HCDA/cikm-paper Changeset - 60d8e26cf4e5 · Centrum Wiskunde & Informatica (CWI)

@@ -847,243 +847,285 @@ Finally,  we select  the 5 most frequent n-grams for each context.

\end{table*}

Table \ref{tab:class-vital} shows the recall performance for vitally judged documents.  On Wikipedia entities, except in the canonical profile, the cleansed version achieves  better results than the raw version.  However, on Twitter entities, the raw corpus achieves  better  in all entity profiles (except  in name-variant partial).  At an aggregate (both Wikipedia and Twitter) level, we see that in three profiles, cleansed achieves better.  Only in canonical partial, does raw perform better. Overall cleansed achieves better results than raw.  This result is interesting because we saw in previous sections that the raw corpus achieves  higher recall than cleansed. In the case name-variant partial, for example, 10\% more relevant documents are retrieved in the raw corpus. The gain in recall in raw corpus does not translate into a gain in F\_measure. In fact, in most cases F\_measure decreased. % One explanation for this is that it brings in many false positives from, among related links, adverts, etc.

For Wikipedia entities,  canonical partial  achieves the highest performance. For Twitter, name-variant partial achieves  better results.

In vital-relevant category (Table \ref{tab:class-vital-relevant}), the performances are different.  Except in canonical partial,  raw achieves better results in all cases. For Twitter entities, the raw corpus achieves better results in all cases.  In terms of  entity profiles, Wikipedia's canonical partial  achieves  the best F-score. For Twitter, as before, canonical partial. The raw corpus has more effect on relevant documents and Twitter entities.

%The fact that canonical partial names achieve better results is interesting.  We know that partial names were used as a baseline in TREC KBA 2012, but no one of the KBA participants actually used partial names for filtering.

%    \begin{table*}

% \caption{Breakdown of missing documents by sources for cleansed, raw and cleansed-and-raw}

% \begin{center}\begin{tabular}{l*{9}r}

%   &others&news&social \\

% \hline

% 			&missing from raw only &   0 &0   &217 \\

% 			&missing from cleansed only   &430   &1321     &1341 \\

%                          &missing from both    &19 &317     &2196 \\

% \hline

% \end{tabular}

% \end{center}

% \label{tab:miss-category}

% \end{table*}

%    To gain more insight, I sampled for each 35 entities, one document-entity pair and looked into the contents. The results are in \ref{tab:miss from both}

%    \begin{table*}

% \caption{Missing documents and their mentions }

% \begin{center}

%  \begin{tabular}{l*{4}{l}l}

%  &entity&mentioned by &remark \\

% \hline

%  Jeremy McKinnon  & Jeremy McKinnon& social, mentioned in read more link\\

% Blair Thoreson   & & social, There is no mention by name, the article talks about a subject that is political (credit rating), not apparent to me\\

%   Lewis and Clark Landing&&Normally, maha music festival does not mention ,but it was held there \\

% Cementos Lima &&It appears a mistake to label it vital. the article talks about insurance and centos lima is a cement company.entity-deleted from wiki\\

% Corn Belt Power Cooperative & &No content at all\\

% Marion Technical Institute&&the text could be of any place. talks about a place whose name is not mentioned.

%  roryscovel & &Talks about a video hinting that he might have seen in the venue\\

% Jim Poolman && talks of party convention, of which he is member  politician\\

% Atacocha && No mention by name The article talks about waste from mining and Anacocha is a mining company.\\

% Joey Mantia & & a mention of a another speeedskater\\

% Derrick Alston&&Text swedish, no mention.\\

% Paul Johnsgard&& not immediately clear why \\

% GandBcoffee&& not immediately visible why\\

% Bob Bert && talks about a related media and entertainment\\

% FrankandOak&& an article that talks about a the realease of the most innovative companies of which FrankandOak is one. \\

% KentGuinn4Mayor && a theft in a constituency where KentGuinn4Mayor is vying.\\

% Hjemkomst Center && event announcement without mentioning where. it takes a a knowledge of \\

% BlossomCoffee && No content\\

% Scotiabank Per\%25C3\%25BA && no content\\

% Drew Wrigley && politics and talk of oilof his state\\

% Joshua Zetumer && mentioned by his film\\

% Théo Mercier && No content\\

% Fargo Air Museum && No idea why\\

% Stevens Cooperative School && no content\\

% Joshua Boschee && No content\\

% Paul Marquart &&  No idea why\\

% Haven Denney && article on skating competition\\

% Red River Zoo && animal show in the zoo, not indicated by name\\

% RonFunches && talsk about commedy, but not clear whyit is central\\

% DeAnne Smith && No mention, talks related and there are links\\

% Richard Edlund && talks an ward ceemony in his field \\

% Jennifer Baumgardner && no idea why\\

% Jeff Tamarkin && not clear why\\

% Jasper Schneider &&no mention, talks about rural development of which he is a director \\

% urbren00 && No content\\

% \hline

% \end{tabular}

% \end{center}

% \label{tab:miss from both}

% \end{table*}

\section{Analysis and Discussion}\label{sec:analysis}

We conducted experiments to study  the impacts on recall of

We conducted experiments to study the impacts on recall of

different components of the filtering stage of entity-based filtering and ranking pipeline. Specifically

we conducted experiments to study the impacts of cleansing,

entity profiles, relevance ratings, categories of documents, entity profiles. We also measured  impact of the different factors and choices  on later stages of the pipeline.

Experimental results show that cleansing can remove entire or parts of the content of documents making them difficult to retrieve. These documents can, otherwise, be retrieved from the raw version. The use of the raw corpus brings in documents that can not be retrieved from the cleansed corpus. This is true for all entity profiles and for all entity types. The  recall difference between the cleansed and raw ranges from  6.8\% t 26.2\%. These increases, in actual document-entity pairs,  is in thousands. We believe this is a substantial increase. However, the recall increases do not always translate to improved F-score in overall performance.  In the vital relevance ranking for both Wikipedia and aggregate entities, the cleansed version performs better than the raw version.  In Twitter entities, the raw corpus achieves better except in the case of all name-variant, though the difference is negligible.  However, for vital-relevant, the raw corpus performs  better across all entity profiles and entity types

except in partial canonical names of Wikipedia entities.

The use of different profiles also shows a big difference in recall. Except in the case of Wikipedia where the use of canonical partial achieves better than name-variant, there is a steady increase in recall from canonical to  canonical partial, to name-variant, and to name-variant partial. This pattern is also observed across the document categories.  However, here too, the relationship between   the gain in recall as we move from less richer profile to a more richer profile and overall performance as measured by F-score  is not linear.

%%%%% MOVED FROM LATER ON - CHECK FLOW

There is a trade-off between using a richer entity-profile and retrieval of irrelevant documents. The richer the profile, the more relevant documents it retrieves, but also the more irrelevant documents. To put it into perspective, lets compare the number of documents that are retrieved with  canonical partial and with name-variant partial. Using the raw corpus, the former retrieves a total of 2547487 documents and achieves a recall of 72.2\%. By contrast, the later retrieves a total of 4735318 documents and achieves a recall of 90.2\%. The total number of documents extracted increases by 85.9\% for a recall gain of 18\%. The rest of the documents, that is 67.9\%, are newly introduced irrelevant documents.

entity profiles, relevance ratings, categories of documents, entity

profiles. We also measured  impact of the different factors and

choices  on later stages of the pipeline of our own system.

Experimental results show that cleansing can remove entire or parts of

the content of documents making them difficult to retrieve. These

documents can, otherwise, be retrieved from the raw version. The use

of the raw corpus brings in documents that can not be retrieved from

the cleansed corpus. This is true for all entity profiles and for all

entity types. The  recall difference between the cleansed and raw

ranges from  6.8\% t 26.2\%. These increases, in actual

document-entity pairs,  is in thousands. We believe this is a

substantial increase. However, the recall increases do not always

translate to improved F-score in overall performance.  In the vital

relevance ranking for both Wikipedia and aggregate entities, the

cleansed version performs better than the raw version.  In Twitter

entities, the raw corpus achieves better except in the case of all

name-variant, though the difference is negligible.  However, for

vital-relevant, the raw corpus performs  better across all entity

profiles and entity types except in partial canonical names of

Wikipedia entities.

The use of different profiles also shows a big difference in

recall. While in Wikipedia the use of canonical

partial achieves better than name-variant, there is a steady increase

in recall from canonical to canonical partial, to name-variant, and

to name-variant partial. This pattern is also observed across the

document categories.  However, here too, the relationship between

the gain in recall as we move from less richer profile to a more

richer profile and overall performance as measured by F-score  is not

linear.

%%%%%%%%%%%%

In vital ranking, across all entity profiles and types of corpus, Wikipedia's canonical partial  achieves better performance than any other Wikipedia entity profiles. In vital-relevant documents too, Wikipedia's canonical partial achieves the best result. In the raw corpus, it achieves a little less than name-variant partial. For Twitter entities, the name-variant partial profile achieves the highest F-score across all entity profiles and types of corpus.

In vital ranking, across all entity profiles and types of corpus,

Wikipedia's canonical partial  achieves better performance than any

other Wikipedia entity profiles. In vital-relevant documents too,

Wikipedia's canonical partial achieves the best result. In the raw

corpus, it achieves a little less than name-variant partial. For

Twitter entities, the name-variant partial profile achieves the

highest F-score across all entity profiles and types of corpus.

Cleansing impacts Twitter

entities and relevant documents.  This  is validated by the

observation that recall  gains in Twitter entities and the relevant

categories in the raw corpus also translate into overall performance

gains. This observation implies that cleansing removes relevant and

social documents than it does vital and news. That it removes relevant

documents more than vital can be explained by the fact that cleansing

removes the related links and adverts which may contain a mention of

the entities. One example we saw was the the cleansing removed an

image with a text of an entity name which was actually relevant. And

that it removes social documents can be explained by the fact that

most of the missing of the missing  docuemnts from cleansed are

social. And all the docuemnts that are missing from raw corpus

social. So in both cases socuial seem to suffer from text

social. So in both cases social seem to suffer from text

transformation and cleasing processes.

%%%% NEEDS WORK:

Taking both performance (recall at filtering and overall F-score

during evaluation) into account, there is a clear trade-off between using a richer entity-profile and retrieval of irrelevant documents. The richer the profile, the more relevant documents it retrieves, but also the more irrelevant documents. To put it into perspective, lets compare the number of documents that are retrieved with  canonical partial and with name-variant partial. Using the raw corpus, the former retrieves a total of 2547487 documents and achieves a recall of 72.2\%. By contrast, the later retrieves a total of 4735318 documents and achieves a recall of 90.2\%. The total number of documents extracted increases by 85.9\% for a recall gain of 18\%. The rest of the documents, that is 67.9\%, are newly introduced irrelevant documents.

Wikipedia's canonical partial is the best entity profile for Wikipedia

entities. This is interesting  to see that the retrieval of of

during evaluation) into account, there is a clear trade-off between

using a richer entity-profile and retrieval of irrelevant

documents. The richer the profile, the more relevant documents it

retrieves, but also the more irrelevant documents. To put it into

perspective, lets compare the number of documents that are retrieved

with  canonical partial and with name-variant partial. Using the raw

corpus, the former retrieves a total of 2547487 documents and achieves

a recall of 72.2\%. By contrast, the later retrieves a total of

4735318 documents and achieves a recall of 90.2\%. The total number of

documents extracted increases by 85.9\% for a recall gain of 18\%. The

rest of the documents, that is 67.9\%, are newly introduced irrelevant

documents.

Perhaps surprising, Wikipedia's canonical partial is the best entity profile for Wikipedia

entities. Here, the retrieval of

thousands vital-relevant document-entity pairs by name-variant partial

does not translate to an increase in over all performance. It is even

more interesting since canonical partial was not considered as

contending profile for stream filtering by any of participant to the

best of our knowledge. With this understanding, there  is actually no

need to go and fetch different names variants from DBpedia, a saving

of time and computational resources.

does not materialize into an increase in over all performance. Notice

that none of the participants in TREC KBA considered canonical partial

as a viable strategy though. We conclude that, at least for our

system, the remainder of the pipeline needs a different approach to

handle the correct scoring of the additional documents -- that are

necessary if we do not want to accept a low recall of the filtering

step.

%With this understanding, there  is actually no

%need to go and fetch different names variants from DBpedia, a saving

%of time and computational resources.

%%%%%%%%%%%%

The deltas between entity profiles, relevance ratings, and document

categories reveal four differences between Wikipedia and Twitter

entities. 1) For Wikipedia entities, the difference between canonical

partial and canonical is higher(16.1\%) than between name-variant

partial and  name-variant(8.3\%).  This can be explained by

saturation. This is to mean that documents have already been extracted

by  name-variants and thus using their partials does not bring in many

new relevant documents.  2) Twitter entities are mentioned by

name-variant or name-variant partial and that is seen in the high

recall achieved  compared to the low recall achieved by canonical(or

their partial). This indicates that documents (specially news and

others) almost never use user names to refer to Twitter

entities. Name-variant partials are the best entity profiles for

Twitter entities. 3) However, comparatively speaking, social documents

refer to Twitter entities by their user names than news and others

suggesting a difference in adherence to standard in names and naming.

4) Wikipedia entities achieve higher recall and higher overall performance.

The high recall and subsequent higher overall performance of Wikipedia

entities can  be due to two reasons. 1) Wikipedia entities are

relatively well described than Twitter entities. The fact that we can

retrieve different name variants from DBpedia is a measure of

relatively rich description. Rich description plays a role in both

filtering and computation of features such as similarity measures in

later stages of the pipeline.   By contrast, we have only two names

for Twitter entities: their user names and their display names which

we collect from their Twitter pages. 2) There is not DBpedia-like

resource for Twitter entities from which alternative names cane be

collected.

In the experimental results, we also observed that recall scores in

the vital category are higher than in the relevant category. This

observation  confirms one commonly held assumption:(frequency) mention

is related to relevance.  this is the assumption why term frequency is

used an indicator of document relevance in many information retrieval

systems. The more  a document mentions an entity explicitly by name,

the more likely the document is vital to the entity.

Across document categories, we observe a pattern in recall of

documents from the ``others'' category, followed by ``news'', and then

by ``social''. The social documents relevant to an entity are the

hardest to retrieve. This can be explained by the fact that social

documents (tweets and  blogs) are more likely to point to a resource

where the entity is mentioned, mention the entities with some short

abbreviation, or talk without mentioning the entities, but with some

context in mind. By contrast news documents mention the entities they

talk about using the common name variants more than social documents

do. However, the greater difference in percentage recall between the

different entity profiles in the news category indicates news refer to

a given entity with different names, rather than by one standard

name. By contrast others show least variation in referring to

news. Social documents falls in between the two.  The deltas, for

Wikipedia entities, between canonical partials and canonicals,  and

name-variants and canonicals are high, an indication that canonical

partials

and name-variants bring in new relevant documents that can not be

retrieved by canonicals. The rest of the two deltas are very small,

suggesting that partial names of name variants do not bring in new

relevant documents.

% Was: \section{Unfilterable documents}

\section{Missing vital-relevant documents \label{miss}}

 The use of name-variant partial for filtering is an aggressive

 attempt to retrieve as many relevant documents as possible at the

 cost of retrieving irrelevant documents. However, we still miss about

 2363(10\%) of the vital-relevant documents.  Why are these documents

 never retrieved? If they are not mentioned by partial names of name

 variants, what are they mentioned by? Table \ref{tab:miss} summarizes

 the number of documents that we miss with respect to cleansed and raw

 corpus.  The upper part shows the number of documents missing from

 cleansed and raw versions of the corpus. The lower part of the table

 shows the intersections and exclusions in each corpus.

\begin{table}

\caption{The number of documents missing  from raw and cleansed extractions. }

\begin{center}

\begin{tabular}{l@{\quad}llllll}

\hline

\multicolumn{1}{l}{\rule{0pt}{12pt}category}&\multicolumn{1}{l}{\rule{0pt}{12pt}Vital }&\multicolumn{1}{l}{\rule{0pt}{12pt}Relevant }&\multicolumn{1}{l}{\rule{0pt}{12pt}Total }\\[5pt]

\hline

Cleansed &1284 & 1079 & 2363 \\

Raw & 276 & 4951 & 5227 \\

\hline

 missing only from cleansed &1065&2016&3081\\

  missing only from raw  &57 &160 &217 \\

  Missing from both &219 &1927&2146\\

\hline