25,168 research outputs found
Measuring Distributional Similarity in Context
The computation of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and paraphrases to word sense disambiguation and textual entailment. Vector-based models are typically directed at representing words in isolation and thus best suited for measuring similarity out of context. In his paper we propose a probabilistic framework for measuring similarity in context. Central to our approach is the intuition that word meaning is represented as a probability distribution over a set of latent senses and is modulated by context. Experimental results on lexical substitution and word similarity show that our algorithm outperforms previously proposed models.
Cross-domain sentiment classification using a sentiment sensitive thesaurus
Automatic classification of sentiment is important for numerous applications such as opinion mining, opinion summarization, contextual advertising, and market analysis. However, sentiment is expressed differently in different domains, and annotating corpora for every possible domain of interest is costly. Applying a sentiment classifier trained using labeled data for a particular domain to classify sentiment of user reviews on a different domain often results in poor performance. We propose a method to overcome this problem in cross-domain sentiment classification. First, we create a sentiment sensitive distributional thesaurus using labeled data for the source domains and unlabeled data for both source and target domains. Sentiment sensitivity is achieved in the thesaurus by incorporating document level sentiment labels in the context vectors used as the basis for measuring the distributional similarity between words. Next, we use the created thesaurus to expand feature vectors during train and test times in a binary classifier. The proposed method significantly outperforms numerous baselines and returns results that are comparable with previously proposed cross-domain sentiment classification methods. We conduct an extensive empirical analysis of the proposed method on single and multi-source domain adaptation, unsupervised and supervised domain adaptation, and numerous similarity measures for creating the sentiment sensitive thesaurus
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
Distributional semantics beyond words: Supervised learning of analogy and paraphrase
There have been several efforts to extend distributional semantics beyond
individual words, to measure the similarity of word pairs, phrases, and
sentences (briefly, tuples; ordered sets of words, contiguous or
noncontiguous). One way to extend beyond words is to compare two tuples using a
function that combines pairwise similarities between the component words in the
tuples. A strength of this approach is that it works with both relational
similarity (analogy) and compositional similarity (paraphrase). However, past
work required hand-coding the combination function for different tasks. The
main contribution of this paper is that combination functions are generated by
supervised learning. We achieve state-of-the-art results in measuring
relational similarity between word pairs (SAT analogies and SemEval~2012 Task
2) and measuring compositional similarity between noun-modifier phrases and
unigrams (multiple-choice paraphrase questions)
Distributional Measures of Semantic Distance: A Survey
The ability to mimic human notions of semantic distance has widespread
applications. Some measures rely only on raw text (distributional measures) and
some rely on knowledge sources such as WordNet. Although extensive studies have
been performed to compare WordNet-based measures with human judgment, the use
of distributional measures as proxies to estimate semantic distance has
received little attention. Even though they have traditionally performed poorly
when compared to WordNet-based measures, they lay claim to certain uniquely
attractive features, such as their applicability in resource-poor languages and
their ability to mimic both semantic similarity and semantic relatedness.
Therefore, this paper presents a detailed study of distributional measures.
Particular attention is paid to flesh out the strengths and limitations of both
WordNet-based and distributional measures, and how distributional measures of
distance can be brought more in line with human notions of semantic distance.
We conclude with a brief discussion of recent work on hybrid measures
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Application of Natural Language Processing and Evidential Analysis to Web-Based Intelligence Information Acquisition
The quality of decisions made in business and government relates directly to the quality of the information used to formulate the decision. This information may be retrieved from an organization's knowledge base (Intranet) or from the World Wide Web. Intelligence services Intranet held information can be efficiently manipulated by technologies based upon either semantics such as ontologies, or statistics such as meaning-based computing. These technologies require complex processing of large amount of textual information. However, they cannot currently be effectively applied to Web-based search due to various obstacles, such as lack of semantic tagging. A new approach proposed in this paper supports Web-based search for intelligence information utilizing evidence-based natural language processing (NLP). This approach combines traditional NLP methods for filtering of Web-search results, Grounded Theory to test the completeness of the evidence, and Evidential Analysis to test the quality of gathered information. The enriched information derived from the Web-search will be transferred to the intelligence services knowledge base for handling by an effective Intranet search system thus increasing substantially the information for intelligence analysis. The paper will show that the quality of retrieved information is significantly enhanced by the discovery of previously unknown facts derived from known facts
SensEmbed: Learning sense embeddings for word and relational similarity
Word embeddings have recently gained considerable popularity for modeling words in different Natural Language Processing (NLP) tasks including semantic similarity measurement. However, notwithstanding their success, word embeddings are by their very nature unable to capture polysemy, as different meanings of a word are conflated into a single representation. In addition, their learning process usually relies on massive corpora only, preventing them from taking advantage of structured knowledge. We address both issues by proposing a multifaceted approach that transforms word embeddings to the sense level and leverages knowledge from a large semantic network for effective semantic similarity measurement. We evaluate our approach on word similarity and relational similarity frameworks, reporting state-of-the-art performance on multiple datasets
What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets
In this paper, we claim that Vector Cosine, which is generally considered one
of the most efficient unsupervised measures for identifying word similarity in
Vector Space Models, can be outperformed by a completely unsupervised measure
that evaluates the extent of the intersection among the most associated
contexts of two target words, weighting such intersection according to the rank
of the shared contexts in the dependency ranked lists. This claim comes from
the hypothesis that similar words do not simply occur in similar contexts, but
they share a larger portion of their most relevant contexts compared to other
related words. To prove it, we describe and evaluate APSyn, a variant of
Average Precision that, independently of the adopted parameters, outperforms
the Vector Cosine and the co-occurrence on the ESL and TOEFL test sets. In the
best setting, APSyn reaches 0.73 accuracy on the ESL dataset and 0.70 accuracy
in the TOEFL dataset, beating therefore the non-English US college applicants
(whose average, as reported in the literature, is 64.50%) and several
state-of-the-art approaches.Comment: in LREC 201
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