Archive for the ‘Word Meaning’ Category

TSDW:… [Enterprise Disambiguation]

Monday, April 22nd, 2013

TSDW: Two-stage word sense disambiguation using Wikipedia by Chenliang Li, Aixin Sun, Anwitaman Datta. (Li, C., Sun, A. and Datta, A. (2013), TSDW: Two-stage word sense disambiguation using Wikipedia. J. Am. Soc. Inf. Sci.. doi: 10.1002/asi.22829)

Abstract:

The semantic knowledge of Wikipedia has proved to be useful for many tasks, for example, named entity disambiguation. Among these applications, the task of identifying the word sense based on Wikipedia is a crucial component because the output of this component is often used in subsequent tasks. In this article, we present a two-stage framework (called TSDW) for word sense disambiguation using knowledge latent in Wikipedia. The disambiguation of a given phrase is applied through a two-stage disambiguation process: (a) The first-stage disambiguation explores the contextual semantic information, where the noisy information is pruned for better effectiveness and efficiency; and (b) the second-stage disambiguation explores the disambiguated phrases of high confidence from the first stage to achieve better redisambiguation decisions for the phrases that are difficult to disambiguate in the first stage. Moreover, existing studies have addressed the disambiguation problem for English text only. Considering the popular usage of Wikipedia in different languages, we study the performance of TSDW and the existing state-of-the-art approaches over both English and Traditional Chinese articles. The experimental results show that TSDW generalizes well to different semantic relatedness measures and text in different languages. More important, TSDW significantly outperforms the state-of-the-art approaches with both better effectiveness and efficiency.

TSDW works because Wikipedia is a source of unambiguous phrases, that can also be used to disambiguate phrases that one first pass are not unambiguous.

But Wikipedia did not always exist and was built out of the collaboration of thousands of users over time.

Does that offer a clue as to building better search tools for enterprise data?

What if statistically improbable phrases are mined from new enterprise documents and links created to definitions for those phrases?

Thinking picking a current starting point avoids a “…boil the ocean…” scenario before benefits can be shown.

Current content is also more likely to be a search target.

Domain expertise and literacy required.

Expertise in logic or ontologies not.

Posted in Natural Language Processing, Wikipedia, Word Meaning | No Comments »

Kwong – … Word Sense Disambiguation

Tuesday, January 29th, 2013

New Perspectives on Computational and Cognitive Strategies for Word Sense Disambiguation
 by Oi Yee Kwong.

From the description:

Cognitive and Computational Strategies for Word Sense Disambiguation examines cognitive strategies by humans and computational strategies by machines, for WSD in parallel.

Focusing on a psychologically valid property of words and senses, author Oi Yee Kwong discusses their concreteness or abstractness and draws on psycholinguistic data to examine the extent to which existing lexical resources resemble the mental lexicon as far as the concreteness distinction is concerned. The text also investigates the contribution of different knowledge sources to WSD in relation to this very intrinsic nature of words and senses.

I wasn’t aware that the “mental lexicon” of words had been fully described.

Shows what you can learn from reading marketing summaries of research.

Posted in Disambiguation, Parallel Programming, Word Meaning | No Comments »

If you are in Kolkata/Pune, India…a request.

Tuesday, July 17th, 2012

No emails are given for the authors of: Identify Web-page Content meaning using Knowledge based System for Dual Meaning Words but their locations were listed as Kolkata and Pune, India. I would appreciate your pointing the authors to this blog as one source of information on topic maps.

The authors have re-invented a small part of topic maps to deal with synonymy using XSD syntax. Quite doable but I think they would be better served by either using topic maps or engaging in improving topic maps.

Reinvention is rarely a step forward.

Abstract:

Meaning of Web-page content plays a big role while produced a search result from a search engine. Most of the cases Web-page meaning stored in title or meta-tag area but those meanings do not always match with Web-page content. To overcome this situation we need to go through the Web-page content to identify the Web-page meaning. In such cases, where Webpage content holds dual meaning words that time it is really difficult to identify the meaning of the Web-page. In this paper, we are introducing a new design and development mechanism of identifying the Web-page content meaning which holds dual meaning words in their Web-page content.

Posted in Search Engines, Synonymy, Word Meaning, XML | 2 Comments »

From Words to Concepts and Back: Dictionaries for Linking Text, Entities and Ideas

Friday, May 18th, 2012

From Words to Concepts and Back: Dictionaries for Linking Text, Entities and Ideas by Valentin Spitkovsky and Peter Norvig (Google Research Team).

From the post:

Human language is both rich and ambiguous. When we hear or read words, we resolve meanings to mental representations, for example recognizing and linking names to the intended persons, locations or organizations. Bridging words and meaning — from turning search queries into relevant results to suggesting targeted keywords for advertisers — is also Google’s core competency, and important for many other tasks in information retrieval and natural language processing. We are happy to release a resource, spanning 7,560,141 concepts and 175,100,788 unique text strings, that we hope will help everyone working in these areas.

How do we represent concepts? Our approach piggybacks on the unique titles of entries from an encyclopedia, which are mostly proper and common noun phrases. We consider each individual Wikipedia article as representing a concept (an entity or an idea), identified by its URL. Text strings that refer to concepts were collected using the publicly available hypertext of anchors (the text you click on in a web link) that point to each Wikipedia page, thus drawing on the vast link structure of the web. For every English article we harvested the strings associated with its incoming hyperlinks from the rest of Wikipedia, the greater web, and also anchors of parallel, non-English Wikipedia pages. Our dictionaries are cross-lingual, and any concept deemed too fine can be broadened to a desired level of generality using Wikipedia’s groupings of articles into hierarchical categories.

(examples omitted)

The database that we are providing was designed for recall. It is large and noisy, incorporating 297,073,139 distinct string-concept pairs, aggregated over 3,152,091,432 individual links, many of them referencing non-existent articles. For technical details, see our paper (to be presented at LREC 2012) and the README file accompanying the data. (emphasis added)

Did you catch those numbers?

Now there is a truly remarkable resource.

What will you make out of it?

Posted in Concept Detection, Dictionary, Entities, Wikipedia, Word Meaning | No Comments »

Representing word meaning and order information in a composite holographic lexicon

Saturday, November 19th, 2011

Representing word meaning and order information in a composite holographic lexicon by Michael N. Jones , Douglas J. K. Mewhort.

Abstract:

The authors present a computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language. The model uses simple convolution and superposition mechanisms (cf. B. B. Murdock, 1982) to learn distributed holographic representations for words. The structure of the resulting lexicon can account for empirical data from classic experiments studying semantic typicality, categorization, priming, and semantic constraint in sentence completions. Furthermore, order information can be retrieved from the holographic representations, allowing the model to account for limited word transitions without the need for built-in transition rules. The model demonstrates that a broad range of psychological data can be accounted for directly from the structure of lexical representations learned in this way, without the need for complexity to be built into either the processing mechanisms or the representations. The holographic representations are an appropriate knowledge representation to be used by higher order models of language comprehension, relieving the complexity required at the higher level.

More reading along the lines of higher-dimensional representation techniques. Almost six (6) pages of references to run backwards and forwards so this is going to take a while.

Posted in High Dimensionality, Holographic Lexicon, Word Meaning | No Comments »