Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-ofthe-art WSD system that incorporates several standard WSD features.

/pdf/embeddings-for-word-sense-disambiguation-an-evaluation-study-47lzrtz3wb.pdf

Embeddings for word sense disambiguation: An evaluation study

Systems and methods for word sense disambiguation, including discerning one or more senses or occurrences, distinguishing between senses or occurrences, and determining a meaning for a sense or occurrence of a subject term. In a collection of documents containing terms and a reference collection containing at least one meaning associated with a term, the method includes forming a vector space representation of terms and documents. In some embodiments, the vector space is a latent semantic index vector space. In some embodiments, occurrences are clustered to discern or distinguish a sense of a term. In preferred embodiments, meaning of a sense or occurrence is assigned based on either correlation with an external reference source, or proximity to a reference source that has been indexed into the space.

Word sense disambiguation

A natural language interface system for an Internet search engine shows substantial increases in the precision of query results and the percentage of queries answered correctly. The system expands queries based on a word-sense-disambiguation method and postprocesses retrieved documents to extract only the parts relevant to a query.

Using WordNet and lexical operators to improve Internet searches

/pdf/measures-and-applications-of-lexical-distributional-8vwqvcz3hj.pdf

Measures and Applications of Lexical Distributional Similarity

The present invention provides a method of processing at least one natural language text using a graph. The method includes determining a plurality of text units based upon the natural language text, associating the plurality of text units with a plurality of graph nodes, and determining at least one connecting relation between at least two of the plurality of text units. The method also includes associating the at least one connecting relation with at least one graph edge connecting at least two of the plurality of graph nodes and determining a plurality of rankings associated with the plurality of graph nodes based upon the at least one graph edge. The method can also include a graphical visualization of at least one important text unit in a natural language text or collection of texts. Methods for word sense disambiguation, keyword extraction, and sentence extraction are also provided.

/pdf/graph-based-ranking-algorithms-for-text-processing-3dnmirdkq2.pdf

Graph-based ranking algorithms for text processing

This paper deals with two important ambiguities of natural language: prepositional phrase attachment and word sense ambiguity We propose a new supervised learning method for PPattachment based on a semantically tagged corpus Because any sufficiently big sense-tagged corpus does not exist, we also propose a new unsupervised context based word sense disambiguation algorithm which amends the training corpus for the PP attachment by word sense tags We present the results of our approach and evaluate the achieved PP attachment accuracy in comparison with other methods

Corpus Based PP Attachment Ambiguity Resolution with a Semantic Dictionary

This paper presents a new general supervised word sense disambiguation method based on a relatively small syntactically parsed and semantically tagged training corpus.The method exploits a full sentential context and all the explicit semantic relations in a sentence to identify the senses of all of that sentence's content words. It solves the sparse data problem of a small training corpus by substituting the words by their semantic classes.In spite of a very small training corpus, we report an overall accuracy of 80.3% (85.7, 63.9, 83.6 and 86.5%, for nouns, verbs, adjectives and adverbs, respectively), which exceeds the accuracy of a statistical sense-frequency based semantic tagging, the only really applicable general disambiguating technique. Because the method uses the sentential syntactic structure it is particularly suitable for integration with a probabilistic syntactic analyser.

/pdf/general-word-sense-disambiguation-method-based-on-a-full-1jsjav0192.pdf

General Word Sense Disambiguation Method Based on a Full Sentential Context

This paper deals with two important ambiguities of natural language: prepositional phrase attachment and word sense ambiguity. We propose a new supervised learning method for PP-attachment based on a semantically tagged corpus. Because any sufficiently big sense-tagged corpus does not exist, we also propose a new unsupervised context based word sense disambiguation algorithm which amends the training corpus for the PP attachment by word sense tags. We present the results of our approach, which not only surpasses any existing method but also draws near human performance.

/pdf/pp-attachment-ambiguity-resolution-through-supervised-58ueokr9av.pdf

PP Attachment Ambiguity Resolution through Supervised Learning

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jiri Stetina

Author Tools

Chat about Author

Papers

Corpus Based PP Attachment Ambiguity Resolution with a Semantic Dictionary

General Word Sense Disambiguation Method Based on a Full Sentential Context

PP Attachment Ambiguity Resolution through Supervised Learning