Reviewing Article (synonym)

Building A Lexical Knowledge-Base of Near-Synonym Differences
By Diana Inkpen

A thesis submited in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Computer Sience University of Toronto


Background
Current natural language generation or machine translation systems cannot distinguish among Near-Synonym-words that share the same core meaning but vary in their lexical nuances. This is due to a lack of knowledge about differences between Near-Synonyms in existing computational lexical resources.
The goal of this thesis is to automatically acquire a lexical knowledge-base of Near-Synonym differences from multiple sources, and to show how it can be used in a practical natural language processing system.

Finding or Contributions
In this thesis she show that it is possible to automatically acquire knowledge about the differences between Near-Synonyms. Denotational, attitudinal, and stylistic differences are extracted from a special dictionary of synonyms and from machine-readable dictionaries. Knowledge about the collocational behaviour of the Near-Synonyms is acquired from free text. The resulting lexical knowledge-base of Near-Synonym differences is useful in many natural language processing aplications. She show how a natural language generation system can use it in order to choose the best Near-Synonym that matches a set of input preferences. If the preferences are lexical nuances extracted by the analysis component of a machine translation system, the translation quality would be higher: it would preserve not only the meaning of the text, but also its nuances of meaning.

Method
She designed a method to automatically acquire knowledge from dictionaries of Near-Synonym discrimination written for human readers. An unsupervised decision list algorithm learns patterns and words for classes of distinctions. The patterns are automatically, followed by a manual validation step. The extraction of distinctions between Near-Synonyms is entirely automatic.
She enriched the initial LKB of NS with information extracted from other sources. First,information about the senses of Near-Synonym was added (WordNet senses). Second, knowledge about the collocational behaviour of Near-Synonym was acquired from free text. Third, knowledge about distinctions between Near-Synonym was acquired from machine  readable dictionaries (the General Inquirer and the Macquarie Dictionary)

Strengtness and Weakness 
This study used a modified version of the SynLex dictionary to estimate the level of synonymy between word pairs. The maximum level of synonym did not guarantee that substitutions would be correct, rather the precision at this level was still relatively poor.

Conclussion 
Lexical simplification is a topic that requires more attention in research on automatic text simplification. A common assumption is that frequency alone is a sufficient criterion for estimating the difficulty of words. Although this is naturally not always the case word frequencies are usually a good estimate of word difficulty. Based on this assumption it is easy to compare the difficulty of two words by simply reffering to word frequency information. Many researchers apply this reasoning to lexical simplification but do not give appripriate attention to many of the related questions.

Article Review

Negative Judgments In About Semantic Memory¹
Jhon R. ANDERSON and LYNNE M. REDER
University of Michigan

This result is concered with determining how subjects falsify statements like A collie is a cat. A multiple regersion analysis was performed which used 22 variables to try to predict the negative judgment times. The predictive variables are time to generate the superiordinate of the instance (for example, dog), time to falsify that the superordinate is the predi ate  (for e ample, A dog is a cat), and time to encode theinstance. This finding and others indicate that a prominent negation strategy is one in which the subject generate the superset of an instance anf falsifies that the superset is the predicate. Auxiliary regression analyses are also reported for other reaction time measures gathered in the experiments. It is argued that large-scale regression experiments are critical to the inferential logic of semantic memory experiment.

In the past five years or so, the topic of semantic memory has become a very active area of experimental research. This paper presents a relatively different approach in trying to understand subjects performance in semantic memory tasks. Several of the major theretical analyses in this field are discussed first. We raise some interpretative problems eith earlier studies, and with these considerrations in mind, offer our oen method for investigating the semantic memory parafigm and analtzing its data.
There have been two developments in response to this multiplicity of theories. The first had been the recognition of the diagnostic value of negative judgments. For instance, how do subjects judge a probe like A St. Bernard is a cat or A thypoon is a wheat? The general result is that subjects are slowet the more similar the two concepts or the closer they are in conceptual hierarchy (meyer, 1970; Collins & Quillian, 1972: Smith et al.,in press). Thus subjects are slower in falsifying the first negative example above. This result is completely counter to what collins an Quillian had originally predicted. The result does correspond nicely to the prediction of the similarity model since similar negatives would tend to bias a positive response.

Interpretative problems
The other recent development in semantic memory has been a recognition of the inter-pretative problems posed by the nature of a semantic memory experiment (for example, Anderson & Bowrr, 1973; Clark, 1973; Landauer & Meyer, 1972). The basic problem is that the experimenter cannot randomly construct the material that he will present to his subject.

Summary and conclusions
The primary goal of this paper was to help understand the processes involved in rejecting false categorical statements. Regression analyses were presented using numerous variables to predict the time to reject a categorical statement. Reaction times, rating variables, and frequency norms were used in the prediction of instance negation time. The vatiables found to be critical are the time to generate the superordinate, the time to reject a categorical statements which has the same predicate but use the superordinate rather than the instance, and the time to encode the instance. The prominence of the particular negation strategy found in our experiments is probably due to the logical nature of the task and the fact that negative propositions tend to be stored with large categories.
Another goal of this paper was to call attention to problem in data analyses in most semantic memory tasks. It was pointed out that variables usef in semantic memory studies tend to be correlated and hence confounded. Because one cannot randomly assign materials to condition to test whether certain "variables" affect reaction times, one cannot be certain that the hypothesized vatiables has caused the effect rather than some other known or unknown vatiable related to it.
The present analyses do not tell us anything very critical about the nature of memory. They just identify one prominent falsification strategy.

SEMANTIC

Semantic is the study about meaning of words, sentencen structure and symbols.
Semantic is all about meaning. One word can has two or more meaning.
For example
Book (n) <> Book (v)
Watch (n) <> Watch (v)
Cool (a) <> Cool (n)
Etc.
The purpose of semantic is to propose exact meanings of the words and phrases and remove confusion, which might lead the readers to believe a word has many possible meanings.
Types of Semantic
There are two types of semantics:
Connotative Semantic and Denotative Semantic
What is the different?
Denotative is a word that has real meaning, while the Connotative is a word that has meaning who implied or often reffered as figurative meaning. For exampel
Word.                     Denotative                         Connotative
Chiken                    Ayam                                 Pengecut
Black sheep.          Domba hitam.                  Pembawa aib
Bullshit.                  Tai Sapi.                            Omong kosong
Sluggish.                Siput.                                 Lamban