Ken Barker

Using Noun Phrase Heads to Extract Document Keyphrases

Automatically extracting keyphrases from documents is a task with many applications in information retrieval and natural language processing. Document retrieval can be biased towards documents containing relevant keyphrases; documents can be classified or categorized based on their keyphrases; automatic text summarization may extract sentences with high keyphrase scores. This paper describes a simple system for choosing noun phrases from a document as keyphrases. A noun phrase is chosen based on its length, its frequency and the frequency of its head noun. Noun phrases are extracted from a text using a base noun phrase skimmer and an off-the-shelf online dictionary. Experiments involving human judges reveal several interesting results: the simple noun phrase-based system performs roughly as well as a state-of-the-art, corpus-trained keyphrase extractor; ratings for individual keyphrases do not necessarily correlate with ratings for sets of keyphrases for a document; agreement among unbiased judges on the keyphrase rating task is poor.

Semi-Automatic Recognition of Noun Modifier Relationships

Semantic relationships among words and phrases are often marked by explicit syntactic or lexical clues that help recognize such relationships in texts. Within complex nominals, however, few overt clues are available. Systems that analyze such nominals must compensate for the lack of surface clues with other information. One way is to load the system with lexical semantics for nouns or adjectives. This merely shifts the problem elsewhere: how do we define the lexical semantics and build large semantic lexicons? Another way is to find constructions similar to a given complex nominal, for which the relationships are already known. This is the way we chose, but it too has drawbacks. Similarity is not easily assessed, similar analyzed constructions may not exist, and if they do exist, their analysis may not be appropriate for the current nominal.We present a semi-automatic system that identifies semantic relationships in noun phrases without using precoded noun or adjective semantics. Instead, partial matching on previously analyzed noun phrases leads to a tentative interpretation of a new input. Processing can start without prior analyses, but the early stage requires user interaction. As more noun phrases are analyzed, the system learns to find better interpretations and reduces its reliance on the user. In experiments on English technical texts the system correctly identified 60--70% of relationships automatically.

INTERACTIVE SEMANTIC ANALYSIS OF TECHNICAL TEXTS

Sentence syntax is the basis for organizing semantic relations in TANKA, a project that aims to acquire knowledge from technical text. Other hallmarks include an absence of precoded domain‐specific knowledge; significant use of public‐domain generic linguistic information sources; involvement of the user as a judge and source of expertise; and learning from the meaning representations produced during processing. These elements shape the realization of the TANKA project: implementing a trainable text processing system to propose correct semantic interpretations to the user. A three‐level model of sentence semantics, including a comprehensive Case system, provides the framework for TANKAs representations. Text is first processed by the DIPETT parser, which can handle a wide variety of unedited sentences. The semantic analysis module HAIKU then semi‐automatically extracts semantic patterns from the parse trees and composes them into domain knowledge representations. HAIKUs dictionaries and main algorithm are described with the aid of examples and traces of user interaction. Encouraging experimental results are described and evaluated.

Systematic construction of a versatile case system

Case systems abound in natural language processing. Almost any attempt to recognize and uniformly represent relationships within a clause – a unit at the centre of any linguistic system that goes beyond word level statistics – must be based on semantic roles drawn from a small, closed set. The set of roles describing relationships between a verb and its arguments within a clause is a case system. What is required of such a case system? How does a natural language practitioner build a system that is complete and detailed yet practical and natural? This paper chronicles the construction of a case system from its origin in English marker words to its successful application in the analysis of English text.

A Trainable Bracketer for Noun Modifiers

Noun phrases carry much of the information in a text. Systems that attempt to acquire knowledge from text must first decompose complex noun phrases to get access to that information. In the case of noun compounds, this decomposition usually means bracketing the modifiers into nested modifier-head pairs. It is then possible to determine the semantic relationships among individual components of the noun phrase.

Test-Driving TANKA: Evaluating a Semi-automatic System of Text Analysis for Knowledge Acquisition

The evaluation of a large implemented natural language processing system involves more than its application to a common performance task. Such tasks have been used in the message understanding conferences (MUCs), text retrieval conferences (TRECs) as well as in speech technology and machine translation workshops. It is useful to compare the performance of different systems in a predefined application, but a detailed evaluation must take into account the specificity of the system.

What is technical text

Abstract Beyond labeling it easier to process than other types, few researchers who use technical text in their work try to define what it is. This paper describes a study that investigates the character of texts typically considered technical. We identify 42 features of a text considered likely to correlate with its degree of technicality. These include both objectively verifiable measures like marked presence of interrogative or imperative sentences which are akin to the criteria used by Biber in Variation Across Speech and Writing , and subjective measures such as presence of hierarchical organization . All are less ambiguous than technicality, so our inventory may be suited to use in a procedure that classifies text as technical or non-technical. An inventory organizing and describing these lexical, syntactic, semantic and discourse features was used to rate nine varied sample texts. Analysis of 22 ratings of each text indicated that 31 features in the inventory were meaningful predictors of text technicality when considered independently. The inventory has been revised and a formula to compute technicality has been developed in the light of these findings.