Comparison between LSA-LDA-Lexical Chains

Costin Chiru, Traian Rebedea and Silvia Ciotec

University Politehnica of Bucharest, Department of Computer Science and Engineering,

313 Splaiul Independetei, Bucharest, Romania

Keywords: Latent Semantic Analysis - LSA, Latent Dirichlet Allocation - LDA, Lexical Chains, Semantic Relatedness.

Abstract: This paper presents an analysis of three techniques used for similar tasks, especially related to semantics, in

Natural Language Processing (NLP): Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA)

and lexical chains. These techniques were evaluated and compared on two different corpora in order to

highlight the similarities and differences between them from a semantic analysis viewpoint. The first corpus

consisted of four Wikipedia articles on different topics, while the second one consisted of 35 online chat

conversations between 4-12 participants debating four imposed topics (forum, chat, blog and wikis). The

study focuses on finding similarities and differences between the outcomes of the three methods from a

semantic analysis point of view, by computing quantitative factors such as correlations, degree of coverage

of the resulting topics, etc. Using corpora from different types of discourse and quantitative factors that are

task-independent allows us to prove that although LSA and LDA provide similar results, the results of

lexical chaining are not very correlated with neither the ones of LSA or LDA, therefore lexical chains might

be used complementary to LSA or LDA when performing semantic analysis for various NLP applications.

1 INTRODUCTION

Latent Semantic Analysis (LSA) (Landauer and

Dumais, 1997), Latent Dirichlet Allocation (LDA)

(Blei et. al, 2003) and lexical chains (Halliday and

Hasan, 1976; Morris and Hirst, 1991) are widely

used in NLP applications for similar tasks. All these

methods use semantic distances or similarities/

relatedness between terms to form topics or chains

of words. LSA and LDA use the joint frequency of

the co-occurrence of words in different corpora,

while the lexical chains technique uses WordNet

(http://wordnet.princeton.edu/) synsets and links

between them to find groups of highly-connected or

closely-related words.

Although these methods can be similarly used

for various NLP tasks - text summarization (Barzilay

and Elhadad, 1997; Gong and Liu, 2001; Haghighi

and Vanderwende, 2009), question answering

(Novischi and Moldovan, 2006) or topic detection

(Carthy, 2004) - they calculate different measures,

having different meanings. LDA generates topical

threads under a prior Dirichlet distribution, LSA

produces a correlation matrix between words and

documents, while lexical chains use the WordNet

structure to establish a connection between synsets.

Therefore, the comparison and interpretation of

similarities and differences between the

aforementioned methods is important to understand

which model might be the most appropriate for a

given scenario (task and discourse type, for

example). Previous studies were aimed at comparing

different similarity measures built on top of

WordNet in order to decide which one gives better

results (Barzilay and Elhadad, 1997), or to compare

the results provided by the lexical chains built using

different measures with the ones given by LSA in

order to add a further relationship layer to WordNet

for improving its usefulness to NLP tasks (Boyd-

Graber et. al, 2006). However, more recently Cramer

(2008) pointed out that the existing studies are

inconsistent to each other and that human judgments

should not be used as a baseline for the evaluation or

comparison of different semantic measures.

This work aims to study the behaviour of the

three methods: LSA, LDA and lexical chains, based

on a series of tests performed on two corpora: one

consisting on four Wikipedia articles on different

topics and another one built from multi-party online

chat conversations debating four pre-imposed topics:

forum, chat, blog, wikis.

The paper continues with a review of the

255

Chiru C., Rebedea T. and Ciotec S..

Comparison between LSA-LDA-Lexical Chains.

DOI: 10.5220/0004798102550262

In Proceedings of the 10th International Conference on Web Information Systems and Technologies (WEBIST-2014), pages 255-262

ISBN: 978-989-758-024-6

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

evaluated techniques. Afterwards, we present the

procedure for comparing the three methods along

with the texts used for evaluation. Section 4

describes the obtained results and our observations,

while the last section highlights the main

conclusions of the study.

2 EVALUATED METHODS

2.1 LSA – Latent Semantic Analysis

LSA (Landauer and Dumais, 1997) is a statistical

method for extracting the relations between words in

texts. It is a corpus-based method that does not use

dictionaries, semantic networks, grammars, syntactic

or morphological parsers, and its input is represented

only by raw text divided in “chunks”. A chunk may

be a sentence, an utterance in a chat, a paragraph or

even a whole document, depending on the corpus.

The method starts from the term-doc matrix

computed on the corpus segmented into chunks and

then applies a singular value decomposition in order

to compute the most important singular values.

Then, it produces a representation in a new space,

called the latent semantic space, which uses only the

most important (large) k singular values. The value

for k depends on the corpus and task, and is usually

between 100 and 600, a common choice being 300.

This new space is used to compute similarities

between different words and even whole documents,

practically considering that words that are co-

occurring in similar contexts may be considered to

be semantically related.

2.2 LDA – Latent Dirichlet Allocation

LDA (Blei et. al, 2003) is a generative probabilistic

model designed to extract topics from text. The basic

idea behind LDA is that documents are represented

as random mixtures of latent topics, where each

topic is characterized by a set of pairs word-

probability, representing the probability that a word

belongs to a topic.

LDA assumes the following generative process

for each document in a corpus: for each word w

d,i

the corpus, it generates a topic z dependent on the

mixture θ associated to the document d and then it

generates a word from the topic z. To simplify this

basic model, the size of the Dirichlet distribution k

(the number of topics z) is assumed to be known and

fixed. The Dirichlet prior is used because it has

several convenient properties that facilitate inference

and parameter estimation algorithms for LDA.

2.3 Lexical Chains

Lexical chains are groups of words that are

semantically similar (Halliday and Hasan, 1976;

Morris and Hirst, 1991). Each word in the chain is

linked to its predecessors through a certain lexical

cohesion relationship. Lexical chains require a

lexical database or an ontology (most of the time,

this database is WordNet) for establishing a

semantic similarity between words. For this task, we

have used WordNet and the Jiang-Conrath measure

(Jiang and Conrath, 1997). As this measure requires

the frequency of words in the English language and

since we didn’t have access to a relevant corpus, we

have used the number of hits returned by a Google

search for each of the considered words. Once the

distances between words were computed, we have

used a full-clustering algorithm to group the words

in chains. The algorithm worked in an online fashion

(each word was evaluated in the order of their

appearance in the analyzed text), adding a word to

an existing cluster only if it was related to more than

90% of the words that were already part of that

chain. If the considered word could not be fitted in

any of the existing chains, then we created a new

chain containing only that specific word (Chiru,

Janca and Rebedea, 2010).

3 COMPARISON

METHODOLOGY

Experiments were conducted on two different

corpora:

 a corpus composed of four articles from

Wikipedia that were debating completely

different topics: graffiti, tennis, volcano and

astrology, consisting of 294 paragraphs and

having a vocabulary size of 7744 words. In

order not to have our results affected by noise,

we removed from the corpus pronouns,

articles, prepositions and conjunctions.

 a corpus consisting of 35 online chat

conversations debating four pre-imposed

topics: forum, chat, blog, wikis, each of them

involving between 4 to 12 participants. This

corpus consisted of 6000 utterances (41902

words), with a vocabulary size of 2241 words.

3.1 Methods for Obtaining the Results

The SVD is performed using the airhead-research

package (https://code.google.com/p/airhead-

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research/wiki/LatentSemanticAnalysis) and a value

of k = 300. Then, the LSA results are obtained

starting from the matrix of similarities between each

pair of words in the corpus. The degree of similarity

between two words is computed using the cosine of

the corresponding vectors in the latent space.

For LDA, the results are obtained from the

distribution of each topic’s words and the

corresponding probabilities. In the first corpus,

containing encyclopaedic articles from four different

domains, we decided to use a number of topics k = 4

for this analysis. For the second corpus, consisting

on debates on four imposed topics, we decided to

use k = 5 topics for the analysis, as besides the

imposed topics, the participants also inputted some

off-topic content that could have been considered as

the fifth topic. In order to better understand the

behaviour of LDA, we extracted the top 35, 50, 100,

150 and 200 words that were considered

representative for each topic, given that each article

contained over 1000 words. The topic models were

extracted using MALLET - MAchine Learning for

LanguagE Toolkit (http://mallet.cs.umass.edu/).

In the case of lexical chains, we analyzed the

words from each chain and also considered the

maximum length and the total number of the lexical

chains from a document (chat or Wikipedia article).

3.1.1 LDA - LSA Comparison

In order to compare the two methods, we started

from the LDA topics and computed an LSA score

for each concept from each topic generated by LDA.

This score represented the average similarity

between the target concept and each of the

remaining words from the topic. The assessment of

the relationship between LSA and LDA scores

distributions was performed using Pearson’s

correlation coefficient and Spearman’s rank

correlation coefficient. LSA and LDA have also

been compared on several NLP tasks, such as

predicting word associations (Griffiths et al., 2007)

and automatic essay grading (Kakkonen et al.,

2008).

3.1.2 LSA - Lexical Chains Comparison

For comparing these two methods, we determined a

similarity value for each lexical chain based on the

LSA similarity as follows: we computed the LSA

similarity between any pair of two words from the

chain and averaged over all the words in that chain.

LSA has been previously compared with semantic

distances in WordNet (Tsatsaronis et al., 2010), but

not with lexical chains.

3.1.3 LDA - Lexical Chains Comparison

This comparison is based on the number of common

words between the lexical chains and the LDA

topics. For each LDA topic we extracted a number

of 35, 50, 100, 150 and 200 words, and computed

different statistics for each case. To our knowledge,

LDA and lexical chains have only been compared as

an alternative for text segmentation (Misra et al.,

2009).

4 EXPERIMENTAL RESULTS

4.1 Wikipedia Corpus

4.1.1 LDA - LSA Comparison

Table 1 presents the top 10 words from the 4 LDA

topics of the first corpus. In Table 2 we present the

most similar 30 word-pairs generated by LSA. We

need to mention that LSA was trained on the

concatenation of all 4 articles from Wikipedia.

Table 1: Top 10 words from the LDA topics for the

Wikipedia corpus.

Topic 0 Topic 1 Topic 2 Topic 3

graffiti tennis volcanoes astrology

new game volcano been

culture player lava Chinese

form first volcanic personality

york players surface scientific

design two example based

popular court formed considered

hip three examples birth

style point extinct bce

spray French flows belief

Table 2: Top 30 most similar word-pairs generated by

LSA for the Wikipedia corpus.

LSA Word Pairs

men-cup mid-thinning plates-tectonic

mid-crust tie-addition center-baseline

hop-music thinning-ridge choice-receiver

mid-ridge pace-receiver depicted-dealer

lake-park shift-equinox degrees-equinox

mm-bounce basque-perera gladiatorial-cil

lady-week degrees-shift difficult-extinct

são-brazil rhode-newport tectonic-ridge

force-hero federation-itf era-compete

test-results mud-formation lifespans-

volcanologist

For each topic we plotted the distributions of LDA

and LSA scores for each word from that topic,

ComparisonbetweenLSA-LDA-LexicalChains

257

computed as described in the previous section. Each

LDA topic has 35 words that are sorted decreasing

according to the LSA scores. The best result we

have obtained was for the Topic 1 (tennis), where

with very few exceptions, the LSA and LDA scores

were very well correlated (0.855). This case is

presented in Figure 1, where the x-axis represents

the word number from the LDA topic and on the y

axis we plotted the LDA and LSA scores

corresponding to that word. The words’ probabilities

for the considered topic computed with LDA are

represented by the blue colour while in red we

present the LSA scores. The scattering diagram for

the same topic is presented in Figure 2.

Figure 1: LDA – LSA distributions for Topic 1 (tennis)

from the Wikipedia corpus.

Figure 2: Scattering plot for the rank distributions for the

LDA – LSA comparison for Topic 1 (tennis).

For a better visualization of the relationship

between the two distributions, we present in Table 3

the Pearson’s correlation and the Spearman’s rank

correlation coefficients between the LDA and LSA

scores for each of the four LDA topics. With one

exception, these values are close to 1, indicating a

very good correlation (the strongest is highlighted in

bold).

Table 3: LDA-LSA Pearson’s Coefficient for the

Wikipedia corpus.

Topic

Pearson’s

Coefficient

Spearman’s

Coefficient

0 (graffiti) 0.560 0.778

1 (tennis)

0.855 0.873

2 (volcanoes) 0.782 0.840

3 (astrology) 0.745 0.745

These results prove that there is clearly a correlation

between the two distributions because both tend to

decrease towards the last words of the topic.

However, there are some words for which the two

scores are discordant. We have extracted them and

obtained the following results:

 for Topic 0 (graffiti): hip, produced, styles,

non, offered, property;

 for Topic 1 (tennis): point, receiving;

 for Topic 2 (volcanoes): extinct, gases,

features, falls;

 for Topic 3 (astrology): considered, challenge,

avoid.

It is interesting to observe that the better

correlated the LSA and LDA scores are for a given

topic, the more the words underestimated by LSA

correspond to that topic.

4.1.2 LSA - Lexical Chains Comparison

Using the LSA similarity between words, we

computed a score ranging from 0 to 1 for every

lexical chain.

Figure 3: LSA scores for the lexical chains of the tennis

article from the Wikipedia corpus.

For an example of the obtained results, see Figure 3

(for Topic 1 - tennis) where on the x-axis are the

lexical chains (excluding those formed only by one

word) and on the y-axis are their LSA scores.

We have noticed that the best lexical chains are

obtained for the texts that had also a good

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correlation between the scores obtained by LDA and

LSA. Also, one can see that there are only few

lexical chains which are closely related in terms of

LSA, which leads us to believe that LSA and lexical

chains are not very well correlated.

Approximately 70% of the generated lexical

chains were composed of a single word. In the rest

of the lexical chains, the most frequent ones are

those having small LSA scores - in the range (0,

0.25]. The other intervals represent only a small

percent from the number of chains remaining when

the single word chains are ignored.

The LSA scores are dependent on the lexical

chain length, so we considered that it would be

interesting to draw a parallel between these two

elements. In Figure 4 are plotted the lexical chains

lengths with their corresponding LSA scores for the

tennis article. The x-axis contains the lexical chains

indexes and the z-axis contains the LSA score and

the length of that chain.

Figure 4: The LSA scores (green) and the lexical chains

length (blue) from the tennis article.

4.1.3 LDA - Lexical Chains Comparison

For this comparison, we generated the most

representative words for each of the four topics

keeping the top 35, 50, 100 and 200 words and

gradually comparing the number of common words

between the topics and the lexical chains. It should

be mentioned that a word can be representative for

multiple topics (having different probabilities for

each topic). The maximum lengths of the lexical

chains from each article were 31, 28, 24 and 12

words for the articles about volcanoes, graffiti,

astrology and tennis respectively. In the case of

LDA topics having 35 words, the common words

between LDA and lexical chains were:

 Volcano article: volcano, lava, surface,

example, extinct, flow, explosive, water,

generally, volcanism, fire, form, fluid, field,

few, weight, first;

 Tennis article: tennis, game, player, first,

court, point, french, receiver, real, playing,

wide, cup, usually, full, current, covered,

recent;

 Graffiti article: graffiti, new, culture, form,

york, design, popular, hip, style, spray, paint,

early, different, day, rock, history, elements,

stencil, due, chicago, dragon, disagreement,

newspaper, egypt, popularity, production;

 Astrology article: astrology, chinese,

personality, scientific, birth, belief, challenge,

astronomical, astronomy, avoid, philosophy,

babylonian, basis, basic, average, birthday,

beginning, century, believe.

In order to compare the results between LDA

and lexical chains, we determined how many chains

contained words that were also considered

representative for the four LDA topics along with

the number of such common words.

First of all, we computed for each topic the first

35 words and represented the frequency of common

words between the lexical chains and the topics of

this size. In this case, most chains had no common

words with any of the topics (more than 700 such

chains). The Topic 0 (graffiti) had one common

word with the largest number of lexical chains (over

25 chains), the Topic 1 (tennis) had a common word

with 17 such chains, while the last topic (volcano)

had words in 15 lexical chains. Topic 2 (astrology)

had two common words with 3 lexical chains (most

chains comparing with the other topics), but had a

smaller number of lexical chains (13) with which it

had a single word in common. As an overall statistic,

the words from Topic 0 (graffiti) could be found in

the most lexical chains. After we increased the

number of words to 50 per topic, around 430 chains

had no word in common with the topics, and the

number of most common words between topics and

lexical chains increased to 3, although there were

only two such chains – one for Topic 1 (tennis) and

one for Topic 3 (volcano). Further increasing the

number of words in a topic to 100, we saw that

Topic 3 (volcano) had 4 common words with one

lexical chain and, compared to the previous case,

this time all the topics have found 3 common words

with at least one lexical chains. At this point, Topic

1 (tennis) had a single word in common with over 40

lexical chain, this becoming the best score,

comparing to the previous cases when the Topic 0

(graffiti) was the most popular in this category.

Overall, the Topic 3’s words are the most often

found in the lexical chains (over 40 chains having o

ComparisonbetweenLSA-LDA-LexicalChains

259

word in common, 2 having 2 words in common and

1 with 3 and 1 with 4 words in common).

Finally we increased the number of words per

topic to 200 (Figure 5). Also in this case, there still

remained around 350 chains that had no words in

common with any of the topics. It can be seen that

the Topic 3 (volcano) has 7 words in common with

one of the lexical chains (the best score so far),

while Topic 2 (astrology) had 5 common words with

one of the chains. The details of this discussion are

summarized in Table 4.

Figure 5: The distribution of the common words between

topics (of 200 words) and the lexical chains.

Table 4: Number of chains having a single word in

common with different topics (highest values are in bold),

and the maximum number of words in common with a

topic in a single chain.

Topic

words/

topic

T0 T1 T2 T3

topic

Max.

common

words

>25

16 12 15 >300

2 (3 chains

for T2, 1 for

the rest)

17 15 20 ~300 3 (T1 & T3)

100 24

>40

33 >40 ~270 4 (T3)

150 34 51 41

>50

~260 6 (T3)

200 >40

>70

>50 >60 ~250 7 (T3)

In conclusion, the most frequent situation (besides

the lexical chains having no word in common with

the topics) is the one when the lexical chains and the

topics have exactly one common word, and the

maximum number of common words that was found

was 7 for topics consisting of 200 words.

4.2 Chat Conversations Corpus

A similar methodology was used to compare the

results on the chat corpus in order to see if there are

any noticeable differences due to the change of the

type of discourse. The results are reported more

briefly in this section.

4.2.1 LDA - LSA Comparison

Table 5 presents the top 10 words from the 5 LDA

topics. In Table 6 we present the most similar 30

word-pairs generated by LSA.

Similarly to the Wikipedia corpus, we plotted the

distributions of LDA and LSA scores for each word

from that topic and obtained the best result for Topic

1 (0.73). This case is presented in Figure 6, while in

Figure 7 we present the scattering diagram for this

topic. The Pearson’s and the Spearman’s Rank

correlation coefficient between the LDA and LSA

scores for each LDA topics are presented in Table 7.

Table 5: Top 10 words from the LDA topics in the chat

corpus.

Topic 0 Topic 1 Topic 2 Topic 3 Topic 4

Forums wiki blogs chat blog

Internet solutions brain

storming

information person

Good solve company friends forum

Ideas opinion clients find board

Right web changes folksonomy certain

Users wave compare follow fun

write number cases great new

idea need different hard part

people like easy integrate change

help use more maybe friend

Table 6: Top 30 most similar word-pairs generated by

LSA in the chat corpus.

LSA Word Pairs

traveller-

messaging

patterns-

vmtstudents

mathematicians-

patterns

sets-colinear flame-wars dictate-behaviour

decides-improper physically-online

satisfaction-

conducted

easely-

switchboard

inconvenient-

counterargument counts-popularity

ads-revenue induction-patterns editors-objectivity

supplying-

focuses

inconvenient-

counter duties-minimum

patient-recall sets-colinear decides-improper

hm-conversions

equations-

quicksilver lie-proud

secure-hacked simplifies-equals chatroom-leaves

careful-posible fellow-worker hexagonal-array

As it was expected, the results for the chat corpus

are less correlated than the ones obtained for the

Wikipedia corpus. This drop in performance can be

partly explained by the increased number of topics

(one additional topic), but mostly by the different

nature of discourse: the Wikipedia articles are much

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Figure 6: LDA – LSA distributions for Topic 1 from the

chat corpus.

Figure 7: Scattering plot for the ranks distributions for the

LDA–LSA comparison for Topic 1 from the chat corpus.

Table 7: LDA-LSA Pearson’s Coefficient for the chat

corpus.

Topic

Pearson’s

Coefficient

Spearman’s

Coefficient

0 0.63 0.46

0.73 0.55

2 0.55 0.41

3 0.46 0.35

4 0.71 0.32

more focused/cohesive and coherent than chat

conversation between multiple participants. It also

provides an insight related to the content of the chat

conversations: it seemed that the topic one (related

to wikis/Wikipedia) discovered by LDA was more

coherent than the other topics, at least by looking at

the LSA correlation scores. The second highest

score in this hierarchy was for the forum-blog topic

showing that the participants do not perceive

significant differences between these concepts.

However, the most intriguing result was the

placement of the third topic (related to chat) on the

last place, showing the least coherence. We expected

that this topic to have in fact the highest coherence,

being the tool most frequently used by the

participants and therefore the tool that they knew

best. These results may also be influenced by the

way we are measuring the coherence of a LDA topic

through its correlation with the average LSA

similarity scores.

4.2.2 LSA - Lexical Chains Comparison

For the chat corpus, the values of the LSA similarity

between words for every lexical chain ranged from -

1 to 1, as it can be seen in Figure 8. We can observe

that the correlation between the LSA and lexical

chains for the chat corpus is lower than the one for

the Wikipedia corpus, this fact being generated by

the lower cohesion of the text in this case.

Figure 8: LSA scores for the lexical chains from the chat

corpus.

4.2.3 LDA - Lexical Chains Comparison

Similarly to the Wikipedia corpus, each of the five

topics was generated keeping the top 35, 50, 100 and

200 words and gradually comparing the number of

common words between the topics and the lexical

chains. The maximum length of the lexical chains

from this corpus was 84, much larger than the one

obtained in the case of the Wikipedia corpus. This is

due to the fact that the four topics imposed for

debating in the chat conversations (forum, chat,

blog, and wikipedia) were strongly related compared

to the Wikipedia articles that debated topics from

different domains.

The number of common words is predominantly

1, reaching a maximum of 8 common words for the

third topic (related to chat) for a length of the lexical

chain of 150 words. The results are similar to those

obtained for the Wikipedia corpus.

ComparisonbetweenLSA-LDA-LexicalChains

261

5 CONCLUSIONS

In this paper we discussed the characteristics and

behaviour of three methods frequently used to assess

semantics in various NLP applications: LSA, LDA

and lexical chaining. These methods have been

tested on two different corpora containg different

types of written discouse: a corpus consisting of 4

articles from Wikipedia and another one consisting

of 35 chat conversations with multiple participants

debating four pre-imposed topics: forum, chat, blog

and wikis.

In contrast with the previous studies, we have

compared the outcomes of the three methods using

quantitative scores computed based on the outputs of

each method. These scores included correlations

between similarity scores and the number of

common words from topics and chains. Thus, the

obtained results are task and discourse-independent.

The most important result is that LSA and LDA

have shown the strongest correlation on both

corpora. This is consistent with the theoretical

underpinnings, as LDA is similar to Probabilistic

Latent Semantic Analysis (pLSA), except that the

LDA distribution of topics is assumed to have a

prior Dirichlet distribution. Moreover, LSA scores

might be used to compute the coherence of a LDA

topic as shown in the paper.

Another important contribution is that WordNet-

based lexical chains are not very correlated with

neither LSA nor LDA, therefore they might be seen

as complementary to the LSA or LDA results.

ACKNOWLEDGEMENTS

This research was supported by project No.264207,

ERRIC-Empowering Romanian Research on

Intelligent Information Technologies/FP7-REGPOT-

2010-1.

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