowledge sources.

John Bear

Papers

Information retrieval by natural language querying

FASTUS: A Finite-state Processor for Information Extraction from Real-world Text.

FASTUS: A Cascaded Finite-State Transducer for Extracting Information from Natural-Language Text

Gemini: a natural language system for spoken-language understanding

Detection and Correction of Repairs in Human-Computer Dialog